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Showing papers on "Swarm intelligence published in 2016"


Journal ArticleDOI
TL;DR: The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature.
Abstract: A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at http://www.alimirjalili.com/DA.html.

1,897 citations


Journal ArticleDOI
TL;DR: This review identifies the popularly used algorithms within the domain of bio-inspired algorithms and discusses their principles, developments and scope of application, which would pave the path for future studies to choose algorithms based on fitment.
Abstract: Review of applications of algorithms in bio-inspired computing.Brief description of algorithms without mathematical notations.Brief description of scope of applications of the algorithms.Identification of algorithms whose applications may be explored.Identification of algorithms on which theory development may be explored. With the explosion of data generation, getting optimal solutions to data driven problems is increasingly becoming a challenge, if not impossible. It is increasingly being recognised that applications of intelligent bio-inspired algorithms are necessary for addressing highly complex problems to provide working solutions in time, especially with dynamic problem definitions, fluctuations in constraints, incomplete or imperfect information and limited computation capacity. More and more such intelligent algorithms are thus being explored for solving different complex problems. While some studies are exploring the application of these algorithms in a novel context, other studies are incrementally improving the algorithm itself. However, the fast growth in the domain makes researchers unaware of the progresses across different approaches and hence awareness across algorithms is increasingly reducing, due to which the literature on bio-inspired computing is skewed towards few algorithms only (like neural networks, genetic algorithms, particle swarm and ant colony optimization). To address this concern, we identify the popularly used algorithms within the domain of bio-inspired algorithms and discuss their principles, developments and scope of application. Specifically, we have discussed the neural networks, genetic algorithm, particle swarm, ant colony optimization, artificial bee colony, bacterial foraging, cuckoo search, firefly, leaping frog, bat algorithm, flower pollination and artificial plant optimization algorithm. Further objectives which could be addressed by these twelve algorithms have also be identified and discussed. This review would pave the path for future studies to choose algorithms based on fitment. We have also identified other bio-inspired algorithms, where there are a lot of scope in theory development and applications, due to the absence of significant literature.

397 citations


Journal ArticleDOI
TL;DR: The main focus is on studies characterized by distributed control, simplicity of individual robots and locality of sensing and communication, and distributed algorithms are shown to bring cooperation between agents.

337 citations


Journal ArticleDOI
TL;DR: A new bio-inspired algorithm, namely Bird Swarm Algorithm (BSA), is proposed for solving optimisation applications based on the swarm intelligence extracted from the social behaviours and social interactions in bird swarms.
Abstract: A new bio-inspired algorithm, namely Bird Swarm Algorithm (BSA), is proposed for solving optimisation applications. BSA is based on the swarm intelligence extracted from the social behaviours and social interactions in bird swarms. Birds mainly have three kinds of behaviours: foraging behaviour, vigilance behaviour and flight behaviour. Birds may forage for food and escape from the predators by the social interactions to obtain a high chance of survival. By modelling these social behaviours, social interactions and the related swarm intelligence, four search strategies associated with five simplified rules are formulated in BSA. Simulations and comparisons based on eighteen benchmark problems demonstrate the effectiveness, superiority and stability of BSA. Some proposals for future research about BSA are also discussed.

330 citations


Journal ArticleDOI
TL;DR: A novel lightweight feature selection is proposed designed particularly for mining streaming data on the fly, by using accelerated particle swarm optimization (APSO) type of swarm search that achieves enhanced analytical accuracy within reasonable processing time.
Abstract: Big Data though it is a hype up-springing many technical challenges that confront both academic research communities and commercial IT deployment, the root sources of Big Data are founded on data streams and the curse of dimensionality. It is generally known that data which are sourced from data streams accumulate continuously making traditional batch-based model induction algorithms infeasible for real-time data mining. Feature selection has been popularly used to lighten the processing load in inducing a data mining model. However, when it comes to mining over high dimensional data the search space from which an optimal feature subset is derived grows exponentially in size, leading to an intractable demand in computation. In order to tackle this problem which is mainly based on the high-dimensionality and streaming format of data feeds in Big Data, a novel lightweight feature selection is proposed. The feature selection is designed particularly for mining streaming data on the fly, by using accelerated particle swarm optimization (APSO) type of swarm search that achieves enhanced analytical accuracy within reasonable processing time. In this paper, a collection of Big Data with exceptionally large degree of dimensionality are put under test of our new feature selection algorithm for performance evaluation.

