Abstract
Particle swarm optimizer was proposed in 1995, and since then, it has become an extremely popular swarm intelligent algorithm with widespread applications. Many modified versions of it have been developed, in which, comprehensive learning particle swarm optimizer is a very powerful one. In order to enhance its performance further, a local search based on Latin hypercube sampling is combined with it in this work. Due to that a hypercube should become smaller and smaller for better local search ability during the search process, a control method is designed to set the size of the hypercube. Via numerical experiments, it can be observed that the comprehensive learning particle swarm optimizer with the local search based on Latin hypercube sampling has a strong ability on both global and local search. The hybrid algorithm is applied in cylindricity error evaluation problem and it outperforms several other algorithms.
Highlights
Global optimization problems widely exist in engineering application and scientific research
In order to enhance its performance further, a local search based on Latin hypercube sampling is combined with it in this work
A local search based on Latin hypercube sampling is combined with comprehensive learning particle swarm optimizer (CLPSO)
Summary
Global optimization problems widely exist in engineering application and scientific research. Wu et al [27] proposed a superior solution guided particle swarm optimization (SSG-PSO) combined with four gradient-based or derivative-free local search methods. Latin hypercube sampling can be available in CLPSO In this work, it is used as a local search method for the global best particle in CLPSO, and this derived method is been called as CLPSO-LHS. Many metaheuristic search methods have been applied in this problem, such as improved Genetic algorithms (GA) [32], particle swarm optimization (PSO) [33], hybrid particle swarm optimization-differential evolution algorithm (PSO-DE) [31], and hierarchical PSO with Latin sampling based memetic algorithm (MA-HPSOL) [22]. Can be the corresponding dimension of any particle’s pbest, including its own pbest
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