A Swarm Intelligence Algorithm-Lion Swarm Optimization
LIU Shengjian1, YANG Yan1, ZHOU Yongquan2
1.Department of Game, South China Institute of Software Engineering, Guangzhou University, Guangzhou 510990 2.College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006
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.
[1] DORIGO M, MANIEZZO V, COLOMI A. Ant System: Optimization by a Colony of Coorperating Agents. IEEE Transactions on Systems, Man, and Cybernetics(Cybernetics), 1996, 26(1): 29-41. [2] KENNEDY J, EBERHART R C. Particle Swarm Optimization//Proc of the IEEE International Conference on Neural Networks. Washington, USA: IEEE, 1995: 1942-1948. [3] EBERHART R C, KENNEDY J. A New Optimizer Using Particle Swarm Theory//Proc of the 6th International Symposium on Micro Machine and Human Science. Washington, USA: IEEE, 1995: 39-43. [4] KENNEDY J. Bare Bones Particle Swarms//Proc of the IEEE Swarm Intelligence Symposium. Washington, USA: IEEE, 2003: 80-87. [5] 王东风,孟 丽,赵文杰.基于自适应搜索中心的骨干粒子群算法.计算机学报, 2016, 39(12): 2652-2667. (WANG D F, MENG L, ZHAO W J. Improved Bare Bones Particle Swarm Optimization with Adaptive Search Center. Chinese Journal of Computers, 2016, 39(12): 2652-2667.) [6] 周新宇,吴志健,王 晖,等.一种精英反向学习的粒子群优化算法.电子学报, 2013, 41(8): 1647-1652. (ZHOU X Y, WU Z J, WANG H, et al . Elite Opposition-Based Particle Swarm Optimization. Acta Electronica Sinica, 2013, 41(8): 1647-1652.) [7] 耿焕同,陈正鹏,陈 哲,等.基于平衡搜索策略的多目标粒子群优化算法.模式识别与人工智能, 2017, 30(3): 224-234. (GENG H T, CHEN Z P, CHEN Z, et al . Multi-objective Particle Swarm Optimization Algorithm Based on Balance Search Strategy. Pattern Recognition and Artificial Intelligence, 2017, 30(3): 224-234.) [8] 李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法.系统工程理论与实践, 2002, 22(11): 32-38. (LI X L, SHAO Z J, QIAN J X. An Optimizing Method Based on Autonomous Animats: Fish-Swarm Algorithm. Systems Engineering-Theory & Practice, 2002, 22(11): 32-38.) [9] EUSUFF M M, LANSEY K E. Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm//Proc of the World Water and Environmental Resources Congress. New York, USA: ACM, 2001. DOI: 10.1061/40569(2001)412. [10] KRISHNANAND K N, GHOSE D. Glowworm Swarm Optimisation : A New Method for Optimising Multi-modal Functions. International Journal of Computational Intelligence Studies, 2009, 1(1): 93-119. [11] 吴虎胜,张凤鸣,吴庐山.一种新的群体智能算法——狼群算法.系统工程与电子技术, 2013, 35(11): 2430-2438. (WU H S, ZHANG F M, WU L S. New Swarm Intelligence Algorithm-Wolf Pack Algorithm. Systems Engineering and Electronics, 2013, 35(11): 2430-2438.) [12] KARABOGA D. An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report, TR06. Kayseri, Turkey: Erciyes University, 2005. [13] RASHEDI E, NEZAMABADI-POUR H, SARYAZDI S. GSA：A -Gravitational Search Algorithm. Information Science, 2009, 179(13): 2232-2248. [14] 张聚伟,王 宇,杨 挺.基于模糊粒子群算法的有向传感器网络路径覆盖策略.模式识别与人工智能, 2017, 30(2): 183-192. (ZHANG J W, WANG Y, YANG T. Path Coverage Scheme Based on Fuzzy Particle Swarm Optimization Algorithm for Directional Sensor Networks. Pattern Recognition and Artificial Intelligence, 2017, 30(2): 183-192.) [15] 雷开友,邱玉辉,贺 一.一种优化高维复杂函数的PSO算法.计算机科学, 2006, 33(8): 202-205. (LEI K Y, QIU Y H, HE Y. An Effective Particle Swarm Optimizer for Solving Complex Function with High Dimensions. Computer Science, 2006, 33(8): 202-205.) [16] 吴定会,孔 飞,纪志成.鸡群算法的收敛性分析.中南大学学报(自然科学版), 2017, 48(8): 2105-2112. (WU D H, KONG F, JI Z C. Convergence Analysis of Chicken Swarm Optimization Algorithm. Journal of Central South University(Science and Technology), 2017, 48(8): 2105-2112.) [17] SOLIS F J, WETS J B. Minimization by Random Search Techniques. Mathematics of Operations Research, 1981, 6(1): 19-30.
2018, Vol. 31 
