Showing papers on "Swarm intelligence published in 2015"

A Competitive Swarm Optimizer for Large Scale Optimization

Abstract: In this paper, a novel competitive swarm optimizer (CSO) for large scale optimization is proposed. The algorithm is fundamentally inspired by the particle swarm optimization but is conceptually very different. In the proposed CSO, neither the personal best position of each particle nor the global best position (or neighborhood best positions) is involved in updating the particles. Instead, a pairwise competition mechanism is introduced, where the particle that loses the competition will update its position by learning from the winner. To understand the search behavior of the proposed CSO, a theoretical proof of convergence is provided, together with empirical analysis of its exploration and exploitation abilities showing that the proposed CSO achieves a good balance between exploration and exploitation. Despite its algorithmic simplicity, our empirical results demonstrate that the proposed CSO exhibits a better overall performance than five state-of-the-art metaheuristic algorithms on a set of widely used large scale optimization problems and is able to effectively solve problems of dimensionality up to 5000.

ABCluster: the artificial bee colony algorithm for cluster global optimization.

Abstract: Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb–Born–Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program “ABCluster” to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters.

A social spider algorithm for global optimization

Abstract: Graphical abstractDisplay Omitted HighlightsWe propose a new nature-inspired social-spider-based swarm intelligence algorithm.We introduce a new social animal foraging model into meta-heuristic design.We introduce the design of information loss to handle pre-mature convergence.We perform a series of benchmark simulations to demonstrate the performance.We investigate the impact of control parameters on optimization results. The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, we propose a novel social spider algorithm to solve global optimization problems. This algorithm is mainly based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys. Different from the previously proposed swarm intelligence algorithms, we introduce a new social animal foraging strategy model to solve optimization problems. In addition, we perform preliminary parameter sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values. The social spider algorithm is evaluated by a series of widely used benchmark functions, and our proposed algorithm has superior performance compared with other state-of-the-art metaheuristics.

A Social Spider Algorithm for Global Optimization

Abstract: The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, we propose a novel Social Spider Algorithm to solve global optimization problems. This algorithm is mainly based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys. Different from the previously proposed swarm intelligence algorithms, we introduce a new social animal foraging strategy model to solve optimization problems. In addition, we perform preliminary parameter sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values. The Social Spider Algorithm is evaluated by a series of widely-used benchmark functions, and our proposed algorithm has superior performance compared with other state-of-the-art metaheuristics.

A directed artificial bee colony algorithm

Abstract: A new method based on artificial bee colony (ABC) algorithm is proposed in this study.The improvement is based on direction information produced for artificial bees.Performance of the proposed method has been examined on numeric functions.The experimental results show that proposed approach is more effective than some classical variants of ABC algorithm. Artificial bee colony (ABC) algorithm has been introduced for solving numerical optimization problems, inspired collective behavior of honey bee colonies. ABC algorithm has three phases named as employed bee, onlooker bee and scout bee. In the model of ABC, only one design parameter of the optimization problem is updated by the artificial bees at the ABC phases by using interaction in the bees. This updating has caused the slow convergence to global or near global optimum for the algorithm. In order to accelerate convergence of the method, using a control parameter (modification rate-MR) has been proposed for ABC but this approach is based on updating more design parameters than one. In this study, we added directional information to ABC algorithms, instead of updating more design parameters than one. The performance of proposed approach was examined on well-known nine numerical benchmark functions and obtained results are compared with basic ABC and ABCs with MR. The experimental results show that the proposed approach is very effective method for solving numeric benchmark functions and successful in terms of solution quality, robustness and convergence to global optimum.

Springer Handbook of Computational Intelligence

Abstract: The Springer Handbook for Computational Intelligence is the first book covering the basics, the state-of-the-art and important applications of the dynamic and rapidly expanding discipline of computational intelligence. This comprehensive handbook makes readers familiar with a broad spectrum of approaches to solve various problems in science and technology. Possible approaches include, for example, those being inspired by biology, living organisms and animate systems. Content is organized in seven parts: foundations; fuzzy logic; rough sets; evolutionary computation; neural networks; swarm intelligence and hybrid computational intelligence systems. Each Part is supervised by its own Part Editor(s) so that high-quality content as well as completeness are assured.

Selectively-informed particle swarm optimization

Abstract: Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors.

