A localized high-fidelity-dominance-based many-objective evolutionary algorithm
Abstract
The practicality of Pareto-dominance in solving many-objective optimization problems becomes questionable due to its inability to factor the critical human decision-making (HDM) elements, including the number of better objectives, the degree of betterment in objectives, and objectives’ relative preference. Relevant dominance principles are recently proposed to incorporate the first two HDM elements, often with the need for new tunable parameters. This article proposes a high-fidelity-dominance principle that factors all the three HDM elements, explicitly and simultaneously, and without requiring tuning of any parameter. This principle has been implemented in a reference-vector-based framework, leading to a computationally efficient many-objective evolutionary algorithm (MaOEA), namely, localized high-fidelity-dominance-based EA (LHFiD). Critically, LHFiD also has an inbuilt mechanism for on-the-fly determination of the timing for: 1) intermittent Nadir point estimation that enables faster convergence and 2) its self-termination that bears practically utility. This article is based on an extensive study involving 41 912 experiments, in which the proposed LHFiD approach is compared with the existing competitive MaOEAs. This article reports statistically better performance in about 60% instances, making it practical and worthy of further investigation and application.
Authors: Dhish Kumar Saxena, Sukrit Mittal, Sarang Kapoor, Kalyanmoy Deb
Published in: IEEE Transactions on Evolutionary Computation (2022)