Subpopulation Diversity Based Selecting Migration Moment in Distributed Evolutionary Algorithms
This work addresses a specific bottleneck in distributed evolutionary algorithms for optimization problems like the traveling salesman problem, offering an incremental improvement over traditional methods.
The paper tackled the problem of predetermined migration intervals in distributed evolutionary algorithms by proposing a scheme that adjusts migration success rates based on subpopulation diversity, resulting in significant advantages in solution quality, especially for high-difficulty instances.
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.