Decomposition-based evolutionary computing in multicriteria optimization environment

The paper presents strategies for implementing decomposition based genetic algorithms in multicriteria design optimization. The decomposition approach requires that the system design problem be partitioned into smaller sized subsystems, and the system solution obtained as a combination of the solutions from the subsystems. The absence of gradient information in a genetic algorithm based search strategy requires alternative methods for communicating the design information in different subsystems. Two newly developed methods referred to as experiential inheritance and interspecies migration were used to coordinate the solutions of subsystems in the decomposition based approach. Both the weighted sum and weighted minimax methods were explored in the solution to the multicriteria design problem. The proposed strategies were validated through implementation in representative algebraic and structural design problems.