202 citations


01 Jan 2016
TL;DR: Swarm intelligence from natural to artificial systems, where people have search hundreds of times for their chosen books, but end up in malicious downloads instead of reading a good book with a cup of coffee in the afternoon.
Abstract: Thank you very much for reading swarm intelligence from natural to artificial systems. As you may know, people have search hundreds times for their chosen books like this swarm intelligence from natural to artificial systems, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some infectious bugs inside their computer.

189 citations


Journal ArticleDOI
TL;DR: This review of seminal works that addressed the problem of target search and tracking in the area of swarm robotics, which is the application of swarm intelligence principles to the control of multi-robot systems, finds variations of the search andtracking problem addressed in the literature.

157 citations


Journal ArticleDOI
TL;DR: In this paper, the history development, and the state-of-the-art of the BSO algorithm are reviewed and the convergent operation and divergent operation in the BSA algorithm are discussed from the data analysis perspective.
Abstract: For swarm intelligence algorithms, each individual in the swarm represents a solution in the search space, and it also can be seen as a data sample from the search space. Based on the analyses of these data, more effective algorithms and search strategies could be proposed. Brain storm optimization (BSO) algorithm is a new and promising swarm intelligence algorithm, which simulates the human brainstorming process. Through the convergent operation and divergent operation, individuals in BSO are grouped and diverged in the search space/objective space. In this paper, the history development, and the state-of-the-art of the BSO algorithm are reviewed. In addition, the convergent operation and divergent operation in the BSO algorithm are also discussed from the data analysis perspective. Every individual in the BSO algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscape of the problem. Swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.

146 citations


Journal ArticleDOI
TL;DR: A novel hybrid Krill herd and quantum-behaved particle swarm optimization, called KH–QPSO, is presented for benchmark and engineering optimization and can easily infer that it is more efficient than other optimization methods for solving standard test problems andengineering optimization problems.
Abstract: A novel hybrid Krill herd (KH) and quantum-behaved particle swarm optimization (QPSO), called KH---QPSO, is presented for benchmark and engineering optimization QPSO is intended for enhancing the ability of the local search and increasing the individual diversity in the population KH---QPSO is capable of avoiding the premature convergence and eventually finding the function minimum; especially, KH---QPSO can make all the individuals proceed to the true global optimum without introducing additional operators to the basic KH and QPSO algorithms To verify its performance, various experiments are carried out on an array of test problems as well as an engineering case Based on the results, we can easily infer that the hybrid KH---QPSO is more efficient than other optimization methods for solving standard test problems and engineering optimization problems

142 citations


Journal ArticleDOI
TL;DR: The most important optimization algorithms based on nonlinear physics, how they have been constructed from specific modeling of a real phenomena, and also their novelty in terms of comparison with alternative existing algorithms for optimization are reviewed.

125 citations


Journal ArticleDOI
01 Apr 2016
TL;DR: A new ABC variant named ABC with memory algorithm (ABCM) is described, which imitates a memory mechanism to the artificial bees to memorize their previous successful experiences of foraging behavior and shows the superiority of ABCM on a set of benchmark problems.
Abstract: Graphical abstractDisplay Omitted HighlightsArtificial bee colony with memory algorithm (ABCM) is proposed.ABCM introduces the memory ability of natural honeybees to ABC.ABCM is designed as simply as possible for easy implementation.Experiments on the benchmark functions show the superiority of ABCM.It bridges the gap between ABC and the neuroscience research of real honeybees. Artificial bee colony algorithm (ABC) is a new type of swarm intelligence methods which imitates the foraging behavior of honeybees. Due to its simple implementation with very small number of control parameters, many efforts have been done to explore ABC research in both algorithms and applications. In this paper, a new ABC variant named ABC with memory algorithm (ABCM) is described, which imitates a memory mechanism to the artificial bees to memorize their previous successful experiences of foraging behavior. The memory mechanism is applied to guide the further foraging of the artificial bees. Essentially, ABCM is inspired by the biological study of natural honeybees, rather than most of the other ABC variants that integrate existing algorithms into ABC framework. The superiority of ABCM is analyzed on a set of benchmark problems in comparison with ABC, quick ABC and several state-of-the-art algorithms.

