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The performance of micro-population teaching-learning-based optimization(TLBO) algorithm is mainly depended on the population diversity. In order to further improve its performance, this paper proposed a micro-population teaching-learningbased optimization based on gene level diversity(MTLBO-GLD) . In the proposed algorithm, the pop ulation diversity was monitored at the genetic level, and the chaos search and the cosine function were used to improve the population diversity in different evolutionary stages. The proposed algorithm was compared with eight meta-heuristic algorithms ( including four micro-population algorithms and four non-micro-population algorithms) on 13 test functions . The experimental results show that the overall performance of the MTLBO-GLD is significantly better than that of compared algorithms. [ABSTRACT FROM AUTHOR]

To improve the capability of multi-objective differential algorithm, this paper proposed a multi-objective differential evolution algorithm based on self-adaptive strategy(MODE-SS). This algorithm not only used the hyper-volume to evaluate the performance of mutation strategies and selected a suitable mutation strategy automatically, but also employed binomial and simulated binary crossover strategies to balance global and local search capabilities. The results on 10 test functions show that MODE-SS outperforms the other six state-of-the-art multi-objective optimization algorithms. Finally, this paper used MODE-SS to solve the problem of energy consumption optimization of sea-rail intermodal transportation. The results can provide a set of available solutions to decision-makers. [ABSTRACT FROM AUTHOR]