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Abstract. Seven up-to-date adaptive variants of differential evolution were compared in six benchmark problems of two levels of dimension (D = 30 and D = 100). The opposition-based optimization was also implemented to each adaptive variant and compared in experiments. It was found that all the algorithms perform very reliably in the problems of D = 30, whereas their reliability rate in the problems of D = 100 differs substantially among the test problems. Only two algorithms (JADE and b6e6rl variant of competitive DE) operate with acceptable reliability in all the problems. Considering the computational costs, the rank of the algorithms is different in various problems. When the average performance over all the problems is taken into account, JADE was the most efficient and b6e6rl the most reliable. The implementation of opposition-based optimization into adaptive variants of differential evolution does not increase the reliability and its positive influence on the efficiency is rare. Based on the results, recommendations to application of adaptive algorithms are formed and the source code of the algorithms is available online.

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This paper describes three different mechanisms used in Jellyfish Search (JS) optimiser. At first, an archive of good old solutions is used to prevent getting stuck in the local-optima area. Further, a distribution coefficient beta is adapted during the search process to control population diversity. Finally, an Eigen transformation of individuals in the reproduction process is used occasionally to cope with rotated functions. Three proposed variants of the JS optimiser are compared with the original JS algorithm and nine various well-known Nature-inspired optimisation methods when solving real-world problems of CEC 2011. Provided results achieved by statistical comparison show efficiency of the individual newly employed mechanisms.

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