A diversity preservation method for expensive multi-objective combinatorial optimization problems using Novel-First Tabu Search and MOEA/D

• Published in: Expert systems with applications Vol. 202; p. 117251
• Main Authors: de Moraes, Matheus Bernardelli, Coelho, Guilherme Palermo
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.09.2022; Elsevier BV
• Subjects: Algorithms; Benchmarks; Black-box optimization; Combinatorial analysis; Constraints; Convergence; Decomposition-based methods; Diversity preservation; Expensive multi-objective combinatorial optimization; Multiple objective analysis; Novel-First Tabu Search; Oil fields; Optimization; Reproduction (copying); Search methods; Tabu search; Diversity preservation; Expensive multi-objective combinatorial optimization; Decomposition-based methods; Novel-First Tabu Search; Black-box optimization
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.117251

Expensive multi-objective combinatorial optimization problems have constraints in the number of objective function evaluations due to time, financial, or resource restrictions. As most combinatorial problems, they are subject to a high number of duplicated solutions. Given the fact that expensive environments limit the number of objective function evaluations, the existence of duplicated solutions heavily impacts the optimization process due to poor diversity and low convergence speed. This paper proposes the Novel-First Tabu Search, a greedy-strategy mechanism that uses Knowledge-Assisted Local Search methods to preserve the population diversity and increase the exploration and exploitation ability of MOEA/D. Experiments are conducted on constrained, unconstrained, multimodal, deceptive, linear, convex, and non-convex Pareto Front multi-objective combinatorial optimization benchmark problems. This paper also conducts an experiment on the real-world, expensive problem of Well Placement Optimization using a benchmark case based on the Namorado oil field, located in the Campos Basin, Brazil. The experimental results and performance comparison with state-of-the-art algorithms demonstrate that the proposed design significantly preserves diversity and increases convergence without violating the constraint in the number of objective function evaluations. •A greedy strategy that uses knowledge-assisted local search methods is developed.•The greedy strategy is combined with the MOEA/D algorithm.•The method is evaluated on five well-known multi-objective combinatorial problems.•The method is evaluated on the real-world problem of Well Placement Optimization.•It achieves faster convergence in comparison with state-of-the-art algorithms.