Showing papers on "Extremal optimization published in 2012"
01 Oct 2012
TL;DR: The simulation examples demonstrate that the GA–PSO-ACO algorithm can greatly improve the computing efficiency for solving the TSP and outperforms the Tabu Search, genetic algorithms, particle swarm optimization, ant colony optimization, PS–ACO and other methods in solution quality.
Abstract: This paper presents a novel two-stage hybrid swarm intelligence optimization algorithm called GA---PSO---ACO algorithm that combines the evolution ideas of the genetic algorithms, particle swarm optimization and ant colony optimization based on the compensation for solving the traveling salesman problem. In the proposed hybrid algorithm, the whole process is divided into two stages. In the first stage, we make use of the randomicity, rapidity and wholeness of the genetic algorithms and particle swarm optimization to obtain a series of sub-optimal solutions (rough searching) to adjust the initial allocation of pheromone in the ACO. In the second stage, we make use of these advantages of the parallel, positive feedback and high accuracy of solution to implement solving of whole problem (detailed searching). To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems from TSPLIB are tested to demonstrate the potential of the proposed two-stage hybrid swarm intelligence optimization algorithm. The simulation examples demonstrate that the GA---PSO---ACO algorithm can greatly improve the computing efficiency for solving the TSP and outperforms the Tabu Search, genetic algorithms, particle swarm optimization, ant colony optimization, PS---ACO and other methods in solution quality. And the experimental results demonstrate that convergence is faster and better when the scale of TSP increases.
149 citations
TL;DR: In this paper some directions for improving the original framework when a strong local search routine is available, are identified and some modifications able to speed up the method and make it competitive on large problem instances are described.
80 citations
TL;DR: The rigorous runtime analysis for two ant colony optimization algorithms, based on these two construction procedures, shows that they lead to good approximation in expected polynomial time on random instances.
Abstract: Bioinspired algorithms, such as evolutionary algorithms and ant colony optimization, are widely used for different combinatorial optimization problems. These algorithms rely heavily on the use of randomness and are hard to understand from a theoretical point of view. This paper contributes to the theoretical analysis of ant colony optimization and studies this type of algorithm on one of the most prominent combinatorial optimization problems, namely the traveling salesperson problem (TSP). We present a new construction graph and show that it has a stronger local property than one commonly used for constructing solutions of the TSP. The rigorous runtime analysis for two ant colony optimization algorithms, based on these two construction procedures, shows that they lead to good approximation in expected polynomial time on random instances. Furthermore, we point out in which situations our algorithms get trapped in local optima and show where the use of the right amount of heuristic information is provably beneficial.
51 citations
TL;DR: It is shown that the adaptation methods proposed so far do not improve, and often even worsen the performance when applied to high performing ant colony optimization algorithms for some classical combinatorial optimization problems.
Abstract: Applying parameter adaptation means operating on parameters of an algorithm while it is tackling an instance. For ant colony optimization, several parameter adaptation methods have been proposed. In the literature, these methods have been shown to improve the quality of the results achieved in some particular contexts. In particular, they proved to be successful when applied to novel ant colony optimization algorithms for tackling problems that are not a classical testbed for optimization algorithms. In this paper, we show that the adaptation methods proposed so far do not improve, and often even worsen the performance when applied to high performing ant colony optimization algorithms for some classical combinatorial optimization problems.
41 citations
10 Jun 2012
TL;DR: This research investigates the application of the ant colony algorithm to minimize user delay at traffic intersections and results show that this new approach outperforms conventional fully actuated control, especially under the condition of high traffic demand.
Abstract: Traffic signal control is an effective way to improve the efficiency of traffic networks and reduce users' delays. Ant Colony Optimization (ACO) is a meta-heuristic algorithm based on the behavior of ant colonies searching for food. ACO has successfully been employed to solve many complicated combinatorial optimization problems and its stochastic and decentralized nature fits well with traffic networks. This research investigates the application of the ant colony algorithm to minimize user delay at traffic intersections. Various ACO algorithms are discussed and a rolling horizon approach is also employed to achieve real-time adaptive control. Computer simulation results show that this new approach outperforms conventional fully actuated control, especially under the condition of high traffic demand.
