Compromising Pareto-Optimality With Regularity in Platform-Based Multiobjective Optimization
提出一种系统搜索方法，不追求理论上的帕累托最优解，而是寻找一组共享期望结构且接近最优的解，便于平台化应用，并通过算法RegEMO在测试和工程问题上验证其意义。
Multi-objective optimization problems give rise to a set of Pareto-optimal solutions, each of which makes a trade-off among the objectives. When multiple Pareto-optimal solutions are to be implemented for different applications as platform-based solutions, a solution principle common to them is highly desired for easier understanding, implementation, and management purposes. In this paper, we propose a systematic search methodology that deviates from finding Pareto-optimal solutions, but finds a set of near Pareto-optimal solutions sharing common principles of a desired structure and still possessing a trade-off among objectives. After proposing the regular evolutionary multi-objective optimization (RegEMO) algorithm, we first demonstrate its working principle on a number of constrained and unconstrained multi-objective test problems. Thereafter, we demonstrate the practical significance of the proposed approach to a number of engineering design problems. Searching for a set of solutions with common principles of desire, rather than theoretical Pareto-optimal solutions without any common structure, is a practically meaningful task and this paper should encourage more such practice-oriented developments of EMO in the near future.