Multiobjective Ant Colony Optimization Algorithm Based on Dynamic Constraint Evaluation Strategy for Highly Constrained Optimization

HCMOP are widespread in practical engineering such as vehicle routing problem and shop scheduling problem etc. The problems introduced above refer to optimization problems with complex constraints which lead to small and disconnected feasible regions. The optimization performance of general evolutionary algorithms decreases due to the small and dispersed feasible regions in highly constrained optimization problems. To address this problem, a multiobjective ant colony optimization algorithm based on dynamic constraint evaluation strategy (MOACO-DCE) is proposed in this article. First, a dynamic constraint violation metric is proposed to evaluate the constraint violation degree of solutions. The population is classified into the subpopulation with evolutionary advantages and the subpopulation with high constraint violation degree by this metric. Second, an evolutionary strategy based on dynamic transfer probabilities is proposed for the subpopulation with evolutionary advantages in order to improve the evolutionary efficiency. A Gaussian variation evolutionary strategy is proposed for the subpopulation with high constraint violation degree in order to improve the population diversity. Third, a pheromone collaborative updating strategy is proposed to achieve the synergistic pheromone updating between two subpopulations. A pheromone updating strategy considering the constraint violation metric is designed to improve the utilization of constraint violation information in the whole population. In addition, compared with other constrained multiobjective optimization algorithms, MOACO-DCE can obtain more satisfactory performance for the highly constrained optimization.