On improving multiobjective genetic algorithms for design optimization

This paper presents some improvements to Multi-Objective Genetic Algorithms (MOGAs). MOGA modifies certain operators within the GA itself to produce a multiobjective optimization technique. The improvements are made to overco me some of the shortcomings in niche formation, stopping criteria and inter action with a design decision-maker. The technique involves filtering, mati ng restrictions, the idea of objective constraints, and detecting Pareto so lutions in the non-convex region of the Pareto set. A step-by-step procedur e for an improved MOGA has been developed and demonstrated via two multiobj ective engineering design examples: (i) two-bar truss design, and (ii) vibr ating platform design. The two-bar truss example has continuous variables w hile the vibrating platform example has mixed-discrete (combinatorial) vari ables. Both examples are solved by MOGA with and without the improvements. Tt is shown that MOGA with the improvements performs better for both exampl es in terms of the number of function evaluations.