Modified the Performance of Differential Evolution Algorithm with Dual Evolution Strategy

Differential Evolution (DE) is one of the novel algorithms of evolution computation. Although it performs superiorly, DE has several disadvantages. In this study, we proposed the construction of a novel DEPSO algorithm in DE and Particle Swarm Optimization (PSO). DEPSO is a strategy of Dual Evolution (DES) based on the master-apprentice mechanism for sharing information. During the iteration, between the two algorithms can be iterative operation to improve the drawbacks easy to drop into region optimum moreover increasing the performance to obtain the advantage of accuracy solving and stable convergence.