Using Imperialist Competitive Algorithm in Optimization of Nonlinear Multiple Responses

The quality of manufactured products is characterized by many controllable quality factors. These factors should be optimized to reach high quality products. In this paper we try to find the controllable factor’s levels with minimum deviation from their target and with a least variation. To solve such problems a simple aggregation function is used to aggregate the multiple response functions followed by an imperialist competitive algorithm used to find the best level of each controllable variable. Moreover the problem has been better analyzed by Pareto optimal solution to release the aggregation function. Then the proposed multiple response imperialist competitive algorithm (MRICA) has been compared with Multiple objective Genetic Algorithm (MOGA). The experimental results show efficiency of the proposed approach in both aggregation and non aggregation methods for optimization of the nonlinear multi-response programming.

Keywords:

Multi-response Optimization , Imperialist competitive Algorithm , Genetic Algorithm , Experimental Design