Compromising Pareto-Optimality With Regularity in Platform-Based Multiobjective Optimization

Multiobjective optimization problems give rise to a set of Pareto-optimal (PO) solutions, each of which makes a tradeoff among the objectives. When multiple PO 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 article, we propose a systematic search methodology that deviates from finding PO solutions, but finds a set of near PO solutions sharing common principles of a desired structure and still possessing a tradeoff among objectives. After proposing the regular evolutionary multiobjective optimization (RegEMO) algorithm, we first demonstrate its working principle on a number of constrained and unconstrained multiobjective 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 PO solutions without any common structure, is a practically meaningful task and this article should encourage more such practice-oriented developments of evolutionary multiobjective optimization in the near future.