24 citations
Posted Content•
TL;DR: In this paper, a modified generalized extremal optimization (GEO) algorithm is proposed to solve the quay crane scheduling problem with respect to various interference constraints, which is termed as the modified GEO.
Abstract: The quay crane scheduling problem (QCSP) determines the handling sequence of tasks at ship bays by a set of cranes assigned to a container vessel such that the vessel's service time is minimized. A number of heuristics or meta-heuristics have been proposed to obtain the near-optimal solutions to overcome the NP-hardness of the problem. In this article, the idea of generalized extremal optimization (GEO) is adapted to solve the QCSP with respect to various interference constraints. The resulted GEO is termed as the modified GEO. A randomized searching method for neighboring task-to-QC assignments to an incumbent task-to-QC assignment is developed in executing the modified GEO. In addition, a unidirectional search decoding scheme is employed to transform a task-to-QC assignment to an active quay crane schedule. The effectiveness of the developed GEO is tested on a suite of benchmark problems introduced by \citet{KimPark2004}. Compared with other well known existing approaches, the experiment results show that the proposed modified GEO is capable of obtaining the optimal or near-optimal solution in reasonable time, especially for large-sized problems.
21 citations
12 Nov 2012
TL;DR: A novel type of quantum-inspired evolutionary algorithm for numerical optimization inspired by the multiple universes principle of quantum computing, which is based on the concept and principles of quantum Computing, such as a quantum bit and superposition of states is proposed.
Abstract: This paper proposes a novel type of quantum-inspired evolutionary algorithm (QiEA) for numerical optimization inspired by the multiple universes principle of quantum computing, which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Numerical optimization problems are an important field of research with several applications in several areas: industrial plant optimization, data mining and many others, and although being successfully used for solving several optimization problems, evolutionary algorithms still present issues that can reduce their performances when faced with task where the evaluation function is computationally intensive. In order to address those issues the QiEA represent the most recent advance in the field of evolutionary computation. This work present some application about combinatorial and numerical optimization problems.
20 citations
TL;DR: A Hybrid Algorithm combining Particle Swarm Optimization (PSO) and Simulated Annealing (SA) is proposed, in order to solve the PTSP, and improves the performance of simple PSO algorithm for all instances.
Abstract: The Probabilistic Traveling Salesman Problem (PTSP) is a variation of the well known Traveling Salesman Problem (TSP). This problem arises when the information about customers demand is not available at the moment of the tour generation and/or the tour re-calculating cost is too elevated. In this article, a Hybrid Algorithm combining Particle Swarm Optimization (PSO) and Simulated Annealing (SA) is proposed, in order to solve the PTSP. The PSO heuristic offers a simple structured algorithm which supplies a high level of exploration and fast convergence, compared with other evolutionary algorithms. The SA algorithm is used to improve the particle diversity and to avoid the algorithm being trapped into local optimum. Two well-known benchmarks of the literature are used and the proposed PSO-SA algorithm obtains acceptable results. In fact, the hybrid algorithm improves the performance of simple PSO algorithm for all instances.
16 citations
01 Jan 2012
TL;DR: An approach that utilizes a genetic algorithm and discrete event simulation to solve the physician scheduling problem in a hospital is proposed and is tested on real world datasets for physician schedules.
Abstract: Emergency departments have repeating 24-hour cycles of non-stationary Poisson arrivals and high levels of service time variation. The problem is to find a shift schedule that considers queuing effects and minimizes average patient waiting time and maximizes physicians’ shift preference subject to constraints on shift start times, shift durations and total physician hours available per day. An approach that utilizes a genetic algorithm and discrete event simulation to solve the physician scheduling problem in a hospital is proposed. The approach is tested on real world datasets for physician schedules.
15 citations
01 Jan 2012
TL;DR: This paper describes a graph-theoretic model of system-level synthesis, and presents three important optimization problems: scheduling, allocation, and partitioning, which turn out be NP-hard.
