Automatic Niching Differential Evolution With Contour Prediction Approach for Multimodal Optimization Problems

Zi-Jia WANG, Zhi-Hui ZHAN, Ying LIN, Wei-Jie YU, Hua WANG, Sam KWONG, Jun ZHANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

154 Citations (Scopus)


Niching techniques have been widely incorporated into evolutionary algorithms for solving multimodal optimization problems (MMOPs). However, most of the existing niching techniques are either sensitive to the niching parameters or require extra fitness evaluations (FEs) to maintain the niche detection accuracy. In this paper, we propose a new automatic niching technique based on the affinity propagation clustering (APC) and design a novel niching differential evolution (DE) algorithm, termed as automatic niching DE (ANDE), for solving MMOPs. In the proposed ANDE algorithm, APC acts as a parameter-free automatic niching method that does not need to predefine the number of clusters or the cluster size. Also, it can facilitate locating multiple peaks without extra FEs. Furthermore, the ANDE algorithm is enhanced by a contour prediction approach (CPA) and a two-level local search (TLLS) strategy. Firstly, the CPA is a predictive search strategy. It exploits the individual distribution information in each niche to estimate the contour landscape, and then predicts the rough position of the potential peak to help accelerate the convergence speed. Secondly, the TLLS is a solution refine strategy to further increase the solution accuracy after the CPA roughly predicting the peaks. Compared with other state-of-the-art DE and non-DE multimodal algorithms, even the winner of competition on multimodal optimization, the experimental results on 20 widely used benchmark functions illustrate the superiority of the proposed ANDE algorithm.
Original languageEnglish
Pages (from-to)114-128
JournalIEEE Transactions on Evolutionary Computation
Issue number1
Early online date11 Apr 2019
Publication statusPublished - Feb 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the Outstanding Youth Science Foundation under Grant 61822602, in part by the National Natural Science Foundations of China under Grant 61772207 and Grant 61873097, in part by the Natural Science Foundation of Guangdong Province (NSFGD) for Distinguished Young Scholars under Grant 2014A030306038, in part by the Project for Pearl River New Star in Science and Technology under Grant 201506010047, in part by GDUPS (2016), in part by NSFGD under Grant 2014B050504005, and in part by the Hong Kong GRF-RGC General Research Fund (9042489) under Grant CityU 11206317.


  • Affinity propagation clustering (APC)
  • contour prediction approach (CPA)
  • differential evolution (DE)
  • multimodal optimization problems (MMOPs)
  • niching techniques


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