Multi operators-based partial connected parallel evolutionary algorithm
Yuanjun Laili, Fei Tao, Zhang Li · 2016
With the increase of dimensions and complexity of current engineering problems, parallel evolutionary algorithm which take advantage of population division and information exchange among processors has been introduced for years. However, low solution ability of each sub-group and high communication load between them are always seen as the biggest bottlenecks which hinder parallel evolutionary algorithm to be more efficient. To overcome this two problems, a multi operators-based partial connected parallel evolutionary algorithm, i.e. MO-PCPEA is proposed. By combining multiple evolutionary operators, an adaptive strategy for operator configuration inside each parallel group is designed to ensure the searching ability of the algorithm for wider range of problems. More importantly, a partial connection topology is proposed to guide the periodic communication between each group. Computational results in two typical permutation combinatorial optimization benchmarks and one practical case study demonstrate that MO-PCPEA is highly competitive compared with most tailored serial and parallel evolutionary algorithms in terms of not only searching time, but also solution quality.