A Convergent Differential Evolution Algorithm with Hidden Adaptation Selection for Engineering Optimization

Many improved differential Evolution (DE) algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS) operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011

Multi-population-based differential evolution algorithm for optimization problems

A differential evolution (DE) algorithm is an evolutionary algorithm for optimization problems over a continuous domain. To solve high dimensional global optimization problems, this work investigates the performance of differential evolution algorithms under a multi-population strategy. The original DE algorithm generates an initial set of suitable solutions. The multi-population strategy divides the set into several subsets. These subsets evolve independently and connect with each other according to the DE algorithm. This helps in preserving the diversity of the initial set. Furthermore, a comparison of combination of different mutation techniques on several optimization algorithms is studied to verify their performance. Finally, the computational results on the arbitrarily generated experiments, reveal some interesting relationship between the number of subpopulations and performance of the DE. Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. In this problem, the above algorithm is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed algorithm is one of effective and promising methods for optimal EV centralized charging