Journal ArticleDOI
TL;DR: It is found that the proposed surrogate model combined with MCS can achieve accurate system failure probability evaluation using fewer deterministic slope stability analyzes than other approaches.

Journal ArticleDOI
21 Mar 2016-PLOS ONE
TL;DR: This paper demonstrates for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment and validates that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm.
Abstract: Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.

Journal ArticleDOI
TL;DR: In this article, a multi-objective optimal power flow (MO-OPF) problem has been formulated in which particle swarm optimization (PSO) and Glowworm Swarm Optimization (GSO) have been used to solve the OPF problem with generation cost and emission minimizations as objective functions.

Journal ArticleDOI
TL;DR: Optimum design problem of steel space frames is formulated according to the provisions of LRFD-AISC and its solution is obtained by using enhanced artificial bee colony algorithm by adding Levy flight distribution in the search of scout bees.

Journal ArticleDOI
TL;DR: This paper proposes a novel method for the cell planning problem for fourth-generation (4G) cellular networks using metaheuristic algorithms to satisfy both cell coverage and capacity constraints simultaneously by formulating an optimization problem that captures practical planning aspects.
Abstract: Base station (BS) deployment in cellular networks is one of the fundamental problems in network design. This paper proposes a novel method for the cell planning problem for fourth-generation (4G) cellular networks using metaheuristic algorithms. In this approach, we aim to satisfy both cell coverage and capacity constraints simultaneously by formulating an optimization problem that captures practical planning aspects. The starting point of the planning process is defined through a dimensioning exercise that captures both coverage and capacity constraints. Afterward, we implement a metaheuristic algorithm based on swarm intelligence (e.g., particle swarm optimization or the recently proposed gray-wolf optimizer) to find suboptimal BS locations that satisfy both problem constraints in the area of interest, which can be divided into several subareas with different spatial user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant BSs. We also perform Monte Carlo simulations to study the performance of the proposed scheme and compute the average number of users in outage. Next, the problems of green planning with regard to temporal traffic variation and planning with location constraints due to tight limits on electromagnetic radiations are addressed, using the proposed method. Finally, in our simulation results, we apply our proposed approach for different scenarios with different subareas and user distributions and show that the desired network quality-of-service (QoS) targets are always reached, even for large-scale problems.

Journal ArticleDOI
TL;DR: Final results show the present method can acquire the better identification results, compared with those from GA, the original ABC and quick ABC (QABC) algorithm, even under some measurement noise.
Abstract: An approach for structural damage detection using the artificial bee colony (ABC) algorithm with hybrid search strategy based on modal data is presented. More search strategies are offered and the bee will choose one search mode based on the tournament selection strategy. And a kind of elimination mechanism is introduced to improve the convergence rate. A truss and a plate are studied as two numerical examples to illustrate the efficiency of proposed method. An experimental work on a beam is studied for further verification. Final results show the present method can acquire the better identification results, compared with those from GA, the original ABC and quick ABC (QABC) algorithm, even under some measurement noise.

Journal ArticleDOI
TL;DR: The SMO is a new swarm intelligence technique that models the foraging behavior of spider monkeys that is used to synthesize the array factor of a linear antenna array and to optimally design an E-shaped patch antenna for wireless applications.
Abstract: The aim of this letter is to introduce and use the spider monkey optimization (SMO) as an optimization technique for the electromagnetics and antenna communities. The SMO is a new swarm intelligence technique that models the foraging behavior of spider monkeys. To show the efficiency of the SMO, different examples are presented, and the results are compared to the results obtained using other popular optimization techniques. The optimization procedure is used to synthesize the array factor of a linear antenna array and to optimally design an E-shaped patch antenna for wireless applications. By comparing to traditional optimization techniques reported in the literature, it is evident that SMO is efficient in reaching the optimum solutions with less number of experiments .

