P-ADE: Self-adaptive differential evolution with fast and reliable convergence performance

A new differential evolution algorithm, p-ADE, is proposed to improve the rate and the reliability of convergence performance by implementing a new mutation strategy “DE/pbest-to-best” and controlling the parameters in a self-adaptive manner. “DE/pbest-to-best” utilizes the best previous solutions of each individual to guide the search direction and speed up convergence of the population. For the sake of balancing the global search ability and local search ability, a self-adaptive parameter setting strategy is presented, which avoids the requirement for prior knowledge or user interaction. Experiment results show that p-ADE outperforms many well-known self-adaptive DE algorithms in terms of rate, solution precision and reliability.