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DE-FR: Differential Evolution Algorithm Based on DBSCAN-FR Clustering Method

Published: 23 April 2024 Publication History

Abstract

In recent years, evolutionary algorithms (EAs) have gained attention among scholars and have been applied to optimization engineering with various degrees of success. Concurrently, machine learning methods have rapidly developed in the field of artificial intelligence and have been increasingly integrated with other domains. This paper introduces a novel multi-population differential evolution algorithm called DE-FR, based on the proposed DBSCAN-FR clustering algorithm. This paper contributes to the improvement of the differential evolution algorithm in the following aspects. Firstly, it presents an enhanced clustering algorithm, DBSCAN-FR, which incorporates a forward distance filtering mechanism to divide the population into several groups successfully in high dimensional space. Secondly, it introduces a novel differential evolution algorithm named DE-FR, which builds upon the DBSCAN-FR clustering algorithm aims to solve complex single-objective optimization problems. Lastly, the proposed algorithm is compared with other classical differential evolution variants on CEC2014 benchmarks, and experimental results demonstrate its competitive performance.

References

[1]
Hao Chen, Ali Asghar Heidari, Huiling Chen, Mingjing Wang, Zhifang Pan, and Amir H. Gandomi. 2020. Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies. Future Generation Computer Systems 111 (2020), 175–198.
[2]
Ke Chen, Bing Xue, Mengjie Zhang, and Fengyu Zhou. 2022. Evolutionary Multitasking for Feature Selection in High-Dimensional Classification via Particle Swarm Optimization. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 26, 3 (JUN 2022), 446–460.
[3]
Swagatam Das, Sankha Subhra Mullick, and P.N. Suganthan. 2016. Recent advances in differential evolution – An updated survey. Swarm and Evolutionary Computation 27 (2016), 1–30.
[4]
Wu Deng, Junjie Xu, Yingjie Song, and Huimin Zhao. 2020. An effective improved co-evolution ant colony optimisation algorithm with multi-strategies and its application. International Journal of Bio-Inspired Computation 16, 3 (2020), 158–170.
[5]
Saber Elsayed, Ruhul Sarker, and Carlos A. Coello Coello. 2019. Fuzzy Rule-Based Design of Evolutionary Algorithm for Optimization. IEEE Transactions on Cybernetics 49, 1 (2019), 301–314.
[6]
Yi-Zeng Hsieh and Mu-Chun Su. 2016. A Q-learning-based swarm optimization algorithm for economic dispatch problem. Neural Computing and Applications 27, 8 (01 Nov 2016), 2333–2350.
[7]
Zhihui Li, Li Shi, Caitong Yue, Zhigang Shang, and Boyang Qu. 2019. Differential evolution based on reinforcement learning with fitness ranking for solving multimodal multiobjective problems. Swarm and Evolutionary Computation 49 (2019), 234–244.
[8]
Zhihui Li, Li Shi, Caitong Yue, Zhigang Shang, and Boyang Qu. 2019. Differential evolution based on reinforcement learning with fitness ranking for solving multimodal multiobjective problems. Swarm and Evolutionary Computation 49 (2019), 234–244.
[9]
A Kai Qin and Ponnuthurai N Suganthan. 2005. Self-adaptive differential evolution algorithm for numerical optimization. In 2005 IEEE congress on evolutionary computation, Vol. 2. IEEE, 1785–1791.
[10]
Shahryar Rahnamayan, Hamid R. Tizhoosh, and Magdy M. A. Salama. 2008. Opposition-Based Differential Evolution. IEEE Transactions on Evolutionary Computation 12, 1 (2008), 64–79.
[11]
Karam M Sallam, Saber M Elsayed, Ripon K Chakrabortty, and Michael J Ryan. 2020. Improved multi-operator differential evolution algorithm for solving unconstrained problems. In 2020 IEEE congress on evolutionary computation (CEC). IEEE, 1–8.
[12]
Adam Slowik and Halina Kwasnicka. 2020. Evolutionary algorithms and their applications to engineering problems. Neural Computing and Applications 32, 16 (01 Aug 2020), 12363–12379.
[13]
Ryoji Tanabe and Alex S Fukunaga. 2014. Improving the search performance of SHADE using linear population size reduction. In 2014 IEEE congress on evolutionary computation (CEC). IEEE, 1658–1665.
[14]
Yong Wang, Zixing Cai, and Qingfu Zhang. 2011. Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters. IEEE Transactions on Evolutionary Computation 15, 1 (FEB 2011), 55–66.
[15]
Zi-Jia Wang, Zhi-Hui Zhan, Ying Lin, Wei-Jie Yu, Hua Wang, Sam Kwong, and Jun Zhang. 2020. Automatic Niching Differential Evolution With Contour Prediction Approach for Multimodal Optimization Problems. IEEE Transactions on Evolutionary Computation 24, 1 (2020), 114–128.
[16]
Guohua Wu, Rammohan Mallipeddi, P.N. Suganthan, Rui Wang, and Huangke Chen. 2016. Differential evolution with multi-population based ensemble of mutation strategies. Information Sciences 329 (2016), 329–345.
[17]
Zhi-Hui Zhan, Lin Shi, Kay Chen Tan, and Jun Zhang. 2022. A survey on evolutionary computation for complex continuous optimization. Artificial Intelligence Review 55, 1 (01 Jan 2022), 59–110.
[18]
Jingqiao Zhang and Arthur C. Sanderson. 2009. JADE: Adaptive Differential Evolution With Optional External Archive. IEEE Transactions on Evolutionary Computation 13, 5 (OCT 2009), 945–958.

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ICCIP '23: Proceedings of the 2023 9th International Conference on Communication and Information Processing
December 2023
648 pages
ISBN:9798400708909
DOI:10.1145/3638884
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Association for Computing Machinery

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Published: 23 April 2024

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Author Tags

  1. clustering algorithm
  2. differential evolution
  3. evolutionary algorithm
  4. machine learning

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Overall Acceptance Rate 61 of 301 submissions, 20%

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