Abstract:

Aiming at solving the non-separable function optimization problem, a dual-system cooperative co-evolutionary differential evolution particle swarm optimization algorithm (DCCDE/PSO for short) is developed based on the dual-system cooperative co-evolutionary (CC) framework. The proposed algorithm gives a new CC framework of the dual-system A and B and its corresponding coordination mechanism for improving the diversity and convergence, and gives two algorithms for example differential evolution (DE), the improved particle swarm optimization (PSO) that it solves the systems A and B respectively, as well as complementary and matches with the roles that the systems A and B play in the dual system. The purpose is to improve computational performance of the dual system algorithm based on the CC framework. The numerical experimental results of non-separable Benchmark functions (1000 dimensional) show that the performance (computational accuracy and standard deviation) of the proposed DCCDE/PSO compared favorably against other three representative algorithms has advantages for some of functions and as a whole the four algorithms had theirselves’s strengths for the Benchmark functions and each complemented the other.