| 新型群智能优化算法综述 |
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| 引用本文： | 林诗洁,董 晨,陈明志,张 凡,陈景辉.新型群智能优化算法综述[J].计算机工程与应用,2018,54(12):1-9. |
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| 作者姓名： | 林诗洁 董 晨 陈明志 张 凡 陈景辉 |
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| 作者单位： | 1.福州大学 数学与计算机科学学院，福州 350116
2.福州大学 网络系统信息安全福建省高校重点实验室，福州 350116 |
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| 摘 要： | 传统群智能算法在解决复杂实际多目标优化问题中存在不足，近年来学者提出诸多新型群智能算法，适用性强，在求解复杂实际问题中取得了较好的实验效果。以算法提出时间为主线，对新型群智能算法中细菌觅食优化算法、混合蛙跳算法、人工蜂群算法、萤火虫算法、布谷鸟搜索、果蝇优化算法和头脑风暴优化算法的改进及应用进行分析和综述，并对群智能算法未来的研究发展方向进行了探讨。
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| 关 键 词： | 细菌觅食优化 混合蛙跳算法 人工蜂群算法 萤火虫算法 布谷鸟搜索 果蝇优化算法 头脑风暴优化算法 |
Summary of new group intelligent optimization algorithms |
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| LIN Shijie,DONG Chen,CHEN Mingzhi,ZHANG Fan,CHEN Jinghui.Summary of new group intelligent optimization algorithms[J].Computer Engineering and Applications,2018,54(12):1-9. |
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| Authors: | LIN Shijie DONG Chen CHEN Mingzhi ZHANG Fan CHEN Jinghui |
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| Affiliation: | 1.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
2.Key Laboratory of Information Security of Network Systems, Fuzhou University, Fuzhou 350116, China |
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| Abstract: | Traditional swarm intelligent algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many new swarm intelligent algorithms with strong applicability and have achieved good experimental results in solving complex practical problems. In this paper, it summarizes new swarm intelligent algorithms including Bacterial Foraging Optimization（BFO）, Shuffled Frog Leaping Algorithm（SFLA）, Artificial Bee Colony（ABC）, Glowworm Swarm Optimization（GSO）, Cuckoo Search（CS）, Fruit Fly Optimization Algorithm（FOA） and Brain Storm Optimization（BSO）. Finally, further research direction about it will be discussed. |
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| Keywords: | bacterial foraging optimization shuffled frog leaping algorithm artificial bee colony glowworm swarm optimization cuckoo search fruit fly optimization algorithm brain storm optimization |
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