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Multi-species Cuckoo Search Algorithm for Global Optimization

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Abstract

Many optimization problems in science and engineering are highly nonlinear and thus require sophisticated optimization techniques to solve. Traditional techniques such as gradient-based algorithms are mostly local search methods and often struggle to cope with such challenging optimization problems. Recent trends tend to use nature-inspired optimization algorithms. The standard cuckoo search (CS) is an optimization algorithm based on a single cuckoo species and a single host species. This work extends the standard CS by using the successful features of the cuckoo-host co-evolution with multiple interacting species. The proposed multi-species cuckoo search (MSCS) intends to mimic the co-evolution among multiple cuckoo species that compete for the survival of the fittest. The solution vectors are encoded as position vectors. The proposed algorithm is then validated by 15 benchmark functions as well as five nonlinear, multimodal case studies in practical applications. Simulation results suggest that the proposed algorithm can be effective for finding optimal solutions and all optimal solutions are achievable in the tested cases. The results for the test benchmarks are also compared with those obtained by other methods such as the standard cuckoo search and genetic algorithm. The comparison has demonstrated the efficiency of the present algorithm. Based on numerical experiments and case studies, we can conclude that the proposed algorithm can be more efficient in most cases. Therefore, the proposed approach can be a very effective tool for solving nonlinear global optimization problems.

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  1. http://archive.ics.uci.edu/ml/datasets/Iris

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Acknowledgements

The authors would like to thank the anonymous reviewers and editors for their constructive comments.

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Authors and Affiliations

  1. School of Science and Technology, Department of Design Engineering and Mathematics, Middlesex University, London, NW4 4BT, UK

    Xin-She Yang

  2. Ranchi, India

    Suash Deb

  3. Distinguished Professorial Associate, Decision Sciences and Modelling Program, Victoria University, Melbourne, Australia

    Suash Deb

  4. Department of Economics, North-Eastern Hill University, Shillong, India

    Sudhanshu K. Mishra

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  1. Xin-She Yang
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  2. Suash Deb
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  3. Sudhanshu K. Mishra
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Correspondence to Xin-She Yang.

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This research does not involve any use of animals and the research is in compliance with the ethical standards of the Journal.

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Yang, XS., Deb, S. & Mishra, S.K. Multi-species Cuckoo Search Algorithm for Global Optimization. Cogn Comput 10, 1085–1095 (2018). https://doi.org/10.1007/s12559-018-9579-4

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