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A Two-Level Subspace Evolutionary Algorithm for Solving multi-modal function Optimization Problems

Wuhan University Journal of Natural Sciences 2003年第S1期8卷 249-252页

作者： Li Yan, Kang ZhuoComputation Center, Wuhan University, Wuhan 430072, Hubei, China Computation Center Wuhan University Wuhan Hubei China

In this paper, a new algorithm for solving multi-modal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombination search so that the whole population can be separated into several niches according to the position of solutions; then, in the second level, the niche evolutionary strategy is used for local search in the subspaces gotten in the first level till solutions of the problem are found. The new algorithm has been tested on some hard problems and some good results are obtained.

关键词： multi-modal function subspace search evolutionary algorithm

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Research on Modified Shifting Balance Genetic Algorithms

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Journal of China University of Mining and Technology 2007年第2期17卷 188-192页

作者： MA Hong-mei GONG Dun-wei School of Information and Electrical Engineering China University of Mining ＆ Technology Xuzhou Jiangsu 221008 China

The increasing overlap of core and colony populations during the anaphase of evolution may limit the performance of shifting balance genetic algorithms. To decrease such overlapping,so as to increase the local search capability of the core population,the sub-space method was used to generate uniformly distributed initial colony populations over the decision variable space. The core population was also dynamically divided,making simultaneous searching in several local spaces possible. The algorithm proposed in this paper was compared to the original one by searching for the optimum of a complicated multi-modal function. The results indicate that the solutions obtained by the modified algorithm are better than those of the original algorithm.

关键词： genetic algorithms shifting balance genetic algorithms small spaces dynamic partition multi-modal function

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ARTIFICIAL IMMUNE ALGORITHM OF multiCELLULAR GROUP AND ITS CONVERGENCE

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Journal of Pharmaceutical Analysis 2005年第2期17卷 23-27页

作者：罗印升李人厚张维玺 School of Electronics and Information Engineering Xi'an Jiaotong University Xi'an 710049 China

Objective To find out more extrema simultaneously including global optimum and multiple local optima existed in multi-modal functions. Methods Germinal center is the generator and selector of high-affinity B cells, a multicellular group's artificial immune algorithm was proposed based on the germinal center reaction mechanism of natural immune systems. Main steps of the algorithm were given, including hyper-mutation, selection, memory, similarity suppression and recruitment of B cells and the convergence of it was proved. Results The algorithm has been tested to optimize various multi-modal functions, and the simulation results show that the artificial immune algorithm proposed here can find multiple extremum of these functions with lower computational cost. Conclusion The algorithm is valid and can converge on the satisfactory solution set D with probability 1 and approach to global solution and many local optimal solutions existed.

关键词： germinal center reaction B cell artificial immune algorithm multi-modal function

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