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An archive-based artificial bee colony optimization algorithm for multi-objective continuous optimization problem

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Abstract

Research on multi-objective optimization (MO) has become one of the hot points of intelligent computation. In this paper, an archive-based multi-objective artificial bee colony optimization algorithm (AMOABC) is proposed, in which an external archive is used to preserve the current obtained non-dominated best solutions, and a novel Pareto local search mechanism is designed and incorporated into the optimization process. To prevent the searching process from being trapped into local minimum, a novel food source generating mechanism is put forward, and different search strategies are designed for bees and local search process. Comprehensive benchmarking and comparison of AMOABC with the some current-related MO algorithms demonstrate its effectiveness.

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Acknowledgements

This work was supported by the National Natural Science Foundation Program of China (61572116, 61572117, 61502089). Funding was provided by Special Fund for Fundamental Research of Central Universities of Northeastern University (Grant Nos. N150408001, N150404009).

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  1. College of Computer Science and Engineering, Northeastern University, Shenyang, 110819, People’s Republic of China

    Jiaxu Ning, Bin Zhang, Tingting Liu & Changsheng Zhang

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  1. Jiaxu Ning
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  2. Bin Zhang
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  3. Tingting Liu
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  4. Changsheng Zhang
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Correspondence to Changsheng Zhang.

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Ning, J., Zhang, B., Liu, T. et al. An archive-based artificial bee colony optimization algorithm for multi-objective continuous optimization problem. Neural Comput & Applic 30, 2661–2671 (2018). https://doi.org/10.1007/s00521-016-2821-7

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  • DOI: https://doi.org/10.1007/s00521-016-2821-7

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