Topic
Multi-swarm optimization
About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.
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25 Jun 2001
TL;DR: A weapon system consisting of a swarm of air vehicles whose mission is to search for, classify, attack, and perform battle damage assessment, is considered, and the periodic reapplication of the centralized optimization algorithm yields the benefit of cooperative feedback control.
Abstract: A weapon system consisting of a swarm of air vehicles whose mission is to search for, classify, attack, and perform battle damage assessment, is considered. It is assumed that the target field information is communicated to all the elements of the swarm as it becomes available. A network flow optimization problem is posed whose readily obtained solution yields the optimum resource allocation among the air vehicles in the swarm. Hence, the periodic reapplication of the centralized optimization algorithm yields the benefit of cooperative feedback control.
141 citations
TL;DR: This approach integrates the merits of both genetic algorithm (GA) and particle swarm optimization (PSO) and has two characteristic features, and is implemented as a neighborhood search engine to improve the solution quality.
Abstract: In this paper, we propose a hybrid multiobjective evolutionary algorithm combining two heuristic optimization techniques. Our approach integrates the merits of both genetic algorithm (GA) and particle swarm optimization (PSO), and has two characteristic features. Firstly, the algorithm is initialized by a set of random particles which is flown through the search space. In order to get approximate nondominated solutions PND, an evolution of this particle is performed. Secondly, the local search (LS) scheme is implemented as a neighborhood search engine to improve the solution quality, where it intends to explore the less-crowded area in the current archive to possibly obtain more nondominated solutions. Finally, various kinds of multiobjective (MO) benchmark problems including the set of benchmark functions provided for CEC09 have been reported to stress the importance of hybridization algorithms in generating Pareto optimal sets for multiobjective optimization problems.
141 citations
26 Aug 2004
TL;DR: An advanced PSO algorithm with the mutation operator is put forward, which can not only escape the attraction of the local minimum in the later convergence phase, but also maintain the characteristic of fast speed in the early phase.
Abstract: Path planning is one of the most important technologies in the navigation of the mobile robot, which should meet the optimization and real-time requests This paper presents a novel approach of path planning First the MAKLINK graph is built to describe the working space of the mobile robot; then the Dijkstra algorithm is used to obtain the shortest path from the start point to the goal point in the graph, finally the particle swarm optimization algorithm is adopted to get the optimal path Aiming at the shortcoming of the PSO algorithm, that is, easily plunging into the local minimum, this paper puts forward an advanced PSO algorithm with the mutation operator By adding a mutation operator to the algorithm, it can not only escape the attraction of the local minimum in the later convergence phase, but also maintain the characteristic of fast speed in the early phase The results of the simulation demonstrate the effectiveness of the proposed method, which can meet the real-time requests of the mobile robot's navigation
141 citations
08 Dec 2003
TL;DR: The comparative study indicates that the hybridization of PSO with a nonuniform mutation operator significantly improves its performance when dealing with multimodal functions.
Abstract: We present two hybrid particle swarm optimization (PSO) algorithms that incorporate a mutation operator similar to the one used with evolutionary algorithms. We study our hybridized PSO algorithm with two schemes called g/spl I.bar/best and l/spl I.bar/best, and we apply them to multimodal functions. The proposed approaches are validated using test functions taken from the specialized literature, and our results are compared with respect to those obtained by other highly competitive PSO algorithms. Our comparative study indicates that the hybridization of PSO with a nonuniform mutation operator significantly improves its performance when dealing with multimodal functions.
140 citations
TL;DR: The results reveal that design of antenna arrays using the PSO method provides considerable enhancements compared with the uniform array and the synthesis obtained from other optimization techniques.
Abstract: Linear and circular arrays are optimized using the particle swarm optimization (PSO) method. Also, arrays of isotropic and cylindrical dipole elements are considered. The parameters of isotropic arrays are elements excitation amplitude, excitation phase and locations, while for dipole array the optimized parameters are elements excitation amplitude, excitation phase, location, and length. PSO is a high-performance stochastic evolutionary algorithm used to solve N-dimensional problems. The method of PSO is used to determine a set of parameters of antenna elements that provide the goal radiation pattern. The efiectiveness of PSO for the design of antenna arrays is shown by means of numerical results. Comparison with other methods is made whenever possible. The results reveal that design of antenna arrays using the PSO method provides considerable enhancements compared with the uniform array and the synthesis obtained from other optimization techniques.
140 citations