Scalable Polynomial RegEM (a) O for Multi-lMany-objective Platform-based Design Optimization Problems
Abstract
The goal of a generic evolutionary multi- or many-objective algorithm is to explore a search space and find the trade-off optimal solutions for two or more conflicting objectives. In platform-based practical design optimization problems, it is not sufficient to just find a set of trade-off optimal solutions, certain regularity properties are expected in the entire fleet of trade-off solutions. For this purpose, we propose a scalable regularity-based optimization framework - RegEM(a)O - which automatically extracts polynomial regularity principles from the resulting Pareto-optimal front of multi- or many-objective problems. Thereafter, it attempts to search for a regular front of trade-off solutions following a similar form of polynomial regularity principles. Despite being slightly worse than the true Pareto-optimal solutions, regular solutions possess simple properties among them, making them easily interpretable, their inventory easily maintainable, and easily scalable. In this paper, we apply RegEM(a)O to a number of small and large-scale real-world engineering design problems to demonstrate its practical advantage.
Authors: Ritam Guha, Kalyanmoy Deb
Published in: IEEE Congress on Evolutionary Computation (CEC) (2024)