A hybrid GA-based ant colony strategy for continuous correlated multiple response optimization problem

In this paper, suitability of an ant colony optimization (ACO) integrated with genetic algorithm-based local search for continuous multiple response optimization problem, commonly encountered in operations or production is validated. The overall work reported in this paper may be stratified into three parts. The first part is devoted to develop an ACO with diversification scheme for continuous search space using standard test functions. The second part discusses on how genetic algorithm (GA) is integrated with ACO, so as to improve the intensification of the search strategy. The final part of this work compares the performance of ACO-GA with simple ACO and real valued GA in multiple response optimization (MRO) problem. Multiple regression analysis and a `maximin´ desirability function are used to reduce the dimensionality and solve an MRO problem. The overall results indicate suitability of ACO-GA strategy for both single and multiple response optimization problems.