Improvement of multi-objective differential evolutionary algorithm and its application for Hybrid electric vehicles

Differential evolutionary algorithm (DE) is a practical and simple :intelligent algorithm. Its improvement and application in multi objective problem is the main research of this paper. To improve the convergence speed and stability of DE, and use it to solve multi-objective problems, the mixed mutation strategy, self-adaptive parameters and minimum neighbor distance are used in this paper, aimed for better performance of the algorithm. Combining with these ideals, the multi-objective self-adaptive differential evolution (MOSDE) proposed in this paper is used so solve benchmark test functions and be compared with the SPEA2. The optimization of hybrid electric vehicle (HEV) is a nonlinear and constrained multi-objective optimization problem. For low consumption of fuel and emission load, we use the MOSDE to optimize some components' parameters and control strategy variables, and provide the best compromise solution from the Pareto solution set.