Shuffled Mutation Glowworm Swarm Optimization and Its Application

If the glowworm individual has no memory during its movement, and the decision of next direction is limited to its current position. It is precisely these reasons mentioned above that make the basic Glowworm Swarm Optimization easy to trap into the local optimum. In order to solve the problem, this paper suggests a Shuffled mutation glowworm swarm optimization (SMGSO), which combines the thought of Shuffled Frog Leaping with Glowworm Swarm Optimization. Making use of a grouping idea of Shuffled Mutation, the glowworm swarm is divided into several subgroups. The location updating of each individual is not only influenced by the brightest node in neighbour scope, but also by the brightest one in their local subgroup, meanwhile the locations of those isolated nodes are updated by the difference mutation of the global optimum and local optimal. In group shuffling stage, an orthogonal strategy can guide the whole population to generate their offspring. The performance of this proposed approach is examined by well‐known 10 benchmark functions, and its obtained results are compared with what other variants hold. The experimental analysis show that the Shuffled mutation glowworm swarm optimization is effective and outperforms other variants in terms of solving multi‐modal function optimization problems, and the proposed approach can improve the positioning accuracy of the centroid localization.