An Adaptive Dynamic Migration-based Constrained Multiobjective State Transition Algorithm

Complex constrained multi-objective optimization problems are often characterized by narrow and disconnected feasible regions, making it challenging to obtain the optimal constrained Pareto front. To improve feasibility, diversity, and convergence in the optimization process, this paper introduces an adaptive dynamic migration-based constrained multi-objective state transition algorithm. The approach starts with an individual-based state transition algorithm, which searches for candidate solutions across the entire search space. Subsequently, these candidate solutions are evaluated using an adaptive dynamic migration constraint handling technique, which enhances the convergence of candidate solutions in the early stages of optimization and ensures their feasibility in the later stages. The migration process is adaptively adjusted based on the diversity and convergence of the population to maintain a balance among feasibility, diversity, and convergence. Experiments conducted on 14 benchmark problems reveal that the proposed method demonstrates superior or, at the very least, competitive performance when compared to other well-established methods.