Abstract: System-level synthesis aims at partially automating the design and synthesis process of complex systems that consist of both hardware and software. This involves the usage of formal methods such as graph theory, as well as the formulation of some design steps explicitly as optimization problems. This paper describes such a graph-theoretic model, and presents three important optimization problems: scheduling, allocation, and partitioning. All of these turn out be NP-hard. However, some important sub-cases can be efficiently solved.
14 citations
TL;DR: In this article, a multi-objective optimization algorithm called M-GEOreal (Multiobjective Generalized Extremal Optimi-zation with real codification) is proposed to deal with non-linear system and designing nonlinear controller with constant gains facilitating the on board computer implementation.
Abstract: The Attitude Control System (ACS) for Flexible Space Structures (FSS) like rigid-flexible satellite and solar sails demands great reliability, autonomy and robustness. The association of flexible motion and large angle maneuver imply that the FSS dynamics is only captured by complex non-linear mathematical model. As a result, FSS controller performance designed by linear control technique under the hypothesis of rigid dynamic can be degraded. Although vibrations can be suppressed rapidly, the flexibility effect can introduce a tracking error resulting in a minimum attitude acquisition time. On the other hand, faster manoeuvres can excite flexible modes in such a way to make the FSS lose the required pointing accuracy. In the present work, it is shown that a new multi-objective optimization algorithm, called M-GEOreal (Multi-objective Generalized Extremal Optimi- zation with real codification), is a good tool to be used in such kind of problems. The M-GEOreal is a real coded version of the M-GEO evolutionary algorithm. Its performance on finding the gains of a non linear control law is evaluated through its ap- plication to the problem of controlling a large angle attitude manoeuvre of a rigid-flexible satellite.. The satellite non-linear model consists of a rigid central hub with a clamped free flexible beam. The multi-objective approach allows optimizing con- flicting objective functions like time and energy. As a result, one can find a trade-off solution (non-dominated solutions). These solutions become available to the designer for posterior choice of an individual solution to be implemented. The non-dominated solutions are represented in the design space (Pareto set) and in the objective functions space (Pareto front). Having in mind the complexity of implementing a control algorithm in onboard satellite computer, this preliminary investigation has shown that the non-linear controller based on the M-GEOreal algorithm is a promising technique, since it has satisfied all the ACS requirements. A great advantage of the M-GEOreal procedure is its capacity to deal with non-linear system and designing non-linear controller with constant gains facilitating the on board computer implementation.
28 Feb 2012
TL;DR: Techniques for static task mapping onto general-purpose CMPs with multiple pre-defined voltage islands for power management with novel mapping approach based on Extremal Optimization are proposed.
Abstract: The complexity of large Chip Multiprocessors (CMP) makes design reuse a practical approach to reduce the manufacturing and design cost of high-performance systems. This paper proposes techniques for static task mapping onto general-purpose CMPs with multiple pre-defined voltage islands for power management. The CMPs are assumed to contain different classes of processing elements with multiple voltage/frequency execution modes to better cover a large range of applications. Task mapping is performed with awareness of both on-chip and off-chip memory traffic, and communication constraints such as the link and memory bandwidth. A novel mapping approach based on Extremal Optimization is proposed for large-scale CMPs. This new combinatorial optimization method has delivered very good results in quality and computational cost when compared to the classical simulated annealing.
01 Oct 2012
TL;DR: Comparison of test results indicates that the two algorithms, while being very different in strategy, provide similar performance and reach comparable probability distributions for spin selection, and the convergence speed is compared to other heuristic methods such as Neural Network (NN), Tabu Search (TS, and Genetic Algorithm) to approve the truthfulness of proposed methods.
