A sinusoidal differential evolution algorithm for numerical optimisation

Draa, Amer; Bouzoubia, Samira; Boukhalfa, Imene

doi:10.1016/j.asoc.2014.11.003

Cited by 188 publications

(63 citation statements)

References 33 publications

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“…The effectiveness of the proposed MLCC framework have been verified in previous subsections. In this subsection, the MLCCDE algorithm based on SHADE and IDE and the following parameter settings, is compared with eight well-known state-of-the-art and up-to-date DEs, namely, jDE [4], SaDE [29], EPSDE [22], JADE [46], CoDE [38], CoBiDE [39], SinDE [9] and MPEDE [44]. It is noted that MLCCDE uses different NP settings from those of MLCC-SI.…”

Section: Comparisons With State-of-the-art and Up-to-date Desmentioning

confidence: 99%

Multi-layer competitive-cooperative framework for performance enhancement of differential evolution

Zhang

Zheng

Tang

et al. 2019

Information Sciences

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Differential Evolution (DE) is recognized as one of the most powerful optimizers in the evolutionary algorithm (EA) family. Many DE variants were proposed in recent years, but significant differences in performances between them are hardly observed. Therefore, this paper suggests a multi-layer competitive-cooperative (MLCC) framework to facilitate the competition and cooperation of multiple DEs, which in turns, achieve a significant performance improvement. Unlike other multi-method strategies which adopt a multi-population based structure, with individuals only evolving in their corresponding subpopulations, MLCC implements a parallel structure with the entire population simultaneously monitored by multiple DEs assigned to their corresponding layers. An individual can store, utilize and update its evolution information in different layers based on an individual preference based layer selecting (IPLS) mechanism and a computational resource allocation bias (RAB) mechanism. In IPLS, individuals connect to only one favorite layer. While in RAB, high-quality solutions are evolved by considering all the layers. Thus DEs associated in the layers work in a competitive and cooperative manner. The proposed MLCC framework has been implemented on several highly competitive DEs. Experimental studies show that the MLCC variants significantly outperform the baseline DEs as well as several state-of-the-art and up-to-date DEs on CEC benchmark functions.

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Section: Comparisons With State-of-the-art and Up-to-date Desmentioning

confidence: 99%

Multi-layer competitive-cooperative framework for performance enhancement of differential evolution

Zhang

Zheng

Tang

et al. 2019

Information Sciences

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“…GA, FA, PSO and DE are used as comparison. DE has a good optimizing performance and has been employed to solve numerical optimization problems [12][13]. PSO having gained popularity shows significant performance in solving many problems and has been applied to wide applications [14][15][16][17][18].…”

Section: A Experiments Sets and Benchmark Functionsmentioning

confidence: 99%

A hybrid firefly algorithm

Zhu²,

Zhang

2017

2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

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“…For artificial test functions, CEC 2013 [20], the FPA and MGOFPA are compared to the Sinusoidal Differential Evolution algorithm [50], the self-adaptive differential evolution with pBX crossover (MDE-pBX) [78], the CMA-ES algorithm [79,80], the restart CMA evolution strategy with increasing population size (G-CMA-ES) [81] and against two recent variants of the PSO: the comprehensive learning particle swarm optimiser (CLPSO) [82] and the cooperatively coevolving particle swarms optimiser (CCPSO2) [83].…”

Section: Artificial Benchmarksmentioning

confidence: 99%

“…6, there is not enough evidence of the existence of difference between the nine algorithms. Table 19 summarises this comparison, with taking as reference the SinDE [50] algorithm. It is clear that the MGOFPA does not offer Table 19 Holm-Bonferroni procedure for real-world problems comparing: JADE, SPS-JADE, RBDE, SPS-RBDE, SadE, SPS-SadE, SHADE, SPS-SHADE, and MGOFPA algorithms.…”

Section: Mgofpa For Real-world Benchmarksmentioning

confidence: 99%

“…Comparison between the FPA and the state-of-the-art variants of DE: SinDE[50], SMADE[51], the DEcfbLS[52], the b6e6rl[53] and the TLBSade[54], for D = 30; average error, standard deviation of error, and Wilcoxon rank-sum statistical comparison.Please cite this article in press as: A. Draa, On the performances of the flower pollination algorithm -Qualitative and quantitative analyses, Appl. Soft Comput.…”

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confidence: 99%

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