Regional-modal optimization problems and corresponding normal search particle swarm optimization algorithm

Yili Yuan

doi:10.1016/j.swevo.2023.101257

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Regional-modal optimization problems and corresponding normal search particle swarm optimization algorithm

2023-04-01

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DOI：10.1016/j.swevo.2023.101257

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Abstract

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Moavattd by me limit state auve kLSC) finding problem in reliability analysis. a new categmy of optinuyation problems refened to as the regional-modal optimization problems (RMOPs) was investigated in this paper. The most distinguishing feature of RMOPs is the continuity of its solutions. E-dsting optimization methods are not capable of capturing this feature and thus cannot produce acceptable results for RMOPs. Therefore, based on niching PSO a normal search particle swarm optimization (NSPSO) algorithm that can provide discrete optimal solutions for a continuous optimal region with arbitrary pie-specified density was developed. NSPSO is consisting of a normal search pattern and a multi-strategy fusion. Nomial searching is the core of NSPSO, in which each particle is guided by the normal vector of the region composed of its several best neighborhoods. Normal searching prevents the panicles from clustering and thus provide a basis for the solution diversity. Further, a multi strategy framework with three components was introduced to improve the performance of NSPSO. It inchides a dynamic particle repulsion strategy that improve the solution diversity, a panicle memory strategy to prevent local optimum, and an elite breeding strategy that was developed to increase the efficiency of the method. This framework gives NSPSO the ability to maintain the balance between exploitation and e ploration and thereby to realize the uniform disnibution and high coverage and diversity of the algorithm. Furthermore, the key parameters involved in NSPSO were analyzed thoroughly. The NSPSO is compared with ten state-of-theart multi-mode optimization algorithms in terms of twenty typical test functions with different properties that were constructed in this study. The experimental results demonstrate the superiority of our proposed algorithm over the state-of-the-art algorithms in solving RMOPs.

Keywords:

Regional-modal optimization problems

Particle swarm optimization algorithm

Normal search

Dynamic particle repulsion

Particle memory

Density-based elite breeding