Abstract: Nowadays, various imitations of natural processes are used to solve challenging optimization problems faster and more accurately. Spin glass based optimization, specifically, has shown strong local search capability and parallel processing. But, spin glasses have a low rate of convergence since they use Monte Carlo simulation techniques such as simulated annealing (SA). Here, we propose two algorithms that combine the long range effect in spin glasses with extremal optimization (EO-SA) and learning automata (LA-SA). Instead of arbitrarily flipping spins at each step, these two strategies aim to choose the next spin and selectively exploiting the optimization landscape. As shown in this paper, this selection strategy can lead to faster rate of convergence and improved performance. The resulting two algorithms are then used to solve portfolio selection problem that is a non-polynomial (NP) complete problem. Comparison of test results indicates that the two algorithms, while being very different in strategy, provide similar performance and reach comparable probability distributions for spin selection. Furthermore, experiments show there is no difference in speed of LA-SA or EO-SA for glasses with fewer spins, but EO-SA responds much better than LA-SA for large glasses. This is confirmed by tests results of five of the world's major stock markets. In the last, the convergence speed is compared to other heuristic methods such as Neural Network (NN), Tabu Search (TS), and Genetic Algorithm (GA) to approve the truthfulness of proposed methods.
Posted Content•
TL;DR: The contribution of this work is to provide the reader with a sort of consultation guide for developing ACO algorithms, by presenting a collection of different approaches that can be found in literature, regarding the ACO building blocks.
Abstract: Scientific literature is prolific both on exact and on heuristic solution methods developed to solve optimization problems. Although the former methods have an indisputable theoretical value when it comes to solve large realistic combinatorial optimization problems they are usually associated with large and even prohibitive running times. Heuristic methods, do not guarantee to determine a global optimal solution for a problem but are usually able to find a good solution rapidly, perhaps a local optimum, and require less computational resources. Ant Colony Optimization (ACO) algorithms belong to a class of heuristics based on the behaviour of nature ants. These algorithms have been used to solve many combinatorial optimization problems and have been known to outperform other popular heuristics such as Genetic Algorithms. Therefore, we believe that the number of ACO based algorithms will continue to grow for a long time. The contribution of this work is to provide the reader with a sort of consultation guide for developing ACO algorithms, by presenting a collection of different approaches that can be found in literature, regarding the ACO building blocks.
02 Jul 2012
TL;DR: A hybrid approach is proposed for TSP, which is based on ant colony optimization (ACO) and the mutation operators in genetic algorithm (GA) and compared the performance of PMACO, with ACO and GA separately.
Abstract: Traveling salesman problem (TSP) is a popular routing problem, which is a sub-problem of many application domains such as transportation, network communication, vehicle routing and integrated circuits designs. Among many approaches which have been proposed for TSP so far, evolutionary algorithms effectively applied to solve it, and attempt to avoid trapping in local minima. In this paper, a hybrid approach (named PMACO) is proposed for TSP, which is based on ant colony optimization (ACO) and the mutation operators in genetic algorithm (GA). In this manner, in the each iteration a hybrid mutation scheme is applied to search the neighborhood area of the solution corresponding with the two best ants (the best current ant, and the total best ant found so far). The population is divided to two parts: the first is regenerated according to the pheromone rule in ACO, and the last is generated by applying the proposed hybrid mutation scheme. Also in this paper, the initial population was not considered randomly; it was generated according to nearest neighborhood rule, which force the ants toward better solutions, and leads to reduce the running time. Finally, we compared the performance of PMACO, with ACO and GA separately. For each algorithm, the various parameters and operators were tested, and the bests were chosen to tune that. Experimental results were carried out on benchmarks from the TSPLIB for validation.
TL;DR: A hybrid ACO algorithm is proposed by introducing extremal optimization local-search algorithm to the ant colony optimization (ACO) algorithm, and is applied to multiuser detection in direct sequence ultra wideband (DS-UWB) communication system in this paper.
Abstract: A hybrid ant colony optimization algorithm is proposed by introducing extremal optimization local-search algorithm to the ant colony optimization (ACO) algorithm, and is applied to multiuser detection in direct sequence ultra wideband (DS-UWB) communication system in this paper. ACO algorithms have already successfully been applied to combinatorial optimization; however, as the pheromone accumulates, we may not get a global optimum because it can get stuck in a local minimum resulting in a bad steady state. Extremal optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide variety of optimization problems. Hence in this paper, a hybrid ACO algorithm, named ACO-EO algorithm, is proposed by introducing EO to ACO to improve the local-search ability of the algorithm. The ACO-EO algorithm is applied to multiuser detection in DS-UWB communication system, and via computer simulations it is shown that the proposed hybrid ACO algorithm has much better performance than other ACO algorithms and even equal to the optimal multiuser detector.
