群智能优化算法最新进展

doi:10.3778/j.issn.1002-8331.2403-0328

摘要/Abstract

摘要： 群智能优化算法是一种模拟自然界中生物群体行为特征的优化算法，具有全局搜索能力强、适应性强、并行性强和易于实现的优点。群智能优化算法属于生物启发式算法，在解决复杂优化问题时，面临收敛速度、参数敏感性和鲁棒性的挑战。近年来，在群智能优化算法领域，研究者已经提出了一系列新型的群智能优化算法。综述了最新提出的六种群智能优化算法及其变体模型和应用，并在CEC2020测试函数上进行实验。全面评估了这六种群智能优化算法的收敛精度和稳定性，并简要阐述了群智能优化算法的未来发展趋势。

关键词: 群智能优化算法, 生物启发式算法, 收敛精度, 稳定性

Abstract: Swarm intelligent optimization algorithm is a kind of optimization algorithm that simulates the behavior characteristics of biological groups in nature. It has the advantages of strong global searching ability, strong adaptability, strong parallelism, and easy implementation. Swarm intelligent optimization algorithm is a bio-inspired algorithm, which faces the challenges of convergence speed, parameter sensitivity, and robustness when solving complex optimization problems. In recent years, in the field of swarm intelligence optimization algorithms, researchers have proposed a series of new swarm intelligence optimization algorithms. The newly proposed six-swarm intelligent optimization algorithms and its variant models and applications are reviewed, and experiments are carried out on CEC2020 test function. The convergence accuracy and stability of these six swarm intelligent optimization algorithms are evaluated comprehensively, and the future development trend of swarm intelligent optimization algorithms is briefly described.

Key words: swarm intelligent optimization algorithm, bio-inspired algorithm, convergence accuracy, stability

陈丽芳, 曹柯欣, 张思鹏, 白浩然, 韩阳, 代琪. 群智能优化算法最新进展[J]. 计算机工程与应用, 2024, 60(19): 46-67.

CHEN Lifang, CAO Kexin, ZHANG Sipeng, BAI Haoran, HAN Yang, DAI Qi. Recent Progress of Swarm Intelligent Optimization Algorithms[J]. Computer Engineering and Applications, 2024, 60(19): 46-67.

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