Cubic Chaos Preference Multi-Objective Optimization Algorithm with Adaptive Dual-Mode Mutation

Cubic Chaos Preference Multi-Objective Optimization Algorithm with Adaptive Dual-Mode Mutation

In the study of preference-based multi-objective optimization algorithms, the performance significantly depends on the preference information provided by the decision-maker. Over-reliance on this preference information can lead the algorithms to become trapped in locally optimal solutions, potentially overlooking high-quality solutions in other regions. Therefore, this paper proposes a Cubic Chaos Preference Multi-Objective Optimization Algorithm with Adaptive Dual-mode Mutation (CPMOP-DM). Firstly, this algorithm utilizes the cubic chaos strategy to initialize population. This strategy possesses better chaos traversal and optimization speed and then, helps enhance the search breadth and global convergence of the optimization algorithm. Secondly, a dynamic focused preference exploration strategy is proposed to enhance the quality and satisfaction of the selected solutions. This strategy can gradually refine the search scope via, constructing dynamically shrinking exploration circles. Compared to traditional preference-based algorithms, experimental results demonstrate the competitiveness of the proposed algorithm. It effectively balances exploration and exploitation searching of the algorithm, thereby enhancing the algorithm’s diversity and distribution. It also avoids the local optimum problem caused by over-reliance on preference information.