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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TL;DR: A novel multi-swarm particle swarm optimization with dynamic learning strategy (PSO-DLS) to improve the performance of PSO and demonstrate its promising effectiveness in solving complex problems statistically comparing to other algorithms.
106 citations
TL;DR: A hybrid intelligent algorithm by combining the particle swarm optimization (PSO) with chaos searching technique (CST) is presented for solving nonlinear bilevel programming problems.
Abstract: In this paper, a hybrid intelligent algorithm by combining the particle swarm optimization (PSO) with chaos searching technique (CST) is presented for solving nonlinear bilevel programming problems. The bilevel programming is transformed into a single level programming problem by use of the KKT conditions of the lower level problem. Then, the hybrid intelligent algorithm is proposed to solve the transformed problem. Our approach embeds the CST into PSO. Firstly, the algorithm is initialized by a set of random particles which travel through the search space. Secondly, an optimization problem is solved by CST to judge whether the particle is feasible or not. In each iteration, all the feasible particles are ranked in ascending order. Particles in the front of list are updated by PSO, while particles in the end of list are updated by CST. The CST used here is not only to enhance the particles but also to improve the diversity of the particle swarm so as to avoid PSO trapping the local optima. Finally, the hybrid intelligent algorithm is commented by illustrating the numerical results on several benchmark problems from the references.
106 citations
TL;DR: An improved vector particle swarm optimization (IVPSO) algorithm is proposed to solve COPs, based on the simple constraint-preserving method, and the performance of IVPSO is tested on 13 well-known benchmark functions.
106 citations
TL;DR: In this paper, the authors proposed a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER.
106 citations
01 Jan 1997
TL;DR: A non-generational genetic algorithm for multiobjective optimization that clearly outperforms previous eeorts based on generational genetic algorithms on three multi-Objective optimization problems of growing dii-culty.
Abstract: This paper describes a non-generational genetic algorithm for multiobjective optimization. The tness of each individual in the population is calculated incrementally based on the degree in which it is dominated in the Pareto sense, or close to other individuals. The closeness of individuals is measured using a sharing function. The performance of the algorithm presented is compared to previous eeorts on three multiobjec-tive optimization problems of growing dii-culty. The behavior of each algorithm is analyzed with regard to the visited search space, the quality of the nal population attained, and the percentage of non-dominated individuals in the population through time. According to all these performance measures, the algorithm presented clearly outperforms previous eeorts based on generational genetic algorithms.
106 citations