一种群体智能算法——狮群算法

doi:10.16451/j.cnki.issn1003-6059.201805005

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Abstract Based on the natural division of labor among lion king, lionesses and cubs in a lion group, a swarm intelligent algorithm, loin swarm optimization(LSO), is proposed. LSO is inspired by intelligent behaviors of three populations including lion guarding, lioness hunting, cubs following. In LSO, policies of location updating are different for three populations. LSO follows the biological competition law of “survival of the fittest” in nature world, i.e., the lion king guards territory and possesses the priority of food, lionesses cooperate in hunting, and lion cubs fall into eating, learning to hunt, and being expelled after entering adulthood. The diversity of lion location updating guarantees that LSO converges fast and is not easily trapped into a local optimal solution. LSO is compared with the particle swarm optimization and the bare bones particle swarm optimization on six optimization test functions. Results show that LSO produces fast convergence and high precision, and it obtains a better global optimal solution.

Key words： Lion Swarm Optimization(LSO) Lion Swarm Optimization(LSO) Particle Swarm Optimization(PSO) Particle Swarm Optimization(PSO) Function Optimization Function Optimization Swarm Intelligence Swarm Intelligence

Received: 06 November 2017

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TP 18

Corresponding Authors: YANG Yan, master, lecturer． Her research interests include computation intelligence， swarm intelligence algorithm and its application.

About author:: LIU Shengjian， master, lecturer． His research interests include swarm intelligence algorithm and deep learning.
ZHOU Yongquan, Ph.D., professor. His research interests include computation intelligence， neural networks and its application.

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	LIU Shengjian
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Cite this article:

LIU Shengjian,LIU Shengjian,YANG Yan等. A Swarm Intelligence Algorithm-Lion Swarm Optimization[J]. , 2018, 31(5): 431-441.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201805005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I5/431

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