On Maximizing Solution Diversity in a Multiobjective Multidisciplinary Genetic Algorithm for Design Optimization

摘要

This article presents a new method for multiobjective multidisciplinary design optimization. The method obtains an estimate of the Pareto frontier with maximum solution diversity using a quality index, referred to as entropy index. Unlike previous methods that maintain diversity in the solution set heuristically, our method improves overall quality of solutions by explicitly optimizing the entropy index at every system-level iteration, and then using this information to bias the search process toward obtaining a solution set with maximum diversity. Our method utilizes a multi-objective genetic algorithm as an optimizer in each subproblem of a multidisciplinary optimization problem. To demonstrate the proposed approach, we applied our method to a mechanical design problem of a speed reducer and the results are compared with those obtained by a few other multiobjective optimization methods. A minimal set of quality indexes is used to compare the diversity and optimality of the obtained solution sets from the different methods on a quantitative basis.