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Metaheuristic research: a comprehensive survey

Abstract

Because of successful implementations and high intensity, metaheuristic research has been extensively reported in literature, which covers algorithms, applications, comparisons, and analysis. Though, little has been evidenced on insightful analysis of metaheuristic performance issues, and it is still a “black box” that why certain metaheuristics perform better on specific optimization problems and not as good on others. The performance related analyses performed on algorithms are mostly quantitative via performance validation metrics like mean error, standard deviation, and co-relations have been used. Moreover, the performance tests are often performed on specific benchmark functions—few studies are those which involve real data from scientific or engineering optimization problems. In order to draw a comprehensive picture of metaheuristic research, this paper performs a survey of metaheuristic research in literature which consists of 1222 publications from year 1983 to 2016 (33 years). Based on the collected evidence, this paper addresses four dimensions of metaheuristic research: introduction of new algorithms, modifications and hybrids, comparisons and analysis, and research gaps and future directions. The objective is to highlight potential open questions and critical issues raised in literature. The work provides guidance for future research to be conducted more meaningfully that can serve for the good of this area of research.

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Acknowledgements

The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM) for supporting this research under Postgraduate Incentive Research Grant, Vote No.U560. This work was supported in part by the National Natural Science Foundation of China under Grant 61672334, 61773119, and 61771297.

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Correspondence to Mohd Najib Mohd Salleh.

Appendix

Appendix

See Table 4.

Table 4 Acronyms of metaheuristics abbreviations used in this study

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Hussain, K., Mohd Salleh, M.N., Cheng, S. et al. Metaheuristic research: a comprehensive survey. Artif Intell Rev 52, 2191–2233 (2019). https://doi.org/10.1007/s10462-017-9605-z

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Keywords

  • Metaheuristic
  • Optimization
  • Global optimization
  • Swarm intelligence
  • Evolutionary algorithms