一种子群体个数动态变化的多目标优化协同进化算法

给出一种新型的在多目标优化条件下的进化算法群体停滞判别准则，并基于该准则提出一种合作型多目标优化协同进化算法．该算法在运行过程中自适应地决定子群体的新增和灭绝．使得子群体个数依据需要动态变化，减小了对计算资源的消耗，并解决了对复杂多目标优化问题难以事先进行分解的问题．对所提算法的计算复杂度进行了理论分析，并把它与已有的多目标进化算法进行了比较，结果表明所提算法具有较高的搜索性能．

A multi-objective optimization co-evolutionary algorithm with dynamically varying number of subpopulations

A new kind of criterion judging the stagnation of the population in evolutionary algorithms in the existence of multiple objectives is presented, and a cooperative multi-objective optimization co-evolutionary algorithm based on this criterion is proposed. The sub-population is adaptively added or deleted during the running of the algorithm, which makes the number of the sub-populations vary dynamically so that the computational cost is reduced and the difficulty of the decomposition of the complicated optimization problem is overcome. The computational complexity of the proposed algorithm is analyzed theoretically and it is compared with existed multi-objective evolutionary algorithms. Results indicate that the proposed algorithm can search more effectively.