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针对约束边界粒子在边界区域搜索能力不足的问题,提出一种基于自适应进化学习的约束多目标粒子群优化算法.该算法根据不符合约束条件粒子的约束违反程度,修正优化算法的进化学习公式,提高算法在约束边界区域的搜索能力;通过引入一种基于拥挤距离的Pareto最优解分布性动态维护策略,在不增加算法复杂度的前提下改进Pareto前沿的分布性.实验结果表明,所提出的算法可以获得具有更好收敛性、分布性和多样性的Pareto前沿.
Aiming at the problem that the bounding boundary particle can not search enough in the boundary area, a constrained multi-objective particle swarm optimization algorithm based on adaptive evolutionary learning is proposed. The algorithm updates the evolutionary learning formula of the optimization algorithm according to the degree of constraint violation of particles that do not meet the constraints , And improve the searching ability of the algorithm in the bounding boundary region. By introducing a Pareto optimal solution dynamic distribution strategy based on congestion distance, the Pareto frontier distribution is improved without increasing the complexity of the algorithm. The experimental results show that, The proposed algorithm can obtain the Pareto front with better convergence, distribution and diversity.