Abstract
Aggregative gradient-based multiobjective optimization (AGMO) is a methodology for obtaining Pareto optimal solutions based on local search techniques. AGMO typically uses the weighing-sum technique, with weighting coefficients adaptively given during the optimization process. An AGMO method can handle large-scale problems that include numerous constraints and design variables, which are problematic when using meta-heuristic techniques. This paper proposes techniques to enhance the diversity of search points during the optimization process in an AGMO method, by introducing distance constraints and adaptive adjustment of the number of search points, so that well-distributed Pareto solutions are obtained. The proposed method is applied to example problems to illustrate its effectiveness.
