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Improving the one-position inheritance artificial bee colony algorithm using heuristic search mechanisms

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Abstract

The artificial bee colony algorithm with one-position inheritance (OPIABC) has shown good performance for large-scale problems. But, the improvement in its performance for some other type test problem is not obvious, since the onlookers in this algorithm use the foraging strategy that randomly selects a neighbor to produce a new candidate. Moreover, the scout foraging behavior in this algorithm is completely random, which would sometimes make it consume more search efforts to discover some promising area and hamper its convergent speed especially for large-scale optimization. To further improve its performance, a running information-guided onlooker foraging strategy and a heuristic scout search mechanism are designed and combined with it. The improved OPIABC algorithm has been tested on a set of test functions with dimensions D = 30, 100 and 1000. Experimental results show that after using the heuristic search mechanisms, the performance of the OPIABC algorithm is significantly improved for most test problems.

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Acknowledgements

This work was supported by the National Natural Science Foundation Program of China (61572116, 61572117, 61502089) and the Special Fund for Fundamental Research of Central Universities of Northeastern University (N161606003, N150408001, N150404009).

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Authors and Affiliations

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

    Jiaxu Ning, Changsheng Zhang, Bin Zhang & Peng Wang

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  1. Jiaxu Ning
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  2. Changsheng Zhang
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  3. Bin Zhang
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  4. Peng Wang
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Correspondence to Bin Zhang.

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Ning, J., Zhang, C., Zhang, B. et al. Improving the one-position inheritance artificial bee colony algorithm using heuristic search mechanisms. Soft Comput 24, 1271–1281 (2020). https://doi.org/10.1007/s00500-019-03964-x

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  • DOI: https://doi.org/10.1007/s00500-019-03964-x

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