Ordered Charging Planning for Electric Vehicle Clusters in Tourist Attractions Based on Improved Moth-Flame Optimization Integrated With Genetic Mechanism

Abstract

This is an optimization problem of ordered charging planning (OCP) for electric vehicle clusters (EVCs) in tourist attractions, and it is an important and difficult problem. In this work, for solving this optimization problem, an improved moth-flame optimization integrated with genetic mechanism (IMFO-G) is proposed. Specifically, based on the moth-flame optimization integrated with genetic mechanism, the adaptive nonlinear decreasing strategies with selection, crossover and mutation probability as well as weight coefficient are designed, and the opposition-based learning is also introduced simultaneously. To verify the effectiveness of proposed IMFO-G, simulation verification of an example of the OCP optimization problem of EVCs in tourist attractions is provided. The simulation results show that the proposed improvement strategies can effectively improve the global optimization performance for IMFO-G, and a more ideal optimization solution of the OCP optimization problem for EVCs in tourist can be obtained.

Keywords

• Ordered charging planning
• electric vehicle clusters
• improved moth-flame optimization
• adaptive nonlinear decreasing strategy
• opposition-based learning