Ship's Trajectory Planning for Collision Avoidance at Sea Based on Ant Colony Optimisation

Abstract: Swarm Intelligence (SI) constitutes a rapidly growing area of research. At the same time trajectory planning in a dynamic environment still constitutes a very challenging research problem. This paper presents a new approach to path planning in dynamic environments based on Ant Colony Optimisation (ACO). Assumptions, a concise description of the method developed and results of real navigational situations (case studies with comments) are included. The developed solution can be applied in decision support systems on board a ship or in an intelligent Obstacle Detection and Avoidance system, which constitutes a component of Unmanned Surface Vehicle (USV) Navigation, Guidance and Control systems.

Ps-abc

Abstract: A hybrid algorithm (PS-ABC) based on PSO and ABC is proposed.PS-ABC examines the aging degree of pbest to decide which type of search phase.Particle swarm optimization (PSO) serves as a local search phase.Onlooker and modified scout bee from the ABC serves as two global search phases.Our algorithm is effective in solving high-dimensional optimization problems. Particle swarm optimization (PSO) and artificial bee colony (ABC) are new optimization methods that have attracted increasing research interests because of its simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optimal because of its low global exploration efficiency; ABC algorithm has slower convergence speed in some cases because of the lack of powerful local exploitation capacity. In this paper, we propose a hybrid algorithm called PS-ABC, which combines the local search phase in PSO with two global search phases in ABC for the global optimum. In the iteration process, the algorithm examines the aging degree of pbest for each individual to decide which type of search phase (PSO phase, onlooker bee phase, and modified scout bee phase) to adopt. The proposed PS-ABC algorithm is validated on 13 high-dimensional benchmark functions from the IEEE-CEC 2014 competition problems, and it is compared with ABC, PSO, HPA, ABC-PS and OXDE algorithms. Results show that the PS-ABC algorithm is an efficient, fast converging and robust optimization method for solving high-dimensional optimization problems.

A survey on the applications of artificial bee colony in signal, image, and video processing

Abstract: Artificial bee colony (ABC) algorithm is a swarm intelligence algorithm, which simulates the foraging behavior of honeybees. It has been successfully applied to many optimization problems in different areas. Since 2009, ABC algorithm has been employed for various problems in signal, image, and video processing fields. This paper presents the problems ABC algorithm has been applied in these fields and describes how ABC algorithm was used in the approaches for solving these kinds of problems.

Finding rough set reducts with fish swarm algorithm

Abstract: Rough set theory is one of the effective methods to feature selection which can preserve the characteristics of the original features by deleting redundant information. The main idea of rough set approach to feature selection is to find a globally minimal reduct, the smallest set of features keeping important information of the original set of features. Rough set theory has been used as a dataset preprocessor with much success, but current approaches to feature selection are inadequate for finding a globally minimal reduct. In this paper, we propose a novel rough set based method to feature selection using fish swarm algorithm. The fish swarm algorithm is a new intelligent swarm modeling approach that consists primarily of searching, swarming, and following behaviors. It is attractive for feature selection since fish swarms can discover the best combination of features as they swim within the subset space. In our proposed algorithm, a minimal subset can be located and verified. To show the efficiency of our algorithm, we carry out numerical experiments based on some standard UCI datasets. The results demonstrate that our algorithm can provide an efficient tool for finding a minimal subset of the features without information loss.

Swarm Intelligence: Principles, Advances, and Applications

Abstract: Swarm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then: Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possible Discusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizers Depicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function design Details the similarities, differences, weaknesses, and strengths of each swarm optimization method Draws parallels between the operators and searching manners of the different algorithms Swarm Intelligence: Principles, Advances, and Applications presents a comprehensive treatment of modern swarm intelligence optimization methods, complete with illustrative examples and an extendable MATLAB package for feature selection in wrapper mode applied on different data sets with benchmarking using different evaluation criteria. The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.

Brain storm optimization algorithm in objective space

Abstract: Brain storm optimization algorithm is a newly proposed swarm intelligence algorithm, which has two main operations, i.e., convergent operation and divergent operation. In the original brain storm optimization algorithm, a clustering algorithm is utilized to cluster individuals into clusters as the convergent operation which is time consuming because of distance calculation during clustering. In this paper, a new convergent operation is proposed to be implemented in the 1-dimensional objective space instead of in the solution space. As a consequence, its computation time will depend on only the population size, not the problem dimension, therefore, a big computation time saving can be obtained which makes it have good scalability. Experimental results demonstrate the effectiveness and efficiency of the proposed brain storm optimization algorithm in objective space.

