Showing papers in "Swarm and evolutionary computation in 2014"
TL;DR: An up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering and key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.
Abstract: The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.
457 citations
TL;DR: A new approach to find the optimal location and size of DG with an objective of minimizing network power losses, operational costs and improving voltage stability is presented.
Abstract: Optimal location and size of distributed generation (DG) in the distribution system play a significant role in minimizing power losses, operational cost and improving voltage stability. This paper presents a new approach to find the optimal location and size of DG with an objective of minimizing network power losses, operational costs and improving voltage stability. Loss sensitivity factor is used to identify the optimal locations for installation of DG units. Bacterial Foraging Optimization Algorithm (BFOA) is used to find the optimal size of DG. BFOA is a swarm intelligence technique which models the individual and group foraging policies of the Escherichia coli bacteria as a distributed optimization process. The technical constraints of voltage and branch current carrying capacity are included in the assessment of the objective function. The proposed method has been tested on IEEE 33-bus and 69-bus radial distribution systems with various load models at different load levels to demonstrate the performance and effectiveness of the technique.
240 citations
TL;DR: This paper investigates the growth of literature in SI and its algorithms, particularly Particle Swarm Optimization (PSO), and provides a thorough literature overview focusing on some of the most cited techniques that have been used for PSO-based data clustering.
Abstract: Optimization based pattern discovery has emerged as an important field in knowledge discovery and data mining (KDD), and has been used to enhance the efficiency and accuracy of clustering, classification, association rules and outlier detection. Cluster analysis, which identifies groups of similar data items in large datasets, is one of its recent beneficiaries. The increasing complexity and large amounts of data in the datasets have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. Swarm intelligence (SI) is one such optimization technique whose algorithms have successfully been demonstrated as solutions for different data clustering domains. In this paper we investigate the growth of literature in SI and its algorithms, particularly Particle Swarm Optimization (PSO). This paper makes two major contributions. Firstly, it provides a thorough literature overview focusing on some of the most cited techniques that have been used for PSO-based data clustering. Secondly, we analyze the reported results and highlight the performance of different techniques against contemporary clustering techniques. We also provide an brief overview of our PSO-based hierarchical clustering approach (HPSO-clustering) and compare the results with traditional hierarchical agglomerative clustering (HAC), K-means, and PSO clustering.
191 citations
TL;DR: Results of applying the proposed algorithm in three benchmark pipe networks show that SLC converges to the global optimum more reliably and rapidly in comparison with other meta-heuristic methods.
Abstract: Water distribution networks are one of the most important elements in the urban infrastructure system and require huge investment for construction. Optimal design of water systems is classified as a large combinatorial discrete non-linear optimization problem. The main concern associated with optimization of water distribution networks is related to the nonlinearity of discharge-head loss equation, availability of the discrete nature of pipe sizes, and constraints, such as conservation of mass and energy equations. This paper introduces an efficient technique, entitled Soccer League Competition (SLC) algorithm, which yields optimal solutions for design of water distribution networks. Fundamental theories of the method are inspired from soccer leagues and based on the competitions among teams and players. Like other meta-heuristic methods, the proposed technique starts with an initial population. Population individuals (players) are in two types: fixed players and substitutes that all together form some teams. The competition among teams to take the possession of the top ranked positions in the league table and the internal competitions between players in each team for personal improvements are used for simulation purpose and convergence of the population individuals to the global optimum. Results of applying the proposed algorithm in three benchmark pipe networks show that SLC converges to the global optimum more reliably and rapidly in comparison with other meta-heuristic methods.
150 citations
TL;DR: The aim of this article is to show that GSA is able to find multiple solutions in multimodal problems and to evaluate the performance of the proposed algorithm, namely Niche GSA (NGSA), compared with those of state-of-the-art niching algorithms.
