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Kumar P, Ali M. SaMDE: A Self Adaptive Choice of DNDE and SPIDE Algorithms with MRLDE. Biomimetics (Basel) 2023; 8:494. [PMID: 37887625 PMCID: PMC10603870 DOI: 10.3390/biomimetics8060494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 10/08/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Differential evolution (DE) is a proficient optimizer and has been broadly implemented in real life applications of various fields. Several mutation based adaptive approaches have been suggested to improve the algorithm efficiency in recent years. In this paper, a novel self-adaptive method called SaMDE has been designed and implemented on the mutation-based modified DE variants such as modified randomized localization-based DE (MRLDE), donor mutation based DE (DNDE), and sequential parabolic interpolation based DE (SPIDE), which were proposed by the authors in previous research. Using the proposed adaptive technique, an appropriate mutation strategy from DNDE and SPIDE can be selected automatically for the MRLDE algorithm. The experimental results on 50 benchmark problems taken of various test suits and a real-world application of minimization of the potential molecular energy problem validate the superiority of SaMDE over other DE variations.
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Affiliation(s)
| | - Musrrat Ali
- Department of Basic Sciences, PYD, King Faisal University, Al Ahsa 31982, Saudi Arabia
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Meng Z, Song Z, Shao X, Zhang J, Xu H. FD-DE: Differential Evolution with fitness deviation based adaptation in parameter control. ISA TRANSACTIONS 2023; 139:272-290. [PMID: 37230905 DOI: 10.1016/j.isatra.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 05/10/2023] [Accepted: 05/10/2023] [Indexed: 05/27/2023]
Abstract
Differential Evolution (DE) is arguably one of the most powerful stochastic optimization algorithms for different optimization applications, however, even the state-of-the-art DE variants still have many weaknesses. In this study, a new powerful DE variant for single-objective numerical optimization is proposed, and there are several contributions within it: First, an enhanced wavelet basis function is proposed to generate scale factor F of each individual in the first stage of the evolution; Second, a hybrid trial vector generation strategy with perturbation and t-distribution is advanced to generate different trial vectors regarding different stages of the evolution; Third, a fitness deviation based parameter control is proposed for the adaptation of control parameters; Fourth, a novel diversity indicator is proposed and a restart scheme can be launched if necessary when the quality of the individuals is detected bad. The novel algorithm is validated using a large test suite containing 130 benchmarks from the universal test suites on single-objective numerical optimization, and the results approve the big improvement in comparison with several well-known state-of-the-art DE variants. Moreover, our algorithm is also validated under real-world optimization applications, and the results also support its superiority.
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Affiliation(s)
- Zhenyu Meng
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China; Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China.
| | - Zhenghao Song
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China
| | - Xueying Shao
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China
| | - Junyuan Zhang
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China
| | - Huarong Xu
- Department of Computer Science and Technology, Xiamen University of Technology, Xiamen, China
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Ghasemi M, Zare M, Trojovský P, Zahedibialvaei A, Trojovská E. A hybridizing-enhanced differential evolution for optimization. PeerJ Comput Sci 2023; 9:e1420. [PMID: 37346618 PMCID: PMC10280462 DOI: 10.7717/peerj-cs.1420] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/08/2023] [Indexed: 06/23/2023]
Abstract
Differential evolution (DE) belongs to the most usable optimization algorithms, presented in many improved and modern versions in recent years. Generally, the low convergence rate is the main drawback of the DE algorithm. In this article, the gray wolf optimizer (GWO) is used to accelerate the convergence rate and the final optimal results of the DE algorithm. The new resulting algorithm is called Hunting Differential Evolution (HDE). The proposed HDE algorithm deploys the convergence speed of the GWO algorithm as well as the appropriate searching capability of the DE algorithm. Furthermore, by adjusting the crossover rate and mutation probability parameters, this algorithm can be adjusted to pay closer attention to the strengths of each of these two algorithms. The HDE/current-to-rand/1 performed the best on CEC-2019 functions compared to the other eight variants of HDE. HDE/current-to-best/1 is also chosen as having superior performance to other proposed HDE compared to seven improved algorithms on CEC-2014 functions, outperforming them in 15 test functions. Furthermore, jHDE performs well by improving in 17 functions, compared with jDE on these functions. The simulations indicate that the proposed HDE algorithm can provide reliable outcomes in finding the optimal solutions with a rapid convergence rate and avoiding the local minimum compared to the original DE algorithm.
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Affiliation(s)
- Mojtaba Ghasemi
- Department of Electronics and Electrical Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Mohsen Zare
- Department of Electrical Engineering, Jahrom University, Jahrom, Iran
| | - Pavel Trojovský
- Department of Mathematics, Faculty of Science, University of Hradec Králové, Hradec Kralove, Czech Republic
| | - Amir Zahedibialvaei
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Eva Trojovská
- Department of Mathematics, Faculty of Science, University of Hradec Králové, Hradec Kralove, Czech Republic
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An evolutionary-state-based selection strategy for enhancing differential evolution algorithm. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.10.111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Improved spherical search with local distribution induced self-adaptation for hard non-convex optimization with and without constraints. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.09.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Liu D, Hu Z, Su Q. Neighborhood-based differential evolution algorithm with direction induced strategy for the large-scale combined heat and power economic dispatch problem. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.09.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Zeng Z, Zhang M, Zhang H, Hong Z. Improved differential evolution algorithm based on the sawtooth-linear population size adaptive method. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Li Y, Han T, Zhou H, Tang S, Zhao H. A novel adaptive L-SHADE algorithm and its application in UAV swarm resource configuration problem. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.05.058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Liang Q, Chu SC, Yang Q, Liang A, Pan JS. Multi-Group Gorilla Troops Optimizer with Multi-Strategies for 3D Node Localization of Wireless Sensor Networks. SENSORS 2022; 22:s22114275. [PMID: 35684896 PMCID: PMC9185536 DOI: 10.3390/s22114275] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 05/23/2022] [Accepted: 05/31/2022] [Indexed: 01/27/2023]
Abstract
The localization problem of nodes in wireless sensor networks is often the focus of many researches. This paper proposes an opposition-based learning and parallel strategies Artificial Gorilla Troop Optimizer (OPGTO) for reducing the localization error. Opposition-based learning can expand the exploration space of the algorithm and significantly improve the global exploration ability of the algorithm. The parallel strategy divides the population into multiple groups for exploration, which effectively increases the diversity of the population. Based on this parallel strategy, we design communication strategies between groups for different types of optimization problems. To verify the optimized effect of the proposed OPGTO algorithm, it is tested on the CEC2013 benchmark function set and compared with Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA) and Artificial Gorilla Troops Optimizer (GTO). Experimental studies show that OPGTO has good optimization ability, especially on complex multimodal functions and combinatorial functions. Finally, we apply OPGTO algorithm to 3D localization of wireless sensor networks in the real terrain. Experimental results proved that OPGTO can effectively reduce the localization error based on Time Difference of Arrival (TDOA).
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Affiliation(s)
- Qingwei Liang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (Q.L.); (S.-C.C.); (Q.Y.)
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (Q.L.); (S.-C.C.); (Q.Y.)
| | - Qingyong Yang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (Q.L.); (S.-C.C.); (Q.Y.)
| | - Anhui Liang
- College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China;
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (Q.L.); (S.-C.C.); (Q.Y.)
- Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China
- Correspondence:
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Meng Z, Zhong Y, Yang C. CS-DE: Cooperative Strategy based Differential Evolution with population diversity enhancement. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.07.080] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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