A hierarchic approach based on swarm intelligence to solve the traveling salesman problem

Abstract: The purpose of this paper is to present a new hierarchic method based on swarm intelligence algorithms for solving the well-known traveling salesman problem. The swarm intelligence algorithms implemented in this study are divided into 2 types: path construction-based and path improvement-based methods. The path construction-based method (ant colony optimization (ACO)) produces good solutions but takes more time to achieve a good solution, while the path improvement-based technique (artificial bee colony (ABC)) quickly produces results but does not achieve a good solution in a reasonable time. Therefore, a new hierarchic method, which consists of both ACO and ABC, is proposed to achieve a good solution in a reasonable time. ACO is used to provide a better initial solution for the ABC, which uses the path improvement technique in order to achieve an optimal or near optimal solution. Computational experiments are conducted on 10 instances of well-known data sets available in the literature. The results show that ACO-ABC produces better quality solutions than individual approaches of ACO and ABC with better central processing unit time.

The continuous artificial bee colony algorithm for binary optimization

Abstract: This paper introduces an ABC variant to solve binary optimization problems.The performance of the proposed method is investigated on well-known UFLPs.The proposed method is compared with the ABC variants and PSO variants.The experimental results show that the proposed algorithm is an alternative tool for binary optimization. Artificial bee colony (ABC) algorithm, one of the swarm intelligence algorithms, has been proposed for continuous optimization, inspired intelligent behaviors of real honey bee colony. For the optimization problems having binary structured solution space, the basic ABC algorithm should be modified because its basic version is proposed for solving continuous optimization problems. In this study, an adapted version of ABC, ABCbin for short, is proposed for binary optimization. In the proposed model for solving binary optimization problems, despite the fact that artificial agents in the algorithm works on the continuous solution space, the food source position obtained by the artificial agents is converted to binary values, before the objective function specific for the problem is evaluated. The accuracy and performance of the proposed approach have been examined on well-known 15 benchmark instances of uncapacitated facility location problem, and the results obtained by ABCbin are compared with the results of continuous particle swarm optimization (CPSO), binary particle swarm optimization (BPSO), improved binary particle swarm optimization (IBPSO), binary artificial bee colony algorithm (binABC) and discrete artificial bee colony algorithm (DisABC). The performance of ABCbin is also analyzed under the change of control parameter values. The experimental results and comparisons show that proposed ABCbin is an alternative and simple binary optimization tool in terms of solution quality and robustness.

Training neural networks with ant colony optimization algorithms for pattern classification

Abstract: Feed-forward neural networks are commonly used for pattern classification. The classification accuracy of feed-forward neural networks depends on the configuration selected and the training process. Once the architecture of the network is decided, training algorithms, usually gradient descent techniques, are used to determine the connection weights of the feed-forward neural network. However, gradient descent techniques often get trapped in local optima of the search landscape. To address this issue, an ant colony optimization (ACO) algorithm is applied to train feed-forward neural networks for pattern classification in this paper. In addition, the ACO training algorithm is hybridized with gradient descent training. Both standalone and hybrid ACO training algorithms are evaluated on several benchmark pattern classification problems, and compared with other swarm intelligence, evolutionary and traditional training algorithms. The experimental results show the efficiency of the proposed ACO training algorithms for feed-forward neural networks for pattern classification.

A cloud model based fruit fly optimization algorithm

Abstract: Fruit Fly Optimization Algorithm (FOA) is a new global optimization algorithm inspired by the foraging behavior of fruit fly swarm. However, similar to other swarm intelligence based algorithms, FOA also has its own disadvantages. To improve the convergence performance of FOA, a normal cloud model based FOA (CMFOA) is proposed in this paper. The randomness and fuzziness of the foraging behavior of fruit fly swarm in osphresis phase is described by the normal cloud model. Moreover, an adaptive parameter strategy for Entropy En in normal cloud model is adopted to improve the global search ability in the early stage and to improve the accuracy of solution in the last stage. 33 benchmark functions are used to test the effectiveness of the proposed method. Numerical results show that the proposed CMFOA can obtain better or competitive performance for most test functions compared with three improved FOAs in recent literatures and seven state-of-the-arts of intelligent optimization algorithm.

A Novel PSO Based Back Propagation Learning-MLP (PSO-BP-MLP) for Classification

Abstract: Particle swarm optimization (PSO) is a powerful globally accepted evolutionary swarm intelligence method for solving both linear and non-linear problems. In this paper, a PSO based evolutionary multilayer perceptron is proposed which is intended for classification task in data mining. The network is trained by using the back propagation algorithm. An extensive experimental analysis has been performed by comparing the performance of the proposed method with MLP, GA-MLP. Comparison result shows that, PSO-MLP gives promising results in majority of test case problems.