A randomized nonmonotone adaptive trust region method based on the simulated annealing strategy for unconstrained optimization

Babaie-Kafaki, Saman; Rezaee, Saeed

doi:10.1108/ijicc-12-2018-0178

Cited by 1 publication

(1 citation statement)

References 45 publications

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“…In particular, these complicated optimization problems usually are non-differentiable, discontinuous, non-convex, non-linear, or multimodal [10][11][12]. Confronted with such kinds of complicated optimization problems, the optimization effectiveness of traditional optimization methods, such as conjugate gradient methods [13,14], space-filling curve methods [15,16], quasi-Newton methods [17,18], line search methods [19][20][21], and trust-region methods [22,23], deteriorates rapidly. In extreme cases, they are even infeasible for solving these complex problems.…”

Section: Introductionmentioning

confidence: 99%

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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