Abstract: Gravitational search algorithm (GSA) has been recently presented as a new heuristic search algorithm with good results in real-valued and binary encoded optimization problems which is categorized in swarm intelligence optimization techniques. The aim of this article is to show that GSA is able to find multiple solutions in multimodal problems. Therefore, in this study, a new technique, namely Niche GSA (NGSA) is introduced for multimodal optimization. NGSA extends the idea of partitioning the main population (swarm) of masses into smaller sub-swarms and also preserving them by introducing three strategies: a K-nearest neighbors (K-NN) strategy, an elitism strategy and modification of active gravitational mass formulation. To evaluate the performance of the proposed algorithm several experiments are performed. The results are compared with those of state-of-the-art niching algorithms. The experimental results confirm the efficiency and effectiveness of the NGSA in finding multiple optima
142 citations
TL;DR: Results of the work reveal that m TLBO performs better than many other algorithms investigated in this work, including PSO, DE and ABC.
Abstract: Teaching–Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique for global optimization over continuous spaces. Few variants of TLBO have been proposed by researchers to improve the performance of the basic TLBO algorithm. In this paper the authors investigate the performance of a new variant of TLBO called modified TLBO ( m TLBO) for global function optimization problems. The performance of m TLBO is compared with the state-of-the art forms of Particle Swarm Optimization (PSO), Differential Evolution (DE) and Artificial Bee Colony (ABC) algorithms. Several advanced variants of PSO, DE and ABC are considered for the comparison purpose. The suite of benchmark functions are chosen from the competition and special session on real parameter optimization under IEEE Congress on Evolutionary Computation (CEC) 2005. Statistical hypothesis testing is undertaken to demonstrate the significance of m TLBO over other investigated algorithms. Finally, the paper investigates the data clustering performance of m TLBO over other evolutionary algorithms on a few standard synthetic and artificial datasets. Results of our work reveal that m TLBO performs better than many other algorithms investigated in this work.
118 citations
TL;DR: A new Artificial Bee Colony (ABC) algorithm for image contrast enhancement is proposed, using a grey-level mapping technique and a new image quality measure, and the comparisons of the obtained results with the genetic algorithm have proven its superiority.
Abstract: Image Enhancement is a crucial phase in almost every image processing system It aims at improving both the visual and the informational quality of distorted images Histogram Equalization (HE) techniques are the most popular approaches for image enhancement for they succeed in enhancing the image and preserving its main characteristics However, using exhaustive approaches for histogram equalisation is an algorithmically complex task These HE techniques also fail in offering good enhancement if not so good parameters are chosen So, new intelligent approaches, using Artificial Intelligence techniques, have been proposed for image enhancement In this context, this paper proposes a new Artificial Bee Colony (ABC) algorithm for image contrast enhancement A grey-level mapping technique and a new image quality measure are used The algorithm has been tested on some test images, and the comparisons of the obtained results with the genetic algorithm have proven its superiority Moreover, the proposed algorithm has been extended to colour image enhancement and given very promising results Further qualitative and statistical comparisons of the proposed ABC to the Cuckoo Search (CS) algorithm are also presented in the paper; not only for the adopted grey-level mapping technique, but also with using another common transformation, generally called the local/global transformation
110 citations
TL;DR: A binary version of the artificial fish swarm algorithm is proposed for solving the 0–1 multidimensional knapsack problem and a comparison shows that the proposed method gives a competitive performance when solving this kind of problems.
Abstract: The 0–1 multidimensional knapsack problem (MKP) arises in many fields of optimization and is NP-hard. Several exact as well as heuristic methods exist. Recently, an artificial fish swarm algorithm has been developed in continuous global optimization. The algorithm uses a population of points in space to represent the position of fish in the school. In this paper, a binary version of the artificial fish swarm algorithm is proposed for solving the 0–1 MKP. In the proposed method, a point is represented by a binary string of 0/1 bits. Each bit of a trial point is generated by copying the corresponding bit from the current point or from some other specified point, with equal probability. Occasionally, some randomly chosen bits of a selected point are changed from 0 to 1, or 1 to 0, with an user defined probability. The infeasible solutions are made feasible by a decoding algorithm. A simple heuristic add_item is implemented to each feasible point aiming to improve the quality of that solution. A periodic reinitialization of the population greatly improves the quality of the solutions obtained by the algorithm. The proposed method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method gives a competitive performance when solving this kind of problems.
