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Review
. 2021 Jul 8;23(7):874.
doi: 10.3390/e23070874.

A Comparative Study of Common Nature-Inspired Algorithms for Continuous Function Optimization

Zhenwu Wang  1 , Chao Qin  1 , Benting Wan  2 , William Wei Song  2   3
Affiliations
Review

A Comparative Study of Common Nature-Inspired Algorithms for Continuous Function Optimization

Zhenwu Wang et al. Entropy (Basel). .

Abstract

Over previous decades, many nature-inspired optimization algorithms (NIOAs) have been proposed and applied due to their importance and significance. Some survey studies have also been made to investigate NIOAs and their variants and applications. However, these comparative studies mainly focus on one single NIOA, and there lacks a comprehensive comparative and contrastive study of the existing NIOAs. To fill this gap, we spent a great effort to conduct this comprehensive survey. In this survey, more than 120 meta-heuristic algorithms have been collected and, among them, the most popular and common 11 NIOAs are selected. Their accuracy, stability, efficiency and parameter sensitivity are evaluated based on the 30 black-box optimization benchmarking (BBOB) functions. Furthermore, we apply the Friedman test and Nemenyi test to analyze the performance of the compared NIOAs. In this survey, we provide a unified formal description of the 11 NIOAs in order to compare their similarities and differences in depth and a systematic summarization of the challenging problems and research directions for the whole NIOAs field. This comparative study attempts to provide a broader perspective and meaningful enlightenment to understand NIOAs.

Keywords: bio-inspired algorithm; black-box optimization benchmarking; meta-heuristic algorithm; nature-inspired algorithm; statistical test; swarm intelligence algorithm.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Number of papers published per year until 13 October 2020 (from Web of Science and Scopus databases).
Figure 2
Figure 2
Total number of papers published until 13 October 2020 (from Web of Science and Scopus databases).
Figure 3
Figure 3
The common process of NIOAs.
Figure 4
Figure 4
Topologies of 11 compared NIOAs.
Figure 5
Figure 5
Comparison of DE (control algorithm) against other compared algorithms using the Nemenyi test for the experimental results in 10-dimensional space under parameters I.
Figure 6
Figure 6
Comparison of DE (control algorithm) against other compared algorithms using the Nemenyi test for the experimental results in 10-dimensional space under parameters II.
Figure 6
Figure 6
Comparison of DE (control algorithm) against other compared algorithms using the Nemenyi test for the experimental results in 10-dimensional space under parameters II.
Figure 7
Figure 7
Comparison of DE (control algorithm) against other compared algorithms using the Nemenyi test for the experimental results in 50-dimensional space under parameters I.
Figure 8
Figure 8
Comparison of DE (control algorithm) against other compared algorithms using the Nemenyi test for the experimental results in 50-dimensional space under parameters II.
Figure 9
Figure 9
The design of tension/compression spring.
See this image and copyright information in PMC

References

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