Enhancing the Convergence Ability of Evolutionary Multi-objective Optimization Algorithms with Momentum
Pages 476 - 483
Abstract
To improve the convergence ability of evolutionary multi-objective optimization algorithms (EMOAs), various strategies have been proposed. One effective strategy is to use good momentum from the previous generations to create new solutions. However, the definition of good momentum has not been carefully studied. In this paper, we propose five different definitions of good momentum for EMOAs. Then, we explain their integration into popular EMOAs such as NSGA-II, MOEA/D, and SMS-EMOA. Through computational experiments, we demonstrate that the use of an appropriate definition of good momentum greatly accelerates the convergence of EMOAs on both artificial test problems and real-world problems, particularly on large-scale problems.
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Index Terms
- Enhancing the Convergence Ability of Evolutionary Multi-objective Optimization Algorithms with Momentum
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Published: 14 July 2024
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GECCO '24: Genetic and Evolutionary Computation Conference
July 14 - 18, 2024
VIC, Melbourne, Australia
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