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为解决工程优化设计问题,引入文化进化框架,提出一种拥挤距离排序的多目标文化粒子群算法,采用拥挤距离排序算子,并删除密集区域的多余粒子,以保证Pareto前沿的分布均匀性;基于拥挤距离值,从精英知识和条件知识中选择处于最分散区域的粒子,并将其分别作为全局和局部最优,以增强算法全局寻优能力;依据拥挤距离的变化,动态调整粒子群飞行参数,以提高算法收敛效率,通过标准测试问题以及与其他算法的对比,表明了所提出算法的有效性和鲁棒性。
In order to solve the problem of engineering optimization design, a cultural evolutionary framework is introduced and a multi-objective cultural particle swarm optimization algorithm is proposed, which adopts crowding distance sorting operator and removes redundant particles in dense areas to ensure the uniformity of Pareto frontier distribution. Based on the congestion distance value, the particles in the most scattered area are selected from the elitist knowledge and condition knowledge, and are respectively considered as the global and local optimum, so as to enhance the global optimization ability of the algorithm. Based on the variation of congestion distance, the particle swarm is dynamically adjusted Parameters in order to improve the convergence efficiency of the algorithm. The standard test and comparison with other algorithms show the effectiveness and robustness of the proposed algorithm.