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 new hybrid approach is proposed, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal, where significant improvements regarding forecasting accuracy are attainable.
153 citations
TL;DR: In this article, a chaotic particle swarm optimization (CPSO) approach was proposed to generate the optimal or near-optimal assembly sequences of complex products under the same assembly process constraints.
Abstract: Assembly sequence planning of complex products is difficult to be tackled, because the size of the search space of assembly sequences is exponentially proportional to the number of parts or components of the products Contrasted with the conventional methods, the intelligent optimization algorithms display their predominance in escaping from the vexatious trap This paper proposes a chaotic particle swarm optimization (CPSO) approach to generate the optimal or near-optimal assembly sequences of products Six kinds of assembly process constraints affecting the assembly cost are concerned and clarified at first Then, the optimization model of assembly sequences is presented The mapping rules between the optimization model and the traditional PSO model are given The variable velocity in the traditional PSO algorithm is changed to the velocity operator (vo) which is used to rearrange the parts in the assembly sequences to generate the optimal or near-optimal assembly sequences To improve the quality of the optimal assembly sequence and increase the convergence rate of the traditional PSO algorithm, the chaos method is proposed to provide the preferable assembly sequences of each particle in the current optimization time step Then, the preferable assembly sequences are considered as the seeds to generate the optimal or near-optimal assembly sequences utilizing the traditional PSO algorithm The proposed method is validated with an illustrative example and the results are compared with those obtained using the traditional PSO algorithm under the same assembly process constraints
152 citations
TL;DR: The experimental analysis showed that the proposed GA with a new multi-parent crossover converges quickly to the optimal solution and thus exhibits a superior performance in comparison to other algorithms that also solved those problems.
152 citations
TL;DR: The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature and guidelines for determining parameter values are given.
Abstract: The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature The effects of the major parameters on CFM were systematically investigated based on some benchmark functions The constriction factor, velocity constraint, and population size all have significant impact on the performance of CFM for PSO The constriction factor and velocity constraint have optimal values in practical application, and improper choice of these factors will lead to bad results Increasing population size can improve the solution quality, although the computing time will be longer The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper
152 citations
TL;DR: Two advanced optimization algorithms known as particle swarm optimization (PSO) and simulated annealing (SA) are presented to find the optimal combination of design parameters for minimum weight of a spur gear train.
152 citations