Probabilistic Multimodal Optimization

Yang, Qiang; Chen, Wei-Neng; Zhang, Jun

doi:10.1007/978-3-030-79553-5_9

Cited by 5 publications

(2 citation statements)

References 64 publications

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“…r 1 and r 2 are uniformly and randomly sampled within [0, 1]. Equation (2) shows that in the canonical PSO, all particles learn from the global best position gbest of the whole swarm found so far. Such an exemplar is too greedy and thus easily leads to falling into local basins of the swarm when coping with optimization problems with many local regions [49,74,75].…”

Section: Canonical Psomentioning

confidence: 99%

“…Optimization problems emerge commonly and become more and more complicated in many research fields and industrial engineering [1,2], such as object detection and tracking [3,4], automatic design of algorithms for visual attention [5,6], path planning optimization [7,8], and control of pollutant spreading on social networks [9]. In particular, these complicated optimization problems usually are non-differentiable, discontinuous, non-convex, non-linear, or multimodal [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Predominant Cognitive Learning Particle Swarm Optimization for Global Numerical Optimization

Yang

Zhang

2022

Mathematics

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Particle swarm optimization (PSO) has witnessed giant success in problem optimization. Nevertheless, its optimization performance seriously degrades when coping with optimization problems with a lot of local optima. To alleviate this issue, this paper designs a predominant cognitive learning particle swarm optimization (PCLPSO) method to effectively tackle complicated optimization problems. Specifically, for each particle, a new promising exemplar is constructed by letting its personal best position cognitively learn from a better personal experience randomly selected from those of others based on a novel predominant cognitive learning strategy. As a result, different particles preserve different guiding exemplars. In this way, the learning effectiveness and the learning diversity of particles are expectedly improved. To eliminate the dilemma that PCLPSO is sensitive to the involved parameters, we propose dynamic adjustment strategies, so that different particles preserve different parameter settings, which is further beneficial to promote the learning diversity of particles. With the above techniques, the proposed PCLPSO could expectedly compromise the search intensification and diversification in a good way to search the complex solution space properly to achieve satisfactory performance. Comprehensive experiments are conducted on the commonly adopted CEC 2017 benchmark function set to testify the effectiveness of the devised PCLPSO. Experimental results show that PCLPSO obtains considerably competitive or even much more promising performance than several representative and state-of-the-art peer methods.

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