TY - JOUR
T1 - Chaotic Local Search-Based Differential Evolution Algorithms for Optimization
AU - Gao, Shangce
AU - Yu, Yang
AU - Wang, Yirui
AU - Wang, Jiahai
AU - Cheng, Jiujun
AU - Zhou, Mengchu
N1 - Funding Information:
Manuscript received September 27, 2019; accepted November 23, 2019. Date of publication December 20, 2019; date of current version May 18, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61673403 and Grant 61872271, and in part by Japan Society for the Promotion of Science KAKENHI under Grant JP17K12751. This article was recommended by Associate Editor L. Wang. (Corresponding authors: Jiujun Cheng; MengChu Zhou.) S. Gao, Y. Yu, and Y. Wang are with the Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan (e-mail: gaosc@eng.u-toyama.ac.jp; ntrqz@hotmail.com; wyr607@foxmail.com).
Publisher Copyright:
© 2013 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - JADE is a differential evolution (DE) algorithm and has been shown to be very competitive in comparison with other evolutionary optimization algorithms. However, it suffers from the premature convergence problem and is easily trapped into local optima. This article presents a novel JADE variant by incorporating chaotic local search (CLS) mechanisms into JADE to alleviate this problem. Taking advantages of the ergodicity and nonrepetitious nature of chaos, it can diversify the population and thus has a chance to explore a huge search space. Because of the inherent local exploitation ability, its embedded CLS can exploit a small region to refine solutions obtained by JADE. Hence, it can well balance the exploration and exploitation in a search process and further improve its performance. Four kinds of its CLS incorporation schemes are studied. Multiple chaotic maps are individually, randomly, parallelly, and memory-selectively incorporated into CLS. Experimental and statistical analyses are performed on a set of 53 benchmark functions and four real-world optimization problems. Results show that it has a superior performance in comparison with JADE and some other state-of-the-art optimization algorithms.
AB - JADE is a differential evolution (DE) algorithm and has been shown to be very competitive in comparison with other evolutionary optimization algorithms. However, it suffers from the premature convergence problem and is easily trapped into local optima. This article presents a novel JADE variant by incorporating chaotic local search (CLS) mechanisms into JADE to alleviate this problem. Taking advantages of the ergodicity and nonrepetitious nature of chaos, it can diversify the population and thus has a chance to explore a huge search space. Because of the inherent local exploitation ability, its embedded CLS can exploit a small region to refine solutions obtained by JADE. Hence, it can well balance the exploration and exploitation in a search process and further improve its performance. Four kinds of its CLS incorporation schemes are studied. Multiple chaotic maps are individually, randomly, parallelly, and memory-selectively incorporated into CLS. Experimental and statistical analyses are performed on a set of 53 benchmark functions and four real-world optimization problems. Results show that it has a superior performance in comparison with JADE and some other state-of-the-art optimization algorithms.
KW - Chaotic local search (CLS)
KW - chaotic map
KW - differential evolution (DE)
KW - incorporation scheme
KW - optimization algorithm
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U2 - 10.1109/TSMC.2019.2956121
DO - 10.1109/TSMC.2019.2956121
M3 - Article
AN - SCOPUS:85106495329
SN - 2168-2216
VL - 51
SP - 3954
EP - 3967
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 6
M1 - 8937719
ER -