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Piecewise combinatorial particle swarm optimization
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AI
DOI：10.1016/j.asoc.2024.112060.png)
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Abstract
En 中文
The particle swarm optimization (PSO) is easy to fall into local optima in the search process, and usually the optimum stops updating at the later stage of the iteration, especially for complex problems. In this paper, a novel piecewise combinatorial particle swarm optimization is proposed, where a new piecewise combinatorial strategy and a new exploitation space search strategy are designed. The piecewise combinatorial strategy increases population diversity through copying, crossover, and mutation operations. The exploitation space search strategy improves particles search speed by setting the exploitation particle. This algorithm is tested and compared with 11 popular PSO variants on 30 benchmark functions. The results show that the performance of this algorithm is better than many existing PSO variants, especially for complex optimization problems. In addition, the piecewise combinatorial strategy provides a new idea for integrating excellent algorithms with different focuses. In the end, the algorithm is applied to a traveling salesman problem, and it is found that the algorithm exhibits excellent performance.
Keywords:
Particle swarm optimization
Piecewise combinatorial strategy
Exploitation space search strategy