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中文摘要：
提出一种高维多目标多方向协同进化算法（HMMCA）.该算法利用目标空间内的一组方向向量将多目标优化问题分解成多个方向进行寻优,并提出一种混合变异策略以加强算法在每个方向上的收敛能力;同时,该算法采用改进的交互式模糊支配和拥挤度估计因子来维护外部归档集的规模,增强种群的收敛性和分布性.将该算法与目前性能最好的3种多目标进化算法在标准测试函数集上进行对比实验,所得结果表明HMMCA与其他算法相比具有更好的收敛性和分布性.
英文摘要：
A high-dimensional multi-objective multi-directional co-evolutionary algorithm（HMMCA） is proposed. Firstly, the multi-objective optimization problem is decomposed into multiple directions for optimization by using a set of direction vector, and a hybrid mutation strategy is proposed to improve the convergence performance in every direction. Then, an innovative interactive fuzzy dominance and crowding factor are used to maintain the size of external archive. The proposed algorithm is compared to 3 state-of-the-art MOEAs on benchmark test problems. Simulation results show that the HMMCA has obvious advantage in convergence and distribution than other algorithms.
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