Large scale continuous global optimization based on micro differential evolution with local directional search

Yildiz, Yunus Emre; Topal, Ali Osman

doi:10.1016/j.ins.2018.10.046

Cited by 27 publications

(18 citation statements)

References 27 publications

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“…In [11], a mDE algorithm with local search operator, called mDELS, is developed and applied for solving large-scale optimization problems. In [12], a mDE algorithm with a directional local search (called µDSDE) is proposed to solve largescale problems. In µDSDE, exploration is realized by reinitializing the worse individuals, while exploitation is performed through mutation, crossover and directional local search.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In µDSDE, exploration is realized by reinitializing the worse individuals, while exploitation is performed through mutation, crossover and directional local search. In [11,12], the local search technique increases the exploitation ability of the mDE algorithms. Meanwhile, it also suffers the risk of local convergence.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, mDE often suffers premature convergence and stagnation due to the lack of population diversity. To overcome the weakness, several modified mDE algorithms have been developed [8,9,10,11,12,13,14] .…”

Section: Introductionmentioning

confidence: 99%

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A vectorized bimodal distribution based micro differential evolution algorithm (VB-mDE)

Miao

Tianfield

2020

MGS

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Micro differential evolution (mDE) refers to algorithms that evolve with a small population to search for good solutions. Although mDEs are very useful for resource-constrained optimization tasks, the research on mDEs is still limited. In this paper, we propose a new mDE, i.e., vectorized bimodal distribution based mDE (called VB-mDE). The main idea is to employ a vectorized bimodal distribution parameter adjustment mechanism in mDE for performance enhancement. Specifically, in the VB-mDE, two important control parameters, i.e., scale factor F and crossover rate C⁢R, are adjusted by bimodal Cauchy distribution. At the same time, to increase the population diversity, the scale factor F is vectorized. The proposed VB-mDE is evaluated on the CEC2014 benchmark functions and compared with the state-of-the-art mDEs and normal DEs. The results show that the proposed VB-mDE has advantages in terms of solution accuracy and convergence speed.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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A vectorized bimodal distribution based micro differential evolution algorithm (VB-mDE)

Miao

Tianfield

2020

MGS

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“…Inspired by nature, a large variety of metaheuristic algorithms [1] have been proposed that provide optimal or near-optimal solutions to various complex large-scale problems that are difficult to solve using traditional techniques. Some of the many successful metaheuristic approaches include particle swarm optimization (PSO) [2,3], cooperative coevolution [4][5][6], seagull optimization algorithm [7], GRASP [8], clustering algorithm [9], and differential evolution (DE) [10,11], among others.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems

et al. 2019

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Inspired by the behavior of elephants in nature, elephant herd optimization (EHO) was proposed recently for global optimization. Like most other metaheuristic algorithms, EHO does not use the previous individuals in the later updating process. If the useful information in the previous individuals were fully exploited and used in the later optimization process, the quality of solutions may be improved significantly. In this paper, we propose several new updating strategies for EHO, in which one, two, or three individuals are selected from the previous iterations, and their useful information is incorporated into the updating process. Accordingly, the final individual at this iteration is generated according to the elephant generated by the basic EHO, and the selected previous elephants through a weighted sum. The weights are determined by a random number and the fitness of the elephant individuals at the previous iteration. We incorporated each of the six individual updating strategies individually into the basic EHO, creating six improved variants of EHO. We benchmarked these proposed methods using sixteen test functions. Our experimental results demonstrated that the proposed improved methods significantly outperformed the basic EHO.

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“…To adapt the classic covariance matrix adaptation evolution strategy algorithm to LSGO problems, Li et al [43] designed a Gaussian model with a low-rank covariance matrix and achieved desirable performance. As for differential evolution (DE), Yildiz et al [44] scaled it up to LSGO with a directional local search strategy and a reinitializing technique. Different from the above methods, some other researchers utilized dimensionality reduction techniques, such as random projection [45] and random embeddings [46], to narrow the high-dimensional solution space such that the optimizers for small and middle scale problems can be successfully employed in the reduced solution space.…”

mentioning

confidence: 99%

Bi-Hierarchical Cooperative Coevolution for Large Scale Global Optimization

et al. 2020

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Taking ''divide-and-conquer'' as a basic idea, cooperative coevolution (CC) has shown a promising prospect in large scale global optimization. However, its high requirement on the decomposition accuracy can hardly be satisfied in practice. Directing against this issue, this study proposes a bi-hierarchical cooperative coevolution (BHCC), which can tolerate a certain degree of decomposition error. Besides the cooperation among sub-problems as in the conventional CC, BHCC introduces a kind of cooperation between sub-problems and the overall problem. By systematically exploiting the excellent sub-solutions obtained during the sub-space optimization process, it initializes the population for the optimization process on the overall problem and thus can conduct search in promising regions of the whole solution space. The newly acquired complete solutions are in turn employed to update the context vector and the population of each sub-problem, where the context vector is used for sub-solution evaluation. Consequently, the search direction misdirected by an improper decomposition can be corrected to a great extent. To keep the balance between the two types of optimization processes, an adaptive triggering mechanism for the overall optimization process is specially designed for BHCC. Experimental results on two widely-used benchmark suites verify the effectiveness of the new strategies in BHCC and also indicate that BHCC is more robust than existing CCs and can achieve competitive performance compared with several state-of-the-art algorithms.

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Large scale continuous global optimization based on micro differential evolution with local directional search

Cited by 27 publications

References 27 publications

A vectorized bimodal distribution based micro differential evolution algorithm (VB-mDE)

A vectorized bimodal distribution based micro differential evolution algorithm (VB-mDE)

Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems

Bi-Hierarchical Cooperative Coevolution for Large Scale Global Optimization

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