A sophisticated PSO based on multi-level adaptation and purposeful detection

Xia, Xuewen; Wang, Bojian; Xie, Chengwang; Hu, Zhongbo; Wei, Bo; Jin, Chang

doi:10.1007/s00500-017-2514-x

Cited by 8 publications

(1 citation statement)

References 33 publications

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“…Nevertheless, this approach has the well-known drawback of tedious parameter tuning processes that involved extensive trial and error efforts. Most often, the optimal parameter settings obtained might only applicable for specific problems and this limitation restricts the practicability of parameter adaptation in real-world applications [38]. Recently, there are emerging trends of developing optimization frameworks with multiple learning strategies to address the imbalance issues of exploration and exploitation searches in PSO.…”

Section: Introductionmentioning

confidence: 99%

Modified Particle Swarm Optimization With Effective Guides

2020

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Despite of its simplicity, the conventional learning strategy of canonical particle swarm optimization (PSO) is inefficient to handle complex optimization problems due to its tendency of overemphasizing the fitness information of global best position without considering the diversity information of swarm. In this paper, a modified particle swarm optimization with effective guides (MPSOEG) is proposed, aiming to improve the algorithm's search performances in handling the optimization problems with different characteristics. Depending on the search performance of algorithm, two types of exemplars can be generated by an optimal guide creation (OGC) module incorporated into MPSOEG by referring to the particles with valuable directional information. Particularly, a global exemplar is generated by OCG module to guide the swarm converging towards the promising solution regions of search space, whereas a unique local exemplar can be customized for each particle to enable it escaping from local or non-optimal solution regions. In contrary to global best particle, the exemplars generated by OGC module are able to guide all MPSOEG particles more effectively by considering both fitness and diversity information of swarm, hence can achieve better balancing of algorithm's exploration and exploitation searches. Another notable contribution of MPSOEG is the simplicity of its learning framework through the elimination of both inertia weight and acceleration coefficients parameters. Comprehensive simulation studies are conducted with 25 benchmark functions and the proposed MPSOEG is reported to outperform its six peer algorithms in terms of search accuracy, search reliability and search efficiency in most tested problems.

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Section: Introductionmentioning

confidence: 99%