84 citations
TL;DR: Performance comparison of CEFLann and RBF with different learning schemes clearly reveals that CEFLANN model trained with SADHS-OELM outperforms other learning methods and also the RBF model for both stock index and volatility prediction.
Abstract: This paper proposes a hybrid learning framework called Self Adaptive Differential Harmony Search Based Optimized Extreme Learning Machine (SADHS-OELM) for single hidden layer feed forward neural network (SLFN). The new learning paradigm seeks to take advantage of the generalization ability of extreme learning machines (ELM) along with the global learning capability of a self adaptive differential harmony search technique in order to optimize the fitting performance of SLFNs. SADHS is a variant of harmony search technique that uses the current to best mutation scheme of DE in the pitch adjustment operation for harmony improvisation process. SADHS has been used for optimal selection of the hidden layer parameters, the bias of neurons of the hidden-layer, and the regularization factor of robust least squares, whereas ELM has been applied to obtain the output weights analytically using a robust least squares solution. The proposed learning algorithm is applied on two SLFNs i.e. RBF and a low complexity Functional link Artificial Neural Networks (CEFLANN) for prediction of closing price and volatility of five different stock indices. The proposed learning scheme is also compared with other learning schemes like ELM, DE-OELM, DE, SADHS and two other variants of harmony search algorithm. Performance comparison of CEFLANN and RBF with different learning schemes clearly reveals that CEFLANN model trained with SADHS-OELM outperforms other learning methods and also the RBF model for both stock index and volatility prediction.
74 citations
TL;DR: This chapter introduces a new algorithmic nature inspired approach based on Bumble Bees Mating Optimization for successfully solving the Vehicle Routing Problem.
Abstract: Recently, a number of swarm intelligence algorithms based on the behaviour of the bees have been presented. These algorithms are divided, mainly, in two categories according to the bees’ behaviour in the nature, the foraging behaviour and the mating behaviour. The most important approaches that simulate the foraging behaviour of the bees are the Artificial Bee Colony algorithm, the Virtual Bee algorithm, the Bee Colony Optimization algorithm, the BeeHive algorithm, the Bee Swarm Optimization algorithm and the Bees algorithm. Contrary to the fact that there are many algorithms that are based on the foraging behaviour of the bees, the main algorithm proposed based on the mating behaviour is the Honey Bees Mating Optimization algorithm. This chapter introduces a new algorithmic nature inspired approach based on Bumble Bees Mating Optimization for successfully solving the Vehicle Routing Problem. Bumble Bees Mating Optimization algorithm is a new population-based swarm intelligence algorithm that simulates the mating behaviour that a swarm of bumble bees perform. Two sets of benchmark instances are used in order to test the proposed algorithm with very satisfactory results.
62 citations
TL;DR: The two-swarm cooperative particle swarm optimization (TCPSO) can not only catch the global optimum in a large search space such as 2×10 10, but also obtains a good balance between the swarm diversity and the convergence speed.
Abstract: Particle swarm optimization (PSO) is a powerful stochastic evolutionary algorithm based on swarm intelligence. However, the canonical PSO is easily and prematurely trapped in the local optima due to loss of its diversity. Although some improved algorithms retain the diversity, their speeds of convergence become slow. Meanwhile, PSO could only find out the global optimum in a small search interval, which greatly limits its applications to many practical engineering problems. In this study, the two-swarm cooperative particle swarm optimization (TCPSO) is presented. TCPSO can not only catch the global optimum in a large search space such as 2×10 10 , but also obtains a good balance between the swarm diversity and the convergence speed. It uses two particle swarms, the slave swarm and the master swarm with the clear division of their works. The former particles are updated without using the current velocities, the dimension of each particle learns from the same dimension of its neighboring particle instead of the best-so-far position. These features make the particles of the slave swarm concentrate toward the local optimum, thus accelerating the convergence. The latter particles are updated based on the former particles. And the equation in which the velocities of its particles are updated uses a large inertia weight. The feature of the master swarm keeps its diversity invariant. The experiments on TCPSO through 14 test functions showed that it significantly improves the performance of PSO and possesses the best performance among all the examined problems no matter multimodal or unimodal functions.
