admin DOI:
Keywords:
Multi Objective Optimization, MaF8 Benchmark, MOBCA, LMPFE, PlatEMO, Evolutionary Algorithms
Abstract
The multi-objective optimization problems (MOPs) aim to balance conflicting objectives to identify a set of trade-off solutions, known as the Pareto front. This study evaluates the Multi-Objective Besiege and Conquer Algorithm (MOBCA), an evolutionary algorithm extending the single objective Besiege and Conquer Algorithm (BCA) by integrating grid, archiving, and leader selection mechanisms to approximate the Pareto optimal frontier. MOBCA is compared with the Large-Margin Pareto Front Estimation (LMPFE) algorithm on the MaF8 benchmark, a complex problem featuring a concave Pareto front and high-dimensional decision space. Using the PlatEMO platform, we assessed performance over 30 runs. Convergence was greater in case of LMPFE while on other hand computational efficiency was core objective of MOBCA together with solution diversity. The summary of this whole procedure illustrates the value of MOBCA in processing complex objective problems while taking additional standards on the board.
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