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Elitism-based immune genetic algorithm and its application to optimization of complex multi-modal functions

Journal of Central South University of Technology 2008年第6期15卷 845-852页

作者：谭冠政周代明江斌 DIOUBATE Mamady I School of Information Science and Engineering Central South University

A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.

关键词： immune genetic algorithm multi-modal function optimization evolutionary computation elitist selection elitist crossover

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Niche Ant Colony Algorithm and Simulation Study

Niche Ant Colony Algorithm and Simulation Study

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The 2011 Fourth International Symposium on Knowledge Acquisition and Modeling（KAM 2011）

作者： Tang Chao-li Huang You-rui Li ji-yun Tian xiao-min Institute of Electrical and Information Engineering Anhui University of Science and Technology

A niche ant colony algorithm（NACA） for multi-modal function optimization, which bases on principle of ant colony algorithm, is devised. It adopts real code. By simulation research of the NACA, and comparing with correlative algorithms, the results show that the NACA has advantages, such as easy to select parameters, strong adaptability, and good convergence, and is very suitable for multi-modal functions optimization with searching for multiple optimal solutions or global optimal solutions.

关键词： niche technology ant colony algorithm multi-modal function optimization

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