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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04 Jun 2001
TL;DR: It is shown that the combined algorithm out performs both of the component algorithms under most conditions, in both absolute and computational load weighted terms.
Abstract: An algorithm that is a combination of the particle swarm and differential evolution algorithms is introduced. The results of testing this on a graduated set of trial problems is given. It is shown that the combined algorithm out performs both of the component algorithms under most conditions, in both absolute and computational load weighted terms.
124 citations
TL;DR: Proposing equations analogous to those of the classical PSO equations, a discrete PSO algorithm (DPSO) is presented to minimize makespan (C"m"a"x) criterion and results signify that the proposed DPSO algorithm is very competitive and can be rapidly guided when hybridizing with a local search heuristic.
124 citations
TL;DR: The experimental results show that, for almost all functions, the proposed chaotic dynamic weight particle swarm optimization technique has superior performance compared with other nature-inspired optimizations and well-known PSO variants.
Abstract: Particle swarm optimization (PSO), which is inspired by social behaviors of individuals in bird swarms, is a nature-inspired and global optimization algorithm. The PSO method is easy to implement and has shown good performance for many real-world optimization tasks. However, PSO has problems with premature convergence and easy trapping into local optimum solutions. In order to overcome these deficiencies, a chaotic dynamic weight particle swarm optimization (CDW-PSO) is proposed. In the CDW-PSO algorithm, a chaotic map and dynamic weight are introduced to modify the search process. The dynamic weight is defined as a function of the fitness. The search accuracy and performance of the CDW-PSO algorithm are verified on seventeen well-known classical benchmark functions. The experimental results show that, for almost all functions, the CDW-PSO technique has superior performance compared with other nature-inspired optimizations and well-known PSO variants. Namely, the proposed algorithm of CDW-PSO has better search performance.
124 citations
TL;DR: The flexibility and ease of implementation of the CSO algorithm is evident from this analysis, showing the algorithm's usefulness in electromagnetic optimization problems.
Abstract: Antenna arrays with high directivity and low side lobe levels need to be designed for increasing the efficiency of communication systems. A new evolutionary technique, cat swarm optimization (CSO), is proposed for the synthesis of linear antenna arrays. The CSO is a high performance computational method capable of solving linear and non-linear optimization problems. CSO is applied to optimize the antenna element positions for suppressing side lobe levels and for achieving nulls in desired directions. The steps involved in the problem formulation of the CSO are presented. Various design examples are considered and the obtained CSO based results are validated by comparing with the results obtained using particle swarm optimization (PSO) and ant colony optimization (ACO). The flexibility and ease of implementation of the CSO algorithm is evident from this analysis, showing the algorithm's usefulness in electromagnetic optimization problems.
124 citations
TL;DR: In this article, particle swarm optimization is employed to evaluate the parametric regions where different dynamic phenomena (periodic oscillations, double-period oscillations and chaos) can be expected in dynamic models.
124 citations