TL;DR: A novel hybrid algorithm with the features of advanced genetic algorithm and particle swarm optimization has been developed for determining the best found solutions for solving mixed integer nonlinear reliability optimization problems in series, series–parallel and bridge systems.
Abstract: This paper deals with the development of an efficient hybrid approach based on genetic algorithm and particle swarm optimization for solving mixed integer nonlinear reliability optimization problems in series, series–parallel and bridge systems. This approach maximizes the overall system reliability subject to the nonlinear resource constraints arising on system cost, volume and weight. To meet these purposes, a novel hybrid algorithm with the features of advanced genetic algorithm and particle swarm optimization has been developed for determining the best found solutions. To test the capability and effectiveness of the proposed algorithm, three numerical examples have been solved and the computational results have been compared with the existing ones. From comparison, it is observed that the values of the system reliability are better than the existing results in all three examples. Moreover, the values of average computational time and standard deviation are better than the same of similar studies available in the existing literature. The proposed approach would be very helpful for reliability engineers/practitioners for better understanding about the system reliability and also to reach a better configuration.
TL;DR: On examining the performance metrics, it is observed that the proposed NS-MOPSO approach is capable of identifying good Pareto solutions, maintaining adequate diversity.
Abstract: This paper addresses a realistic portfolio assets selection problem as a multiobjective optimization one, considering the budget, floor, ceiling and cardinality as constraints. A novel multiobjective optimization algorithm, namely the non-dominated sorting multiobjective particle swarm optimization (NS-MOPSO), has been proposed and employed efficiently to solve this important problem. The performance of the proposed algorithm is compared with four multiobjective evolution algorithms (MOEAs), based on non-dominated sorting, and one MOEA algorithm based on decomposition (MOEA/D). The performance results obtained from the study are also compared with those of single objective evolutionary algorithms, such as the genetic algorithm (GA), tabu search (TS), simulated annealing (SA) and particle swarm optimization (PSO). The comparisons of the performance include three error measures, four performance metrics, the Pareto front and computational time. A nonparametric statistical analysis, using the Sign test and Wilcoxon signed rank test, is also performed, to demonstrate the superiority of the NS-MOPSO algorithm. On examining the performance metrics, it is observed that the proposed NS-MOPSO approach is capable of identifying good Pareto solutions, maintaining adequate diversity. The proposed algorithm is also applied to different cardinality constraint conditions, for six different market indices, such as the Hang-Seng in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA, Nikkei 225 in Japan, and BSE-500 in India.
TL;DR: The results prove that the proposed methods are time efficient while compared to their conventional counterparts, and also reveals the weaknesses of the latter.
Abstract: In this paper, two meta-heuristics techniques have been employed to introduce two new quantum inspired meta-heuristic methods, namely quantum inspired genetic algorithm and quantum inspired particle swarm optimization for bi-level thresholding. The proposed methods use Otsu's method, Ramesh's method, Li's method, Shanbag's method and also correlation coefficient as evaluation functions to determine optimal threshold values of gray-level images. They exploit the trivial concepts of quantum computing such as qubits and superposition of states. These properties help to exhibit the feature of parallelism that in turn utilizes the time discreteness of quantum mechanical systems. The proposed methods have been compared with their classical counterparts and later with the quantum evolutionary algorithm (QEA) proposed by Han et al. to evaluate the performance among all participating algorithms for three test images. The optimal threshold value with the corresponding fitness value, standard deviation of fitness and finally the computational time of each method for each test image have been reported. The results prove that the proposed methods are time efficient while compared to their conventional counterparts. Another comparative study of the proposed methods with the quantum evolutionary algorithm (QEA) proposed by Han et al. also reveals the weaknesses of the latter.
TL;DR: A new constraint handling technique is proposed that systematically takes closeness, diversity and feasibility as three objectives in a multi-objective subproblem and solutions in each iteration are sorted by optimal sequence method based on those three objectives.
