Compromising Pareto-Optimality With Regularity in Platform-Based Multiobjective Optimization
Abstract
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.
Authors: Ritam Guha, Kalyanmoy Deb
Published in: IEEE Transactions on Evolutionary Computation (2023)