01 Dec 2012
TL;DR: In this paper, an extremal optimization based particle swarm optimization algorithm has been proposed to determine optimal size of system components, which can deliver energy in a stand-alone installation with minimum system cost and maximum reliability.
Abstract: One of the most important issue in designing of renewable hybrid energy systems is to size the system components optimally to meet load requirement with minimum annualized cost of system and maximum reliability of power supply. This paper presents comparison of two approaches for calculating optimal size of hybrid energy system components with minimum annualized cost considering reliability constraint. In this paper an extremal optimization based particle swarm optimization algorithm has been proposed to determine optimal size of system components. To demonstrate the validity of proposed approach and to evaluate relative merit of this approach, a comparative study has been done with simple particle swarm optimization algorithm considering identical system configuration. The simulation results show that the optimal system components can deliver energy in a stand -alone installation with minimum system cost and maximum reliability.
07 Jul 2012
TL;DR: The algorithms are assessed on the noiseless testbed of the Black-Box Optimization Benchmarking 2012 workshop, providing useful insight regarding their relative efficiency and effectiveness.
Abstract: MEMPSODE is a recently published optimization software that implements memetic Particle Swarm Optimization and Differential Evolution approaches. It combines previously proposed variants of the two algorithms, with the Merlin optimization environment, which includes a variety of established local search methods for continuous optimization. The present study aims at comparing the performance of the memetic variants produced by the two metaheuristics within the framework of MEMPSODE. The algorithms are assessed on the noiseless testbed of the Black-Box Optimization Benchmarking 2012 workshop, providing useful insight regarding their relative efficiency and effectiveness.
01 Jan 2012
TL;DR: An ant colony optimization based on the predicted traffic for time-dependent traveling salesman problems (TDTSP), where the travel time between cities changes with time, is proposed.
Abstract: In this paper, we propose an ant colony optimization based on the predicted traffic for time-dependent traveling salesman problems (TDTSP), where the travel time between cities changes with time. Prediction values required for searching is assumed to be given in advance. We previously proposed a method to improve the search rate of Max-Min Ant System (MMAS) for static TSPs. In the current work, the method is extended so that the predicted travel time can be handled and formalized in detail. We also present a method of generating a TDTSP to use in evaluating the proposed method. Experimental results using benchmark problems with 51 to 318 cities suggested that the proposed method is better than the conventional MMAS in the rate of search.
TL;DR: The experimental results demonstrate that the proposed DPPACO algorithm has a better global searching ability, higher convergence speed and solution diversity, and the proposed algorithm is applied to the traveling salesman problem.
Abstract: In allusion to the phenomenon of stagnation and precocity during evolution in ant colony optimization (ACO) algorithm, this paper proposed a dual population parallel ant colony optimization (DPPACO) algorithm, which was applied to the traveling salesman problem. The DPPACO algorithm separated the ants into soldier ant population and worker ant population which evolve separately by parallel method and exchanges information timely. The dynamic equilibrium between solution diversity and convergence speed is achieved by using the effect of the soldier ant’s distribution to worker ants’ movement choice. The DPPACO algorithm can enlarge searching range and avoid local minimum, prevent local convergence caused by misbalance of the pheromone and can improve the searching performance of the algorithm effectively. The proposed algorithm is applied in the traveling salesman problem by using the 17 data sets obtained from the TSPLIB. We compare the experimental results of the proposed DPPACO method with the traditional methods. The experimental results demonstrate that the proposed algorithm has a better global searching ability, higher convergence speed and solution diversity.
01 Nov 2012
TL;DR: The mathematical model of ant colony optimization algorithm has been proposed on the basis of the analysis to solve the TSP problems and the results of simulation indicate this algorithm has a quite good performance.
