IFMOA: immune forgetting multiobjective optimization algorithm

Authors:
Bin Lu;Licheng Jiao;Haifeng Du;Maoguo Gong
Affiliations:
Institute of Intelligent Information Processing, Xidian University, Xi'an, P.R. China;Institute of Intelligent Information Processing, Xidian University, Xi'an, P.R. China;School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, P.R.China;Institute of Intelligent Information Processing, Xidian University, Xi'an, P.R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

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Abstract

Based on the Antibody Clonal Selection Theory and the dynamic process of immune response, a novel Immune Forgetting Multiobjective Optimization Algorithm (IFMOA) is proposed. IFMOA incorporates a Pareto-strength based antigen-antibody affinity assignment strategy, a clonal selection operation, and a technique simulating the progress of immune tolerance. The comparison of IFMOA with other two representative methods, Multi-objective Genetic Algorithm (MOGA) and Improved Strength Pareto Evolutionary Algorithm (SPEA2), on different test problems suggests that IFMOA extends the searching scope as well as increasing the diversity of the populations, resulting in more uniformly distributing global Pareto optimal solutions and more integrated Pareto fronts over the tradeoff surface.