Abstract: A new constraint handling technique for multi-objective genetic algorithm is proposed in this paper. There are two important issues in multi-objective genetic algorithm, closeness of the obtained solutions to the real Pareto frontier and diversity of the obtained solutions. If considering a constrained multi-objective programming problem, one needs to take account of feasibility of solutions. Thus, in this new constraint handling technique, we systematically take closeness, diversity and feasibility as three objectives in a multi-objective subproblem. And solutions in each iteration are sorted by optimal sequence method based on those three objectives. Then, the solutions inherited to the next generation are selected based on its optimal order. Numerical tests show that the solutions obtained by this method are not only feasible, but also close to the real Pareto front and have good diversity.
TL;DR: A Monte Carlo Simulation based probabilistic load flow, considering uncertainty in load demand and wind generation, is developed and used to modify the WPDGs and capacitors sizes utilizing a sensitivity based approach, which maintains branch currents and bus voltages within their prescribed limits.
Abstract: Wind Power Distributed Generators (WPDGs) are being increasingly placed in the power system due to their several technical and environmental benefits. In this paper, a Modified Particle Swarm Optimizer (MPSO) based method is proposed for placement of multiple WPDGs and capacitors. Monte Carlo Simulation (MCS) based probabilistic load flow, considering uncertainty in load demand and wind generation, is developed. It is used to modify the WPDGs׳ and capacitors׳ sizes utilizing a sensitivity based approach, which maintains branch currents and bus voltages within their prescribed limits. The proposed method is simple, accurate and generic, and it can provide multiple choices to the utilities to place capacitors and WPDGs under various system constraints. Results on three distribution networks demonstrate the effectiveness of the proposed method. The impact of the DG placement on the system voltage profile, line loss, environment, and cost of generation has also been investigated on three distribution systems.
TL;DR: A comparative study of the performance of conventional gradient based methods like LMS, NLMS and RLS, and swarm intelligence based PSO, BFO, GA and ABC techniques is made which reveals that PSO technique gives better performance in average cases of noisy environment with minimum computational complexity.
Abstract: In this paper, event related potential (ERP) generated due to hand movement is detected through the adaptive noise canceller (ANC) from the electroencephalogram (EEG) signals. ANCs are implemented with least mean square (LMS), normalized least mean square (NLMS), recursive least square (RLS) and evolutionary algorithms like particle swarm optimization (PSO), bacteria foraging optimization (BFO) techniques, genetic algorithm (GA) and artificial bee colony (ABC) optimization technique. Performance of this algorithm is evaluated in terms of signal to noise ratio (SNR) in dB, correlation between resultant and template ERP, and mean value. Testing of their noise attenuation capability is done on EEG contaminated with white noise at different SNR levels. A comparative study of the performance of conventional gradient based methods like LMS, NLMS and RLS, and swarm intelligence based PSO, BFO, GA and ABC techniques is made which reveals that PSO technique gives better performance in average cases of noisy environment with minimum computational complexity.
TL;DR: An enhanced self-adaptive differential evolution (ESADE) for global numerical optimization over continuous space is presented and the computational results showed that the ESADE algorithm generally performed better than the state-of-the-art differential evolution variants and PSO.
Abstract: Nowadays, differential evolution (DE) has attracted more and more attention as an effective approach for solving numerical optimization problems. However, the fact that users have to set the control parameters of DE according to every single different problem makes the adjustment of control parameters a very time-consuming work. To solve the problem, this paper presents an enhanced self-adaptive differential evolution (ESADE) for global numerical optimization over continuous space. In this ESADE algorithm, different control parameters have been used to make mutation and crossover. Here is the detailed process: Firstly, it initializes two groups of population. Secondly, it generates a set of control parameters for one of the two populations and then further creates another new series of control parameters for the other population through mutating the initial control parameters. Thirdly, once the control parameters are generated, the two populations are mutated and crossed to produce two groups of trial vectors. Finally, the target vectors are selected from the two groups of trial vectors by selecting operation. In order to enhance its global search capabilities, simulated annealing (SA) are involved in the selecting operation and the control parameters with better performance are chosen as the initial control parameters of the next generation. By employing a set of 17 benchmark functions from previous literature, this study carried out extensive computational simulations and comparisons and the computational results showed that the ESADE algorithm generally performed better than the state-of-the-art differential evolution variants and PSO. Besides, the influences of initialized ambient temperature and simulated annealing on the performance of ESADE have also been tested. For the purpose of testing the application of ESADE in solving real-world problems, ESADE was applied to identify the parameters of proton exchange membrane fuel cell model. The results showed that ESADE was equal with other state-of-the-art differential evolution variants on performance.