Abstract: This paper has made a detailed analysis of the Ant Colony Algorithm and its parameters, integrated the algorithm with the TSP problems and put forward such optimization methods as the node selection by means of piecewise function control, the pheromone updating by means of smooth elitist strategy and the adjacent edge adjustment in the global optimal solution by means of the 2-opt strategy. The mathematical model of ant colony optimization algorithm has been proposed on the basis of the analysis to solve the TSP problems and the results of simulation indicate this algorithm has a quite good performance.
13 Dec 2012
TL;DR: This paper proposes a novel heuristic using Modified Extremal Optimization (MEO) for CMO (Contact Map Overlap), which uses MEO for alternation generations and is characterized by three features.
Abstract: Proteins are important biochemical compounds that have biogenic functions for biological activities. The three-dimensional structures of proteins are closely related to its biological functions, and therefore, techniques for comparing them have been studied. Many of these techniques for comparing protein structures are based on protein structure alignment, which is one of the most effective methods. CMO (Contact Map Overlap) is formulated as combinatorial optimization to find the optimal structure alignments. In this paper, we propose a novel heuristic using Modified Extremal Optimization (MEO) for CMO. Our MEO-based heuristic is characterized by three features. First, the proposed heuristic uses MEO for alternation generations. Second, an initial solution is created by dynamic programming (DP). Third, state transition is executed using the best admissible move strategy.
Journal Article•
TL;DR: Results show that the HAPA has more advantages than the traditional algorithm and the similar algorithm in solving the traveling salesman problem.
Abstract: The Traveling Salesman Problem(TSP) is the oldest and most extensively studied combinatorial optimization problem.For the traveling salesman problem,Hybrid Ant colony and Particle swarm Algorithm(HAPA) is proposed.The HAPA divides the ant colony into several ant sub colonies,then optimizes parameters of the ant sub colonies as particles by the particle swarm optimization algorithm,and introduces the operation of swapping the pheromone in each ant sub colony.Results show that the HAPA has more advantages than the traditional algorithm and the similar algorithm in solving the traveling salesman problem.
TL;DR: The ground state entropy of the network source location problem is evaluated at both the replica symmetric level and one-step replica symmetry breaking level using the entropic cavity method, and the theoretical results are compared with the extremal optimization results.
Abstract: Ground state entropy of the network source location problem is evaluated at both the replica symmetric level and one-step replica symmetry breaking level using the entropic cavity method. The regime that is a focus of this study, is closely related to the vertex cover problem with randomly quenched covered nodes. The resulting entropic message passing inspired decimation and reinforcement algorithms are used to identify the optimal location of sources in single instances of transportation networks. The conventional belief propagation without taking the entropic effect into account is also compared. We find that in the glassy phase the entropic message passing inspired decimation yields a lower ground state energy compared to the belief propagation without taking the entropic effect. Using the extremal optimization algorithm, we study the ground state energy and the fraction of frozen hubs, and extend the algorithm to collect statistics of the entropy. The theoretical results are compared with the extremal optimization results.
01 Jan 2012
TL;DR: Constrained problems, either single or multi-objective, are difficult tasks to be modelled and solved by conventional mathematical techniques and an important area of research is in the domain of novel meta-heuristics that can efficiently solve such problems.
