关键词: 多目标优化, 差分进化算法, 隐马尔可夫链, 海铁联运, 能耗优化

Abstract:

The performance of multi-objective differential evolution algorithm is significantly influenced by its parameter settings and generation strategies（crossover and mutation）. To implement real-time adaptive adjustment of control para-
meters and mutation strategies, a self-adaptive multi-objective differential evolution algorithm based on hidden Markov chain is proposed in the current study. A hidden Markov model is used to analyze the population information and then the optimal sequence is obtained. By comparing the optimal sequence with the actual state sequence, the maximum likelihood estimation values of mutation scaling factor[F]and crossover probability [CR] are obtained to automatically generate suitable parameters. Moreover, a set of strategy chains are obtained by a hidden Markov model to assist multi-objective differential evolution algorithm in selecting an appropriate mutation strategy. Compared with other nine multi-objective evolutionary algorithms on 16 test functions, the results show that the overall performance of the proposed algorithm is better than that of other compared algorithms. Finally, the algorithm is applied to solve the energy consumption optimization problem of sea-rail intermodal transportation, and the obtained results can provide different feasible schemes for decision makers.

Key words: multi-objective optimization, differential evolution algorithm, hidden Markov chain, sea-rail intermodal transportation, energy consumption optimization