摘要
This paper proposes a combination weighting(CW)model based on iMOEA/D-DE(i.e.,improved multiobjective evolutionary algorithm based on decomposition with differential evolution)with the aim to accurately compute the weight of evaluation methods.Multi-expert weight considers only subjective weights,leading to poor objectivity.To overcome this shortcoming,a multiobjective optimization model of CW based on improved game theory is proposed while considering the uncertainty of combination coefficients.An improved mutation operator is introduced to improve the convergence speed,and thus better optimization results are obtained.Meanwhile,an adaptive mutation constant and crossover probability constant with self-learning ability are proposed to improve the robustness of MOEA/D-DE.Since the existing weight evaluation approaches cannot evaluate weights separately,a new weight evaluation approach based on relative entropy is presented.Taking the evaluation method of integrated navigation systems as an example,certain experiments are carried out.It is proved that the proposed algorithm is effective and has excellent performance.
基金
supported by the National Natural Science Foundation of China(Nos.61633008,61773132,and 61803115)
the 7th Generation Ultra Deep Water Drilling Unit Innovation Project Sponsored by Chinese Ministry of Industry and Information Technology,the Heilongjiang Provincial Science Fund for Distinguished Young Scholars,China(No.JC2018019)
the Fundamental Research Funds for the Central Universities,China(No.HEUCFP201768)。
