Solution Space Search Algorithm for Dispute Mediation Schemes Based on the Combination of Genetic Algorithm and Multi-Objective Particle Swarm Optimization

This paper focuses on the utilization of multi-objective optimization algorithms, specifically the Genetic Algorithm (GA) and Multi-objective Particle Swarm Optimization (MOPSO), to effectively navigate complex solution spaces. We introduce an innovative parallel fusion framework designed to leverage the strengths of both algorithms, thereby enhancing the quality of solutions. In this framework, GA and MOPSO operate independently, with each algorithm responsible for identifying high-quality solutions within a defined solution space. This independent operation promotes a diverse exploration of potential solutions. To facilitate collaboration, the best-performing solution sets are exchanged regularly between the two algorithms. This exchange serves as valuable guidance, allowing each algorithm to escape local optima and expand its search capabilities. Additionally, the framework incorporates a crossover operation that combines selected solutions from both GA and MOPSO to generate a new solution set. This new set is then reintegrated into each algorithm, enabling iterative refinement and improving convergence toward optimal solutions. Finally, the effectiveness of the proposed method is validated through real-world data generated during its application.