Abstract: Many real-world optimisation problems, both in the scientific and industrial world, can be classified as constrained combinatorial optimisation problems (CCOPs). Some of the most representative examples of these are: assignment, allocation, scheduling, timetabling, layout, design, routing and distribution problems. These sorts of problems usually have a considerable number of either integer or binary decision variables to which finite and discrete ranges of possible values are assigned. This assignation process has to take into account a set of capacity constraints that must be satisfied (e.g. weight, volume, workload, resources). Additionally, there exists an increasing demand in these sorts of problems to derive solutions that strike a balance between two or more desirable but incompatible objectives, such as maximising component strength while minimising component weight, maximising distance while minimising resource consumption or maximising productivity while minimising runtime. These are commonly referred to as multi-objective problems. Constrained problems, either single or multi-objective, are difficult tasks to be modelled and solved by conventional mathematical techniques. As such, an important area of research is in the domain of novel meta-heuristics that can efficiently solve such problems. In the last few decades, nature-inspired meta-heuristics have become an effective and efficient alternative used to solve single-objective and multi-objective CCOPs. These meta-heuristics capture ideas and features present in nature or our environment, which are then implemented as search algorithms. These search mechanisms have the ability to explore large and complex search spaces, looking for one or more optimal solutions. Genetic Algorithms (GAs), Memetic Algorithms (MAs) and Ant Colony Optimisation (ACO) are some of the more representative and successful meta-heuristics used by nature-inspired approaches. The features found in nature represented as an algorithm through these methods generally use a considerable number of operators and parameters that must be properly set. However, these methods are not the only techniques that can be used. Due to the increasing requirement to solve larger and more
01 Dec 2012
TL;DR: A novel approach to DPSO is used to obtain results in this situation, and Convergence of DPSO to the optimal solution for such a Graph based TSP is tasted.
Abstract: This paper presents a Discrete Particle Swarm Optimization (DPSO) technique to solve Graph based Travelling Salesman Problem (TSP) TSP can be represented as a graph where nodes represent cities and edges represent paths between them A partially connected graph addresses a more realistic problem than a completely connected graph since in real life paths may not exist between certain cities A novel approach to DPSO is used to obtain results in this situation Convergence of DPSO to the optimal solution for such a Graph based TSP is tasted
TL;DR: A new hybrid local search method based on spin glass for using adaptive distributed system capability, extremal optimization (EO) for using evolutionary local search algorithm and SA for escaping from local optimum states and trap to global ones is investigated.
16 Jan 2012
TL;DR: In this article, a heuristic optimization module based on Variable Neighborhood Search (VNS) is used to find extremal or near extremal graphs, i.e., graphs that minimize or maximize an invariant.
Abstract: Using a heuristic optimization module based upon Variable Neighborhood Search (VNS), the system AutoGraphiX's main feature is to find extremal or near extremal graphs, i.e., graphs that minimize or maximize an invariant. From the so obtained graphs, conjectures are found either automatically or interactively. Most of the features of the system relies on the optimization that must be efficient but the variety of problems handled by the system makes the tuning of the optimizer difficult to achieve. We propose a learning algorithm that is trained during the optimization of the problem and provides better results than all the algorithms previously used for that purpose.
22 Jul 2012
TL;DR: Simulation results on power systems which are composed of up to 100-units over a scheduling horizon of 24-hours demonstrate competitive performance with EO method compared with other existing methods for UC problem.
Abstract: This paper introduces extremal optimization (EO) method to solve unit commitment problem for power systems. EO is a local-search heuristic algorithm and originally developed from the fundamentals of statistical physics. In the implementation of EO for unit commitment (UC) problem, a novel problem-specific mutation operator is introduced and rule-based heuristic constraint-repairing techniques are devised. Simulation results on power systems which are composed of up to 100-units over a scheduling horizon of 24-hours demonstrate competitive performance with EO method compared with other existing methods for UC problem.
31 Dec 2012
TL;DR: A method is proposed for Classification using Ant Colony Optimizer as an Adaptive Classifier, which may give more efficient and effective method for classification in the adaptive environment.
Abstract: The widespread popularity of Optimization Algorithm in many fields such as Optimization, Pattern Recognition, Feature Extraction, Feature Selection etc. is mainly due to their ability to solve optimization problems in path planning. Out of many kinds of optimization algorithms, Ant Colony Optimization Algorithm is one of the most popular optimization algorithms. Many algorithms that dynamically construct solution to Ant Colony Optimization have increased in recent years. Several ant colony optimization algorithms present a promising performance on combinatorial optimization problem. Among them, Max Min Ant System performs comparatively better for Travelling Salesman Problem as compared to Ant System and Ant Colony System. A method is proposed for Classification using Ant Colony Optimizer as an Adaptive Classifier. The Classifier using ACO may give more efficient and effective method for classification in the adaptive environment.