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International Journal of Advanced Network, Monitoring and Controls
Xi'an Technological University
Subject: Computer Science, Software Engineering
eISSN: 2470-8038
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Seven-spot Ladybird Optimization Algorithm Based on Bionics Principle
Wei Feng-tao / Lu Feng-yi / Zheng Jian-ming
Keywords : bionics principle, Seven-spot Ladybird Optimization, region search pattern, feasibility analysis, function optimization
Citation Information : International Journal of Advanced Network, Monitoring and Controls. Volume 2, Issue 1, Pages 49-58, DOI: https://doi.org/10.1109/iccnea.2017.23
License : (CC BY-NC-ND 4.0)
Published Online: 07-April-2018
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ARTICLE
ABSTRACT
For solving the problems of modern intelligence algorithms such as slow convergence and low precision, a new algorithm based on bionics principle has been proposed which is inspired by the foraging behavior of seven-spot ladybirds in the nature. By analyzing the bionic principle of Seven-spot ladybird Optimization(SLO), we simulate the region search pattern of predation of seven-spot ladybirds ,combining fast extensive search with careful and slow intensive search of the ladybirds,meanwhile we use three kinds of evaluation information to evaluate the solutions one by one,then the exploration and local approximation in the SLO are balanced. Next,in this paper we analyse the SLO theoretically by mathematics, presenting its specific process and proving the feasibility of SLO. The results based on a set of widely used benchmark functions show that Seven-spot ladybird can converge fast and yield distributed solutions with higher precision.
FIGURES & TABLES
REFERENCES
J. Kennedy, R. Eberhart. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth: IEEE, 1995: 1942-1948.
Karaboga D.An idea based on honey bee swarm for numerical optimization,Technical Report- TR06[R].Kayseri:Erciyes University,Engineering Faculty,Computer Engineering Department,2005.
Geem ZW,Kim JH,Loganatham GV. A new heuristic optimization algorithm: harmony search[J]. Simulation,2001,76(2):60-68.
MENG X B,LIU Y,Gao X Z,et al. A new bio-inspired algorithm: chicken swarm optimization [C]//5th International Conference on Swarm Intelligence .Hefei: Springer International Publishing,2014:86-94.
CHENG L. New bionic algorithm：cockroach swarm optimization[J].Computer Engineering and Applications, 2008,44(34):44-46.
Feng X, Zhang J W, Yu H Q. Mosquito Host-Seeking Algorithm for TSP problem[J].Chinese Journal of computers,2014,37(8):1794-1808.
Cheng X B.Multi-target Tracking Based on Improved Simlified Particle Swarm Optimization[J].Computer Engineering,2016,42(08):282-288.
Yang X H,Xue Jian,Wang Y L.Deployment Optimization of Intergrated Network Node Based on Improved Artificial Bee colony Algorithm[J].Computer Engineering, 2016,42(03):116-120.
Wang G. Foraging behavior of predatory ladybugs [J] . Entomological Knowledge,1991,28(5):316-319.
J.L.Hemptinne,M Gaudin,et al.Social Feeding in ladybird beetles:adaptive significance and mechanism[J].Chemoecology.2000(10):149-152.