分布式进化算法中基于子种群多样性的迁移矩选择
Subpopulation Diversity Based Selecting Migration Moment in Distributed Evolutionary Algorithms
摘要：
在分布式进化算法中，迁移间隔被用来确定迁移矩。然而，由时间间隔预先确定的迁移时刻并不能与演化的动态情况相匹配。本文提出了一种基于子种群多样性的迁移成功率设置方案。在该计划下，移民仍然会间隔时间发生，但移民进入目标亚人口的概率将根据拟议的公式由该亚人口的多样性决定。分析表明，我们的方案的时间消耗是可以接受的。在我们的实验中，并行的基础是旅行商问题的进化算法。在不同的参数值组合的公式，8个基准实例的分布式进化算法的结果与传统的一个，分别进行了比较。结果表明，基于该方案的分布式进化算法在求解高难度问题时具有明显的优势。此外，可以看出，该方案的算法具有最突出的性能在上述参数的三个值组合的公式。
英文摘要：
In distributed evolutionary algorithms, migration interval is used to decide migration moments. Nevertheless, migration moments predetermined by intervals cannot match the dynamic situation of evolution. In this paper, a scheme of setting the success rate of migration based on subpopulation diversity at each interval is proposed. With the scheme, migration still occurs at intervals, but the probability of immigrants entering the target subpopulation will be determined by the diversity of this subpopulation according to a proposed formula. An analysis shows that the time consumption of our scheme is acceptable. In our experiments, the basement of parallelism is an evolutionary algorithm for the traveling salesman problem. Under different value combinations of parameters for the formula, outcomes for eight benchmark instances of the distributed evolutionary algorithm with the proposed scheme are compared with those of a traditional one, respectively. Results show that the distributed evolutionary algorithm based on our scheme has a significant advantage on solutions especially for high difficulty instances. Moreover, it can be seen that the algorithm with the scheme has the most outstanding performance under three value combinations of above-mentioned parameters for the formula.
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