TL;DR: The results show that the proposed ANSSA-based Bees Algorithm is able to achieve better Pareto solutions for the supply chain problem.
Abstract: In this paper, an enhanced version of the Bees Algorithm is proposed in dealing with multi-objective supply chain model to find the optimum configuration of a given supply chain problem in order to minimise the total cost and the total lead-time. The new Bees Algorithm includes an adaptive neighbourhood size change and site abandonment (ANSSA) strategy which is an enhancement to the basic Bees Algorithm. The supply chain case study utilised in this work is taken from literature and several experiments have been conducted in order to show the performances, the strength, the weaknesses of the proposed method and the results have been compared to those achieved by the basic Bees Algorithm and Ant Colony optimisation. The results show that the proposed ANSSA-based Bees Algorithm is able to achieve better Pareto solutions for the supply chain problem.
TL;DR: This paper provides an overview of the algorithms that were proposed in the literature to solve DMOOPs and challenges, practical aspects and possible future research directions of DMOO are discussed.
Abstract: Most real-world optimisation problems are dynamic in nature with more than one objective, where at least two of these objectives are in conflict with one another. This kind of problems is referred to as dynamic multi-objective optimisation problems (DMOOPs). Most research in multi-objective optimisation (MOO) have focussed on static MOO (SMOO) and dynamic single-objective optimisation. However, in recent years, algorithms were proposed to solve dynamic MOO (DMOO). This paper provides an overview of the algorithms that were proposed in the literature to solve DMOOPs. In addition, challenges, practical aspects and possible future research directions of DMOO are discussed.
TL;DR: A multi-objective genetic algorithm with cyclic crossover, two-point mutation, and refining operation is used to solve the TSP problem by introducing the refining operator and experimental results obtained are highly encouraging.
Abstract: In this paper, we have presented a multi-objective solid travelling salesman problem (TSP) in a fuzzy environment. The attraction of the solid TSP is that a traveller visits all the cities in his tour using multiple conveyance facilities. Here we consider cost and time as two objectives of the solid TSP. The objective of the study is to find a complete tour such that both the total cost and the time are minimized. We consider travelling costs and times for one city to another using different conveyances are different and fuzzy in nature. Since cost and time are considered as fuzzy in nature, so the total cost and the time for a particular tour are also fuzzy in nature. To find out Pareto-optimal solution of fuzzy objectives we use fuzzy possibility and necessity measure approach. A multi-objective genetic algorithm with cyclic crossover, two-point mutation, and refining operation is used to solve the TSP problem. In this paper a multi-objective genetic algorithm has been modified by introducing the refining operator. Finally, experimental results are given to illustrate the proposed approach; experimental results obtained are also highly encouraging.
TL;DR: This study takes into account the recurrence number of the fuzzy relations in the stage of defuzzification and applies this approach to the real data sets which are often used in other studies in literature and concludes that the results present superior forecasts performance.
Abstract: Fuzzy time series approaches, which do not require the strict assumptions of traditional time series approaches, generally consist of three stages. These are called as the fuzzification of crisp time series observations, the identification of fuzzy relationships and the defuzzification. All of these stages play a very important role on the forecasting performance of the model. Although there are many studies contributing to the stages of fuzzification and determining fuzzy relationships, the number of the studies about the defuzzification stage, which is very important at least as much as the others, is limited. None of them considered the number of recurrence of the fuzzy relationships in the stage of defuzzification. However it is very reasonable to take into account since fuzzy relations and their recurrence number are reflected the nature of the time series. Then the information obtained from the fuzzy relationships can be used in the defuzzification stage. In this study, we take into account the recurrence number of the fuzzy relations in the stage of defuzzification. Then this new approach has been applied to the real data sets which are often used in other studies in literature. The results are compared to the ones obtained from other techniques. Thus it is concluded that the results present superior forecasts performance.
