This paper proposes an evolutionary multi-objective optimization algorithm that applies the concept of biological immune system as an alternative algorithm for solving Pareto engineering optimization problems. The optimization algorithm developed and presented in this paper uses the cycle of affinity-maturation principle in the immune system that contains the repeated activation, proliferation and differentiation. The algorithm uses the enhanced expression strategy for handling constraints and the recombination in genetic algorithm to promote the solution performance. The designs of Pareto front can be generated in a single run of simulation by applying normalized function and weighting technique. All computational works completed in this paper uses the real-numbercoded representation for genes evolution that can be efficiently applied to general engineering design optimization problems.
淡江理工學刊 = Tamkang Journal of Science and Engineering 11(4), pp.395-402