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

Zhenwu Wang¹, Chao Qin¹, Benting Wan²

¹Department of Computer Science and Technology, China University of Mining and Technology, Beijing 100083, China.

Entropy (Basel, Switzerland)

|August 6, 2021

Summary

This summary is machine-generated.

This study comprehensively compares 11 popular nature-inspired optimization algorithms (NIOAs) using 30 benchmarking functions. It reveals performance differences in accuracy, stability, and efficiency, offering insights for algorithm selection.

Keywords:

bio-inspired algorithm black-box optimization benchmarking meta-heuristic algorithm nature-inspired algorithm statistical test swarm intelligence algorithm

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Area of Science:

Computational Intelligence
Optimization Algorithms
Metaheuristics

Background:

Nature-inspired optimization algorithms (NIOAs) are widely used.
Existing comparative studies often focus on single algorithms.
A comprehensive comparison of popular NIOAs is lacking.

Purpose of the Study:

To conduct a comprehensive comparative and contrastive study of existing NIOAs.
To evaluate the performance of 11 popular NIOAs.
To provide a systematic overview of challenges and future research directions in the NIOAs field.

Main Methods:

Collected over 120 meta-heuristic algorithms, selecting 11 popular NIOAs.
Evaluated NIOA performance on 30 black-box optimization benchmarking (BBOB) functions.
Utilized Friedman and Nemenyi tests for statistical performance analysis.

Main Results:

Detailed comparative analysis of accuracy, stability, efficiency, and parameter sensitivity of 11 NIOAs.
Identified similarities and differences among the selected NIOAs through unified formal descriptions.
Statistical tests revealed significant performance variations among algorithms.

Conclusions:

The study provides a broader perspective on NIOA performance.
Offers meaningful enlightenment for understanding and selecting appropriate NIOAs.
Highlights key challenges and future research avenues for the NIOAs field.