Abstract
Optimization problems become increasingly complicated in the era of big data and Internet of Things, which significantly challenges the effectiveness and efficiency of existing optimization methods. To effectively solve this kind of problems, this paper puts forward a stochastic cognitive dominance leading particle swarm optimization algorithm (SCDLPSO). Specifically, for each particle, two personal cognitive best positions are first randomly selected from those of all particles. Then, only when the cognitive best position of the particle is dominated by at least one of the two selected ones, this particle is updated by cognitively learning from the better personal positions; otherwise, this particle is not updated and directly enters the next generation. With this stochastic cognitive dominance leading mechanism, it is expected that the learning diversity and the learning efficiency of particles in the proposed optimizer could be promoted, and thus the optimizer is expected to explore and exploit the solution space properly. At last, extensive experiments are conducted on a widely acknowledged benchmark problem set with different dimension sizes to evaluate the effectiveness of the proposed SCDLPSO. Experimental results demonstrate that the devised optimizer achieves highly competitive or even much better performance than several state-of-the-art PSO variants.
Highlights
Optimization problems widely exist in daily life and real-world engineering, such as resource allocation optimization [1], path planning optimization [2,3], and robot task allocation [4]
Inspired by the competition mechanisms in human society that groups of randomly assembled individuals spontaneously engage in costly group competition [55], this paper proposes a stochastic cognitive dominance leading particle swarm optimization algorithm (SCDLPSO) to improve the learning effectiveness and efficiency of particles when tackling complicated optimization problems
After deep investigation of the comparison results for different types of optimization problems, we find that SCDLPSO performs much better than the compared algorithms on complicated problems, such as the multimodal problems, the hybrid problems and the composition problems
Summary
Optimization problems widely exist in daily life and real-world engineering, such as resource allocation optimization [1], path planning optimization [2,3], and robot task allocation [4]. In the earliest PSO [10,11], Eberhart and Kennedy utilized the global topology with full connections with all particles to choose the global best position in the whole swarm as one guiding exemplar to direct the update of particles Such a global topology leads to too greedy attraction, and the swarm usually falls into local areas when dealing with multimodal problems. Many remarkable constructive learning strategies [8,51–53] have been devised for PSO to tackle complicated optimization problems In this direction, the most representative method is the comprehensive learning PSO (CLPSO) [8], which constructs a guiding exemplar dimension by dimension for each particle based on the personal best positions of all particles. The classical PSO is very suitable for unimodal optimization problems, but unaccommodating for multimodal optimization problems [22,30,43]
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