Journal ArticleDOI
TL;DR: A modified multi-objective artificial bee colony (MOABC) algorithm is introduced to solve the MDCSCN model, which integrates the priority-based encoding mechanism, the Pareto optimality and the swarm intelligence of the bee colony.
Abstract: A novel strategic model for designing the MDSCN is proposed.Multiple objectives of the MDSCN are designed for sustainable development.A novel MOABC algorithm is introduced with unique features.Both the model and the method are validated through experimentation. The emergence of Omni-channel has affected the practical design of the supply chain network (SCN) with the purpose of providing better products and services for customers. In contrast to the conventional SCN, a new strategic model for designing SCN with multiple distribution channels (MDCSCN) is introduced in this research. The MDCSCN model benefits customers by providing direct products and services from available facilities instead of the conventional flow of products and services. Sustainable objectives, i.e., reducing economic cost, enlarging customer coverage and weakening environmental influences, are involved in designing the MDCSN. A modified multi-objective artificial bee colony (MOABC) algorithm is introduced to solve the MDCSCN model, which integrates the priority-based encoding mechanism, the Pareto optimality and the swarm intelligence of the bee colony. The effect of the MDCSCN model are examined and validated through numerical experiment. The MDCSCN model is innovative and pioneering as it meets the latest requirements and outperforms the conventional SCN. More importantly, it builds the foundation for an intelligent customer order assignment system. The effectiveness and efficiency of the MOABC algorithm is evaluated in comparison with the other popular multi-objective meta-heuristic algorithm with promising results.

Journal ArticleDOI
TL;DR: Extensive experiments on 18 benchmark optimization functions of different types show that ANS has well balanced exploration and exploitation capabilities and performs competitively compared with many efficient PBSAs.

Journal ArticleDOI
TL;DR: The specific SLO (S-SLO) is proposed, constructed by integrating the improved differential evolutionary (DE) algorithm and improved social cognitive optimization (SCO) into the micro-space and the learning space, respectively, to solve the problem of QoS-aware cloud service composition.

Proceedings ArticleDOI
01 Oct 2016
TL;DR: This paper gives a detailed description of the PSO algorithm and significance of the various parameters involved in its update rule and highlights the advantages and disadvantages of usingPSO algorithm in any optimization problem.
Abstract: In the year 1995, Dr R.C. Eberhart, who was an electrical engineer, along with Dr. James Kennedy, a social psycologist invented a random optimization technique which a was later named as Particle Swarm Optimization. As the name itself asserts that this method draws inspiration from natural biotic life of swarms of flocks. It uses the same principle to find most optimal solution to problem in search space as birds do find their most suitable place in a flock or insects do in a swarm. The PSO algorithm is initialized with a horde of particles which are a collection of random feasible solutions. Every single particle in the swarm is initialised a random velocity and as soon as they are assigned a velocity these particles start moving in problem search space. Now from this space the algorithm draws the particle to most suited fitness which in turn pulls it to the location of best fitness achieved across the whole horde. The PSO update rule comprises of many distinguishing features which are adjusted and modified depending upon the area of application of algorithm. This paper gives a detailed description of the PSO algorithm and significance of the various parameters involved in its update rule. It also highlights the advantages and disadvantages of using PSO algorithm in any optimization problem.