TL;DR: This paper proposes a Dynamic Recommender system (DRS) based on evolutionary clustering algorithm that makes clusters of similar users and evolves them depicting accurate and relevant user preferences over time and shows considerable improvement in terms of quality of recommendations and computation time.
Abstract: The use of internet and Web services is changing the way we use resources and communicate since the last decade. Although, this usage has made life easier in many respects still the problem of finding relevant information persists. A naive user faces the problem of information overload and continuous flow of new information makes the problem more complex. Furthermore, user′s interests also keeps on changing with time. Several techniques deal with this problem and data mining is widely used among them. Recommender Systems (RSs) assist users in finding relevant information on the web and are mostly based on data mining algorithms. This paper addresses the problem of user requirements changing over a period of time in seeking information on web and how RSs deal with them. We propose a Dynamic Recommender system (DRS) based on evolutionary clustering algorithm. This clustering algorithm makes clusters of similar users and evolves them depicting accurate and relevant user preferences over time. The proposed approach performs an optimization of conflicting parameters instead of using the traditional evolutionary algorithms like genetic algorithm. The algorithm has been empirically tested and compared with standard recommendation algorithms and it shows considerable improvement in terms of quality of recommendations and computation time.
TL;DR: A novel AFSA algorithm, so called NAFSA, has been proposed in order to eliminate weak points of standard AFSA and increase convergence speed of the algorithm and extensive experiments show that the proposed algorithm significantly outperforms previous algorithms in most of the tested dynamic environments modeled by moving peaks benchmark.
Abstract: Artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligence algorithms that is widely used for optimization purposes in static environments. However, numerous real-world problems are dynamic and uncertain, which could not be solved using static approaches. The contribution of this paper is twofold. First, a novel AFSA algorithm, so called NAFSA, has been proposed in order to eliminate weak points of standard AFSA and increase convergence speed of the algorithm. Second, a multi-swarm algorithm based on NAFSA (mNAFSA) was presented to conquer particular challenges of dynamic environment by proposing several novel mechanisms including particularly modified multi-swarm mechanism for finding and covering potential optimum peaks and diversity increase mechanism which is applied after detecting an environment change. The proposed approaches have been evaluated on moving peak benchmark, which is the most prominent benchmark in this domain. This benchmark involves several parameters in order to simulate different configurations of dynamic environments. Extensive experiments show that the proposed algorithm significantly outperforms previous algorithms in most of the tested dynamic environments modeled by moving peaks benchmark.
TL;DR: A time-adaptive topology is proposed that enables a variant of the particle swarm optimization (PSO) to locate many feasible regions at the early stages of the optimization process and to identify the most promising one at the latter stages ofthe optimization process.
Abstract: For constrained optimization problems set in a continuous space, feasible regions might be disjointed and the optimal solution might be in any of these regions. Thus, locating these feasible regions (ideally all of them) as well as identifying the most promising region (in terms of objective value) at the end of the optimization process would be of a great significance. In this paper a time-adaptive topology is proposed that enables a variant of the particle swarm optimization (PSO) to locate many feasible regions at the early stages of the optimization process and to identify the most promising one at the latter stages of the optimization process. This PSO variant is combined with two local searches which improve the ability of the algorithm in both finding feasible regions and higher quality solutions. This method is further hybridized with covariance matrix adaptation evolutionary strategy (CMA-ES) to enhance its ability to improve the solutions at the latter stages of the optimization process. Results generated by this hybrid method are compared with the results of several other state-of-the-art methods in dealing with standard benchmark constraint optimization problems.
TL;DR: A new approach is proposed in which hybrid PSO algorithms incorporate noise mitigation mechanisms from the other two approaches, and the quality of their results is better than that of the state of the art with a few exceptions.
