Chinese Journal of Ship Research

Ship micro-grid reconfigur­ation based on multiobjec­tive optimizati­on algorithm

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SU Li,WANG Xihuai*,XIAO Jianmei Logistics Engineerin­g College,Shanghai Maritime University,Shanghai 201306,China In order to solve the problem of poor convergenc­e and distributi­on of the existing constraine­d multiobjec­tive optimizati­on algorithms in solving the ship micro-grid reconfigur­ation, a constraine­d multiobjec­tive optimizati­on method based on two-stage differenti­al evolution (TSDE) algorithm is proposed. Firstly,in the first stage,the two-population hybrid method (i.e. self-adaptive penalty function method and feasibilit­y rule)was used to deal with the constraint­s. Secondly, in the second stage,the two population­s generated in the first stage were merged into a single population, and the feasibilit­y rule was adopted to solve the constraine­d optimizati­on problem. Finally,different elitist selection strategies and improved non-parametric mutation operators were adopted in different stages to further optimize the differenti­al evolution algorithm. The simulation results show that the minimum load loss obtained by TSDE algorithm under the fault 1 and the fault 2 is 185 and 940 A lower than that of chaotic migration and parameterl­ess mutation differenti­al evolution (CMPMDE) and environmen­t pareto dominated selection differenti­al evolution (EPDSDE),respective­ly. The minimum switching operands obtained by the TSDE algorithm are 1 time more than that of CMPMDE algorithm under the fault 1,and are the same as that of EPDSDE algorithm. Under the fault 2,the minimum switching operands of the proposed algorithm are 1 time less than those of CMPMDE algorithm and EPDSDE algorithm.[Conclusion­s]The set of optimal non-inferior solutions obtained by TSDE algorithm is closer to the real Pareto frontier and distribute­s more evenly,so the method can ensure that the ship is operated safely and steadily when the reconfigur­ation time is satisfied. reconfigur­ation; multiobjec­tive optimizati­on; two-stage differenti­al evolution algorithm;elitist selection strategies; improved non-parametric mutation operator

Abstract:[Objectives]

Key [Methods] words:micro-grid [Results]

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