Improvement Of Multi-Objective Differential Evolutionary Algorithm And The Optimization For Hybrid Electric Vehicles

As a common and complex problem,multi-objective optimization problem exist widely in practical engineering application and scientific research.The improvement of multi-objective differential evolutionary algorithm is the main research of this paper,so does its application for hybrid electric vehicle.The main research of this paper concludes follows:Firstly,the process and shortcomings of the conventional differential evolution algorithm are pointed out.To improve the convergence speed and the diversity of differential evolutionary algorithm,by combining with the directed variation with the DE/rand/1 and DE/best/1,the SAMDDE is proposed.For constraint handling,the approximate infeasible solution is used to search the constrained boundary better.This algorithm performs well in benchmark test functions.Secondly,for multi-objective optimization,the Pareto domination and the external elite archive is commonly used.In this paper,the crowded entropy is proposed to revise the elite archive.To improve the stability of MODE,the self-adaptive control parameters is used to balance the global search and the local search.This new algorithm(MOSADDE)is compared with the NSGA-? and SPEA2 by test functions.Thirdly,to get better diversity,the normalized neighbor distance is proved to perform better than crowded entropy when revising the external elite archive.Combining with new self-adaptive parameters,the MOSADDE-? performs well in multi-objective benchmark test functions.0Finally,the optimization of hybrid electric vehicles is a nonlinear and strong constrained multi-objective optimization problem.For low consumption of fuel and emission load,this paper use the MOSADDE-? to optimize some components'parameters and control strategy variables,and provide the best compromise solution from the Pareto solution set.