Mechanical Engineering

C. J. Shih This email address is being protected from spambots. You need JavaScript enabled to view it.1 and T. L. Kuan1

1Department of Mechanical and Electro-Mechanical Engineering, Tamkang University, Tamsui, Taiwan 251, R.O.C.

 

Received: July 30, 2007
Accepted: July 17, 2008
Publication Date: December 1, 2008

Download Citation: ||https://doi.org/10.6180/jase.2008.11.4.09  


ABSTRACT

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-number coded representation for genes evolution that can be efficiently applied to general engineering design optimization problems.


Keywords: Evolutionary Algorithm, Immune System, Engineering Optimization, Multi-Objective Optimization, Pareto Front Design


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