Hybrid Intelligent Multi-Objective Dynamic Optimization Based on MOEPSO-GA

In this paper, a hybrid intelligent optimization method based on multi-objective enhanced particle swarm optimization-genetic algorithm (MOEPSO-GA) is proposed for solving multi-objective dynamic optimization problem. This method innovatively combines the global search capability of heuristic algorithms with the high precision and guaranteed optimality of deterministic algorithms, ensuring an evenly distributed Pareto front. Firstly, a global dual optimal selection mechanism is introduced, utilizing a genetic algorithm to balance the diversity and convergence of non-dominated solutions. Secondly, an adaptive flight parameter adjustment mechanism is designed to assist particles in adaptively selecting learning modes, thereby balancing the global exploration and local development capabilities. Thirdly, the optimal replacement mechanism significantly enhances population diversity, enabling MOEPSO-GA to rapidly explore non-dominated regions. Fourthly, the adaptive ε-constraint method is combined with sequential quadratic programming to optimize the non-dominated set explored by MOEPSO-GA, promoting rapid convergence to the true Pareto front and thereby reducing computational costs. Finally, the effectiveness and superiority of the proposed algorithm are validated through numerical simulations and comparisons with state-of-the-art algorithms.