Journal ArticleDOI
TL;DR: The aim of this Special Issue is to highlight the most significant recent developments on the topics of SI and to apply SI algorithms in real-life scenarios.
Abstract: Swarm intelligence (SI) is an artificial intelligence technique based on the study of cooperation behaviors of simple individuals (e.g. ant colonies, bird flocking, animal herding, and bees gathering honey) in various decentralized systems. The population, which consists of simple individuals, can usually solve complex tasks in nature by individuals interacting locally with one another and with their environment. Although a simple individual’s behavior is primarily characterized by autonomy, distributed functioning, and self-organizing capacities, local interactions among the individuals often lead to a global optimal. Therefore, SI is a promising way to develop powerful solution methods for complex optimization problems in mechanical engineering. Recently, SI algorithms have attracted much attention of researchers and have also been applied successfully to solving optimization problems in mechanical engineering. However, for large and complex problems, SI algorithms often consume too much computation time due to the stochastic features of their searching approaches. Thus, there is a potential requirement to develop efficient algorithms that are able to find solutions under limited resources, time and money in realworld applications. The aim of this Special Issue is to highlight the most significant recent developments on the topics of SI and to apply SI algorithms in real-life scenarios. Contributions containing new insights and findings in this field are welcome. Papers selected for this Special Issue present new findings and insights into this field. A broad range of topics are discussed, especially in the following areas: SI algorithms for scheduling of machinery production line, mechanical parameters adjustment based on SI, application of SI algorithms in mechanical fault diagnosis and SI and mechatronics. In the paper titled ‘‘Pareto optimal train scheduling for urban rail transit using generalized particle swarm optimization,’’ W. Chu et al. established a bi-objective optimization model to study the Pareto optimal urban rail train scheduling problem. The aim of the model was to minimize the passengers’ total travel time and the number of used train stocks at the same time. A Pareto-based particle swarm optimization procedure was designed to solve the model. Finally, two different scaled urban rail lines were applied to test the model and the algorithm. In the paper titled ‘‘Estimation of vessel collision risk index based on support vector machine,’’ L. Gang et al. proposed a collision risk index estimation model based on support vector machine and applied genetic algorithm to optimize the corresponding parameters. And the comparison between cross-validation-support vector machine, particle swarm optimization–support vector machine, and genetic algorithm–support vector machine models showed that genetic algorithm–support vector machine model generally provided a better performance for collision risk index estimation. In the paper titled ‘‘Automobile chain maintenance parts delivery problem using an improved ant colony algorithm,’’ J. Gao et al. solved the automobile chain maintenance parts delivery problem by transferring the multi-depot vehicle routing problem with time windows to multi-depot vehicle routing problem with the virtual central depot. Then an improved ant colony optimization with saving algorithms, mutation operation, and adaptive ant-weight strategy was proposed to solve the problem. And the computational results indicated that the proposed algorithm was effective to solve the problem. In the paper titled ‘‘Pareto front–based multiobjective real-time traffic signal control model for intersections using particle swarm optimization algorithm,’’ P. Jiao et al. proposed a Pareto front–based multi-objective traffic signal control model to obtain real-time signal parameters and evaluation indices. The objectives of the model were to minimize delay time, minimize number of stops, and maximize effective capacity. In addition, a step-by-step particle swarm optimization algorithm based on Pareto front was

Journal ArticleDOI
TL;DR: This work investigated the basic properties of PSO algorithms relevant to the implementation of swarm robotics and characterized the limitations that promote this trend to manifest, and validated the existence of premature convergence in these PSO variants.
Abstract: One of the most widely used biomimicry algorithms is the Particle Swarm Optimization (PSO). Since its introduction in 1995, it has caught the attention of both researchers and academicians as a way of solving various optimization problems, such as in the fields of engineering and medicine, to computer image processing and mission critical operations. PSO has been widely applied in the field of swarm robotics, however, the trend of creating a new variant PSO for each swarm robotic project is alarming. We investigate the basic properties of PSO algorithms relevant to the implementation of swarm robotics and characterize the limitations that promote this trend to manifest. Experiments were conducted to investigate the convergence properties of three PSO variants (original PSO, SPSO and APSO) and the global optimum and local optimal of these PSO algorithms were determined. We were able to validate the existence of premature convergence in these PSO variants by comparing 16 functions implemented alongside the PSO variant. This highlighted the fundamental flaws in most variant PSOs, and signifies the importance of developing a more generalized PSO algorithm to support the implementation of swarm robotics. This is critical in curbing the influx of custom PSO and theoretically addresses the fundamental flaws of the existing PSO algorithm.

Journal ArticleDOI
TL;DR: A new modified PSO is proposed by introducing a mutation mechanism and using dynamic algorithm parameters to preserve the diversity of the algorithm in the final searching stage of the evolution process.
Abstract: Particle swarm optimization (PSO) is a population-based stochastic search algorithm inspired from the natural behavior of bird flocking or fish schooling for searching their foods. Due to its easiness in numerical implantations, PSO has been widely applied to solve a variety of inverse and optimization problems. However, a PSO is often trapped into local optima while dealing with complex and real world design problems. In this regard, a new modified PSO is proposed by introducing a mutation mechanism and using dynamic algorithm parameters. According to the proposed mutation mechanism, one particle is randomly selected from all personal best ones to generate some trial particles to preserve the diversity of the algorithm in the final searching stage of the evolution process. Moreover, the random variations in the two learning factors will help the particles to reach an optimal solution. In addition, the dynamic variation in the inertia weight will facilitate the algorithm to keep a good balance between exploration and exploitation searches. The numerical experiments on different case studies have shown that the proposed PSO obtains the best results among the tested algorithms.

