Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

Li, Wei; Wang, Lei; Yao, Quanzhu; Jiang, Qiaoyong; Lei, Yu; Wang, Bin; Hei, Xinhong

doi:10.1155/2015/497398

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…Mostly, researchers have compared their newly introduced metaheuristics with PSO, GA, and ABC or their variants [70][71][72]). Other than these popular methods, ACO, DE, and evolutionary strategies (ES) or their variants have also been used for comparative analysis of results, see for example [73], [53], and [74].…”

Section: Rq4: What Analytical Techniques Have Been Used For Validatinmentioning

confidence: 99%

Metaheuristic research: a comprehensive survey

et al. 2018

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Section: Rq4: What Analytical Techniques Have Been Used For Validatinmentioning

confidence: 99%

Metaheuristic research: a comprehensive survey

et al. 2018

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show abstract

“…Interestingly, in both papers, the proposed algorithms were compared with GA and PSO in terms of mean and standard deviation values. The researches [14][15][16][17][18][19] also used ten test functions in experiments while introducing different metaheuristic algorithms with dimensions ranging from 2 to 30. For comparison with other algorithms, most of these works reported results in the form of mean and standard deviation values of best fitness values collected in 30 independent experiments.…”

Section: Benchmark Functions Used In Literaturementioning

confidence: 99%

Common Benchmark Functions for Metaheuristic Evaluation: A Review

Hussain

Salleh

Cheng

et al. 2017

JOIV : Int. J. Inform. Visualization

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Abstract-In literature, benchmark test functions have been used for evaluating performance of metaheuristic algorithms. Algorithms that perform well on a set of numerical optimization problems are considered as effective methods for solving real-world problems. Different researchers choose different set of functions with varying configurations, as there exists no standard or universally agreed test-bed. This makes hard for researchers to select functions that can truly gauge the robustness of a metaheuristic algorithm which is being proposed. This review paper is an attempt to provide researchers with commonly used experimental settings, including selection of test functions with different modalities, dimensions, the number of experimental runs, and evaluation criteria. Hence, the proposed list of functions, based on existing literature, can be handily employed as an effective test-bed for evaluating either a new or modified variant of any existing metaheuristic algorithm. For embedding more complexity in the problems, these functions can be shifted or rotated for enhanced robustness.

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“…Based on our previous work [27], a modified cloud particles differential evolution algorithm (MCPDE) is proposed.…”

Section: Modified Cloud Particles Differential Evolution Algorithmmentioning

confidence: 99%

A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization

2016

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Abstract:The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for real-parameter optimization. In contrast to the original Cloud Particles Differential Evolution (CPDE) algorithm, firstly, control parameters adaptation strategies are designed according to the quality of the control parameters. Secondly, the inertia factor is introduced to effectively keep a better balance between exploration and exploitation. Accordingly, this is helpful for maintaining the diversity of the population and discouraging premature convergence. In addition, the opposition mechanism and the orthogonal crossover are used to increase the search ability during the evolutionary process. Finally, CEC2013 contest benchmark functions are selected to verify the feasibility and effectiveness of the proposed algorithm. The experimental results show that the proposed MCPDE is an effective method for global optimization problems.

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Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

Cited by 4 publications

References 35 publications

Metaheuristic research: a comprehensive survey

Metaheuristic research: a comprehensive survey

Common Benchmark Functions for Metaheuristic Evaluation: A Review

A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization

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