Abstract: Particle swarm optimization (PSO) is a metaheuristic designed to find good solutions to optimization problems. However, when optimization problems are subject to noise, the quality of the resulting solutions significantly deteriorates, hence prompting the need to incorporate noise mitigation mechanisms into PSO. Based on the allocation of function evaluations, two opposite approaches are generally taken. On the one hand, resampling-based PSO algorithms incorporate resampling methods to better estimate the objective function values of the solutions at the cost of performing fewer iterations. On the other hand, single-evaluation PSO algorithms perform more iterations at the cost of dealing with very inaccurately estimated objective function values. In this paper, we propose a new approach in which hybrid PSO algorithms incorporate noise mitigation mechanisms from the other two approaches, and the quality of their results is better than that of the state of the art with a few exceptions. The performance of the algorithms is analyzed by means of a set of population statistics that measure different characteristics of the swarms throughout the search process. Amongst the hybrid PSO algorithms, we find a promising algorithm whose simplicity, flexibility and quality of results questions the importance of incorporating complex resampling methods into state-of-the-art PSO algorithms.
TL;DR: The results on the standard IEEE systems demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective EELD.
Abstract: In this paper, a new hybrid optimization system is presented. Our approach integrates the merits of both ant colony optimization and steady state genetic algorithm and it has two characteristic features. Firstly, since there is instabilities in the global market and the rapid fluctuations of prices, a fuzzy representation of the economic emission load dispatch (EELD) problem has been defined, where the input data involve many parameters whose possible values may be assigned by the expert. Secondly, by enhancing ant colony optimization through steady state genetic algorithm, a strong robustness and more effectively algorithm was created. Also, stable Pareto set of solutions has been detected, where in a practical sense only Pareto optimal solutions that are stable are of interest since there are always uncertainties associated with efficiency data. Moreover to help the decision maker DM to extract the best compromise solution from a finite set of alternatives a Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method is adopted. It is based upon simultaneous minimization of distance from an ideal point (IP) and maximization of distance from a nadir point (NP). The results on the standard IEEE systems demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective EELD.
TL;DR: An extensive study into aesthetic measures in unsupervised evolutionary art (EvoArt), comparing 7 different aesthetic measures through subjective criteria (‘style’) as well as by quantitative measures reflecting properties of the evolved images.
Abstract: We present an extensive study into aesthetic measures in unsupervised evolutionary art (EvoArt). In contrast to several mainstream EvoArt approaches we evolve images without human interaction, using one or more aesthetic measures as fitness functions. We perform a series of systematic experiments, comparing 7 different aesthetic measures through subjective criteria (‘style’) as well as by quantitative measures reflecting properties of the evolved images. Next, we investigate the correlation between aesthetic scores by aesthetic measures and calculate how aesthetic measures judge each others′ image. Furthermore, we run experiments in which two aesthetic measures are acting simultaneously using a Multi-Objective Evolutionary Algorithm. Hereby we gain insights in the joint effects on the resulting images and the compatibility of different aesthetic measures.
TL;DR: Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems as discussed by the authors, which makes no assumption of the function to be optimized and typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop.
Abstract: Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems. Its popularity is due to its repeated successes in finding an optimum or a near optimal solution for problems in many applied disciplines. The algorithm makes no assumption of the function to be optimized and for biomedical experiments like those presented here, PSO typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop. We apply PSO to find various types of optimal designs for several problems in the biological sciences and compare PSO performance relative to the differential evolution algorithm, another popular metaheuristic algorithm in the engineering literature.
TL;DR: A novel scheduling algorithm using optimization approach based on genetic algorithm (GA) hybridized with knowledge from the real-time task scheduling domain for providing fault-tolerance (FT) in multiprocessor environment is proposed.
Abstract: Conventional methods for fault-tolerant scheduling of real-time tasks based on traditional heuristic approach offer poor performance and inefficient system utilization. The primary-backup (PB) approach is often used as a fault-tolerant scheduling technique to guarantee RT tasks to meet their deadline despite the presence of fault. We propose a novel scheduling algorithm using optimization approach based on genetic algorithm (GA) hybridized with knowledge from the real-time task scheduling domain for providing fault-tolerance (FT) in multiprocessor environment. Exhaustive simulation reveals that the new GA based primary-backup fault-tolerant scheduling (PBFTS) approach outperforms other fault-tolerant scheduling schemes in terms of system utilization and efficiency.