Chaotic Moth Swarm Algorithm

Moth Swarm Algorithm (MSA) is one of the newest developed nature-inspired heuristics for optimization problem. Nevertheless MSA has a drawback which is slow convergence. Chaos is incorporated into MSA to eliminate this drawback. In this paper, ten chaotic maps have been embedded into MSA to find the...

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Bibliographic Details
Published in2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) pp. 90 - 95
Main Authors Guvenc, Ugur, Duman, Serhat, Hinislioglu, Yunus
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2017
Subjects
Benchmark testing
Chaos
chaotic maps
Convergence
Light sources
moth swarm algorithm
Optimization
Sociology
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Summary:Moth Swarm Algorithm (MSA) is one of the newest developed nature-inspired heuristics for optimization problem. Nevertheless MSA has a drawback which is slow convergence. Chaos is incorporated into MSA to eliminate this drawback. In this paper, ten chaotic maps have been embedded into MSA to find the best numbers of prospectors for increase the exploitation of the best promising solutions. The proposed method is applied to solve the well-known seven benchmark test functions. Simulation results show that chaotic maps can improve the performance of the original MSA in terms of the convergence speed. At the same time, sinusoidal map is the best map for improving the performance of MSA significantly.
DOI:10.1109/INISTA.2017.8001138