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Colony search optimization algorithm using global optimization

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Abstract

This paper proposes a novel metaheuristic optimizer, named Colony Search Optimization Algorithm (CSOA). The algorithm mimics the social behavior of early humans. Early humans expanded their settlements in search of more livable places to live. In CSOA, the worst solution is used to escape from local optima. And the number of these redundant solutions’ updates is reduced to improve the performance of the algorithm. CSOA is tested with 26 mathematical optimization problems and 4 classical engineering optimization problems. The optimization results are compared with those of various optimization algorithms. The experimental results show that the CSOA is able to provide very competitive results on most of the tested problems. Then, a new effective method is provided for solving optimization problems.

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Acknowledgements

The author would like to thank anonymous reviewers for their constructive comments and suggestions.

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Heng Wen, Su Xin Wang, Fu Qiang Lu, Ming Feng, Lei Zhen Wang, Jun Kai Xiong & Ma Cong Si

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  1. Heng Wen
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  2. Su Xin Wang
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  3. Fu Qiang Lu
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  4. Ming Feng
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  5. Lei Zhen Wang
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  6. Jun Kai Xiong
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Correspondence to Heng Wen.

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Wen, H., Wang, S.X., Lu, F.Q. et al. Colony search optimization algorithm using global optimization. J Supercomput 78, 6567–6611 (2022). https://doi.org/10.1007/s11227-021-04127-2

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