Journal ArticleDOI
Ying Tan1, Ke Ding1
TL;DR: This paper presents a comprehensive review of GPU-based parallel SIAs in accordance with a newly proposed taxonomy and novel criteria are proposed to evaluate and compare the parallel implementation and algorithm performance universally.
Abstract: Inspired by the collective behavior of natural swarm, swarm intelligence algorithms (SIAs) have been developed and widely used for solving optimization problems. When applied to complex problems, a large number of fitness function evaluations are needed to obtain an acceptable solution. To tackle this vital issue, graphical processing units (GPUs) have been used to accelerate the optimization procedure of SIAs. Thanks to their inherent parallelism, SIAs are very suitable for parallel implementation under the GPU platform which have achieved a great success in recent years. This paper presents a comprehensive review of GPU-based parallel SIAs in accordance with a newly proposed taxonomy. Critical concerns for the efficient parallel implementation of SIAs are also described in detail. Moreover, novel criteria are also proposed to evaluate and compare the parallel implementation and algorithm performance universally. The rationality and practicability of the proposed optimization methodology and criteria are verified by careful case study. Finally, our opinions and perspectives on the trends and prospects on the relatively new research domain are also presented for future development.

Journal ArticleDOI
TL;DR: Two reactive evolutionary algorithms, and four reactive, swarm intelligence-based algorithms (bat, hybrid bat, particle swarm optimization and cuckoo search), were used to tune the PID controller in a comparative study and showed that particle swarm optimize is the best option for such a task.

Journal ArticleDOI
TL;DR: A new variant of SMO algorithm termed as Ageist Spider Monkey Optimization (ASMO) algorithm which seems more practical in biological terms and works on the basis of age difference present in spider monkey population is proposed.
Abstract: Swarm Intelligence (SI) is quite popular in the field of numerical optimization and has enormous scope for research. A number of algorithms based on decentralized and self-organized swarm behavior of natural as well as artificial systems have been proposed and developed in last few years. Spider Monkey Optimization (SMO) algorithm, inspired by the intelligent behavior of spider monkeys, is one such recently proposed algorithm. The algorithm along with some of its variants has proved to be very successful and efficient. A spider monkey group consists of members from every age group. The agility and swiftness of the spider monkeys differ on the basis of their age groups. This paper proposes a new variant of SMO algorithm termed as Ageist Spider Monkey Optimization (ASMO) algorithm which seems more practical in biological terms and works on the basis of age difference present in spider monkey population. Experiments on different benchmark functions with different parameters and settings have been carried out and the variant with the best suited settings is proposed. This variant of SMO has enhanced the performance of its original version. Also, ASMO has performed better in comparison to some of the recent advanced algorithms.

Journal ArticleDOI
TL;DR: Based on the combination of SI and data mining techniques, the authors can have a better understanding of the big data analytics problems, and design more effective algorithms to solve real-world big data analytical problems.
Abstract: – The quality and quantity of data are vital for the effectiveness of problem solving. Nowadays, big data analytics, which require managing an immense amount of data rapidly, has attracted more and more attention. It is a new research area in the field of information processing techniques. It faces the big challenges and difficulties of a large amount of data, high dimensionality, and dynamical change of data. However, such issues might be addressed with the help from other research fields, e.g., swarm intelligence (SI), which is a collection of nature-inspired searching techniques. The paper aims to discuss these issues. , – In this paper, the potential application of SI in big data analytics is analyzed. The correspondence and association between big data analytics and SI techniques are discussed. As an example of the application of the SI algorithms in the big data processing, a commodity routing system in a port in China is introduced. Another example is the economic load dispatch problem in the planning of a modern power system. , – The characteristics of big data include volume, variety, velocity, veracity, and value. In the SI algorithms, these features can be, respectively, represented as large scale, high dimensions, dynamical, noise/surrogates, and fitness/objective problems, which have been effectively solved. , – In current research, the example problem of the port is formulated but not solved yet given the ongoing nature of the project. The example could be understood as advanced IT or data processing technology, however, its underlying mechanism could be the SI algorithms. This paper is the first step in the research to utilize the SI algorithm to a big data analytics problem. The future research will compare the performance of the method and fit it in a dynamic real system. , – Based on the combination of SI and data mining techniques, the authors can have a better understanding of the big data analytics problems, and design more effective algorithms to solve real-world big data analytical problems.

Journal ArticleDOI
TL;DR: Inspired by the promising performance of heuristic algorithms to solve combinatorial problems, an improved quantum ant colony algorithm (QACA) is proposed for exhaustive optimization of the evacuation path that people can evacuate from hazardous areas to safe areas.