An Improved Glowworm Swarm Optimization Algorithm
2018 International Conference on Machine Learning and Cybernetics (ICMLC)(2018)
摘要
In this paper a self-adaptive weight glowworm swarm optimization (SWGSO) algorithm has been proposed for solving the shortcomings of basic glowworm swarm optimization algorithm. This paper improves the self-adaptive step size mechanism, changes the direction of transfer, and randomly perturbs the non-moving glowworms. Experiments were performed using three benchmark functions. The results show that the weighted moving glowworm algorithm attains faster convergence speed and higher accuracy.
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关键词
Glowworm swarm optimization,Self-adaptive weight,Self-adaptive step size,Solution accuracy
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