MultiModal Immune Algorithm Based on Peaks Poised and Gradient Evolution Strategies
YANG KongYu1,2, WANG XiuFeng3
1.College of Computer Information Engineering, Shandong University of Finance, Jinan 250014 2.College of Information and Electrical Engineering, Shandong University of Architecture and Engineering, Jinan 250101 3.College of Information Technology and Science, Nankai University, Tianjin 300071
Abstract Some available multimodal optimization algorithms are analyzed and the faults of them are pointed out. Based on their same features that they all have fine search effect to functions with equivalence peaks, the peaks poised strategy is proposed. Then a new Multimodal Immune Algorithm (MIA) with mechanisms of antibody evolution in immune system and conventional gradient evolution is designed. The implementations of peaks poised strategy and main evolution operators are given, the algorithm's operating mechanisms, complete convergence and computation complicacy are analyzed. The simulation experiments are performed and the results testify that MIA has availability on solving multimodal optimization problems, especially for functions with nonequivalence peaks, complete convergence and quickly convergence ability.
[1] Goldberg D E, Richardson J. Genetic Algorithms with Sharing for Multi-Modal Function Optimization. In: Proc of the 2nd International Conference on Genetic Algorithms and Their Applications. Hillsdale, USA, 1987, 41-49 [2] William M S. Simple Subpopulation Schemes. In: Proc of the 3rd Annual Conference on Evolutionary Programming. San Diego, USA, 1994, 296-307 [3] Yang K Y, Wang X F. Research of an Integrated Immune Evolutionary Algorithm and Its Convergence Property. Computer Engineering and Applications, 2003, 39(31): 33-35,118 (in Chinese) (杨孔雨,王秀峰.一种集成免疫进化算法及其收敛性研究.计算机工程与应用, 2003, 39(31): 33-35,118) [4] Fukuda T, Mori K, Tsukiyama M. Parallel Search for Multi-Modal Function Optimization with Diversity and Learning of Immune Algorithm. In: Dasgupta D, ed. Artificial Immune Systems and Their Applications. Berlin, Germany: Spring-Verlag, 1999, 210-220 [5] Liu H J. Research of Genetic Algorithms and Its Application in Financial Forecast and Financial Decision. Ph.D Dissertation. College of Information Science and Technology, Nankai University, Tianjin, China, 2002 (in Chinese) (刘洪杰. 遗传算法及其在金融预测和金融决策中的应用研究.博士学位论文.南开大学信息技术科学学院,天津, 2002) [6] Yang K Y, Wang X F. Research of Immune Memory Genetic Algorithm and Its Complete Convergence. Computer Engineering and Applications, 2005, 41(12): 47-50 (in Chinese) (杨孔雨,王秀峰.免疫记忆遗传算法及其完全收敛性研究.计算机工程与应用, 2005, 41(12): 47-50) [7] Yu X J, Wang Z J. A New Clustering Method and Its Applications on Multi-Modal Optimization. Journal of Tsinghua University (Science & Technology), 2001, 41(4-5): 159-162 (in Chinese) (于歆杰,王赞基.一种新的聚类方法及其在多峰优化中的应用.清华大学学报(自然科学版), 2001, 41(4-5): 159-162) [8] Luo Y S, Li R H, Zhang W X. Multi-Modal Function Parallel Optimization Algorithm Based on Immune Mechanism. Journal of System Simulation, 2005, 17(2): 319-322 (in Chinese) (罗印升,李人厚,张维玺.基于免疫机理的多峰值函数并行优化算法.系统仿真学报, 2005,17(2): 319-322)
2006, Vol. 19 
