Shuffled Mutation Glowworm Swarm Optimization and Its Application

WANG Hongbo; REN Xuena; TU Xuyan

doi:10.1049/cje.2019.05.009

Volume 28 Issue 4

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WANG Hongbo, REN Xuena, TU Xuyan, “Shuffled Mutation Glowworm Swarm Optimization and Its Application,” Chinese Journal of Electronics, vol. 28, no. 4, pp. 822-828, 2019, doi: 10.1049/cje.2019.05.009

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WANG Hongbo, REN Xuena, TU Xuyan, “Shuffled Mutation Glowworm Swarm Optimization and Its Application,” Chinese Journal of Electronics, vol. 28, no. 4, pp. 822-828, 2019, doi: 10.1049/cje.2019.05.009

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Shuffled Mutation Glowworm Swarm Optimization and Its Application

doi: 10.1049/cje.2019.05.009

1.
School of Computer and Communication Engineering;Beijing Key Lab of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, Beijing 100083, China;
2.
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61572074) and China Scholarship Council for visiting to UK (No.201706465028).

Received Date: 2016-11-15
Rev Recd Date: 2018-06-23
Publish Date: 2019-07-10

Abstract

Abstract

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.
- Orthogonal strategy,
- Shuffled frog leaping,
- Wireless sensor centroid location

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References(20)

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