A multi-distribution evolutionary algorithm with differential evolution (MDEA_DE) is proposed by incorporating the strong global convergence of distribution estimation algorithm and the fast convergence of differential evolution. To improve the global convergence ability, MDEA_DE employs a population-based multi-distribution evolution mechanism, and three Gaussian distributions are utilized to generate diverse population with solutions of high quality. Meanwhile, a search space regulation strategy is proposed to improve sampling precision of the Gaussian distributions, and local exploitation ability is enhanced by an improved differential evolution search in the solution space. Experimental results for selected benchmark problems demonstrate that MDEA_DE converges efficiently to the globally optimal solutions of complicated optimization problems by striking a good balance between global exploration and local exploitation.
XU Yongjian, CHEN Yu, XIE Chengwang
. A Multi-distribution Evolutionary Algorithm with Differential Evolution[J]. Journal of Applied Sciences, 2022
, 40(5)
: 727
-738
.
DOI: 10.3969/j.issn.0255-8297.2022.05.002
[1] Zhan Z H, Shi L, Tan K C, et al. A survey on evolutionary computation for complex continuous optimization[J]. Artificial Intelligence Review, 2022, 55(1):59-110.
[2] Liao T J, Stützle T, Dorigo M, Oca M A M D, et al. A unified ant colony optimization algorithm for continuous optimization[J]. European Journal of Operational Research, 2014, 234(3):597-609.
[3] Hu X M, Zhang J, Li Y. Orthogonal methods based ant colony search for solving continuous optimization problems[J]. Journal of Computer Science and Technology, 2008, 23(1):2-18.
[4] Yang X S, Deb S. Engineering optimisation by cuckoo search[J]. International Journal of Mathematical Modelling and Numerical Optimisation, 2010, 1(4):330-343.
[5] Peng H, Deng C S, Wu Z J. Best neighbor-guided artificial bee colony algorithm for continuous optimization problems[J]. Soft Computing, 2019, 23(18):8723-8740.
[6] Liang Y C, Juarez J R C. A novel metaheuristic for continuous optimization problems:virus optimization algorithm[J]. Engineering Optimization, 2016, 48(1):73-93.
[7] Deng W, Shang S F, Cai X, et al. An improved differential evolution algorithm and its application in optimization problem[J]. Soft Computing, 2021, 25(7):5277-5298.
[8] Zhang J Q, Sanderson A C. JADE:adaptive differential evolution with optional external archive[J]. IEEE Transactions on Evolutionary Computation, 2009, 13(5):945-958.
[9] Qin A K, Huang V L, Suganthan P N. Differential evolution algorithm with strategy adaptation for global numerical optimization[J]. IEEE Transactions on Evolutionary Computation, 2009, 13(2):398-417.
[10] Coelho L D S, Ayala H V H, Freire R Z. Population's variance-based adaptive differential evolution for real parameter optimization[C]//2013 IEEE Congress on Evolutionary Computation, 2013:1672-1677.
[11] Zhou A M, Sun J Y, Zhang Q F. An estimation of distribution algorithm with cheap and expensive local search methods[J]. IEEE Transactions on Evolutionary Computation, 2015, 19(6):807-822.
[12] Wang F, Li Y X, Zhou A M, et al. An estimation of distribution algorithm for mixed-variable newsvendor problems[J]. IEEE Transactions on Evolutionary Computation, 2020, 24(3):479-493.
[13] Yang Q, Chen W N, Li Y, et al. Multimodal estimation of distribution algorithms[J]. IEEE Transactions on Cybernetics, 2017, 47(3):636-650.
[14] 任志刚, 梁永胜, 张爱民, 等. 基于一般二阶混合矩的高斯分布估计算法[J]. 自动化学报, 2018, 44(4):635-645. Ren Z G, Liang Y S, Zhang A M, et al. A Gaussian estimation of distribution algorithm using general second-order mixed moment[J]. Acta Automatica Sinica, 2018, 44(4):635-645. (in Chinese)
[15] Liang Y S, Ren Z G, He M, et al. An efficient estimation of distribution algorithm with rank-one modification and population reduction[J]. Biosystems, 2019, 181:58-70.
[16] Kuo S Y, Chou Y H. Entanglement-enhanced quantum-inspired tabu search algorithm for function optimization[J]. IEEE Access, 2017, 5:13236-13252.
[17] Zhang R, Wang Z T, Zhang H J. Quantum-inspired evolutionary algorithm for continuous space optimization based on multiple chains encoding method of quantum bits[J]. Mathematical Problems in Engineering, 2014:620325.
[18] Mozaffari A, Emami M, Azad N L, et al. Comparisons of several variants of continuous quantum-inspired evolutionary algorithms[J]. Journal of Experimental & Theoretical Artificial Intelligence, 2017, 29(4):869-909.
[19] Gao H, Zhang R. Real-coded quantum evolutionary algorithm for global numerical optimization with continuous variables[J]. Chinese Journal of Electronics, 2011, 20(3):499-503.
[20] Razmjooy N, Ramezani M. An improved quantum evolutionary algorithm based on invasive weed optimization[J]. Indian Journal of Scientific Research, 2014, 4(2):413-422.
[21] Farnad B, Jafarian A, Baleanu D. A new hybrid algorithm for continuous optimization problem[J]. Applied Mathematical Modelling, 2018, 55:652-673.
[22] Aydilek B, Karacizmeli H, Tenekeci M E, et al. Using chaos enhanced hybrid firefly particle swarm optimization algorithm for solving continuous optimization problems[J]. Sadhana:Academy Proceedings in Engineering Science, 2021, 46(2):65-86.
[23] Zhou Y M, Duval B, Hao J K. Improving probability learning based local search for graph coloring[J]. Applied Soft Computing, 2018, 65:542-553.
