Ensemble of parameters in a sinusoidal differential evolution with niching-based population reduction

Awad, Noor H.; Ali, Mostafa Z.; Suganthan, Ponnuthurai Nagaratnam

doi:10.1016/j.swevo.2017.09.009

Cited by 63 publications

(24 citation statements)

References 33 publications

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“…Content may change prior to final publication. 24,19,18,19,17,17,21,19,29,28,18,21,22,26 and 20 test problems, respectively, worse than them in 6, 10, 11, 8, 8, 9, 8, 10, 1, 1, 10, 9, 7, 4, and 9 test functions respectively. In regards to the Wilcoxon test, the proposed algorithm is statistically better than DE-VNS, sinDE, MPEDE, LSHADE, LSHADE44, CoDE, EPSDE, jDE, IDE, ERG-EFADE and SHADE, while there is no siginicance difference with AEPD-JADE, EDEV, iL-SHADE and jSO.…”

Section: G Testing Marlwcma On Higher Dimensionsmentioning

confidence: 92%

“…This is done by using a new ensemble sinusoidal mechanism that automatically tunes F and Cr values. Their proposed algorithm was called LSHADE-EpSin, which it was later enhanced by using a mixture of a Cauchy distribution and two sinusoidal formulas, a restart mechanism that is used at the later generations and a new way to adapt the population size [28]. Mohamed et al [29] proposed a new adaptation technique, semi-parameter adaptation approach, to tune the values of F and Cr in LSHADE algorithm.…”

Section: A De and Its Variantsmentioning

confidence: 99%

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Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

et al. 2020

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Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution algorithm and a co-variance matrix adaptation evolution strategy. In the former, reinforcement learning is used to automatically choose the best differential evolution operator. To judge the performance of the proposed framework, three benchmark sets of bound-constrained optimization problems (73 problems) with 10, 30 and 50 dimensions are solved. Further, the proposed algorithm has been tested by solving optimization problems with 100 dimensions taken from CEC2014 and CEC2017 benchmark problems. A real-world application data set has also been solved. Several experiments are designed to analyze the effects of different components of the proposed framework, with the best variant compared with a number of stateof-the-art algorithms. The experimental results show that the proposed algorithm is able to outperform all the others considered.

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Section: G Testing Marlwcma On Higher Dimensionsmentioning

confidence: 92%

Section: A De and Its Variantsmentioning

confidence: 99%

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

et al. 2020

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“…For rigorous performance verification, Hybrid DA-DE is compared with the following 10 DE-based algorithms (i.e. IMMSADE [5], AGDE [7], EFADE [9], MPEDE [11], EDEV [14], Rank-jDE [15], SHADE [16], Rcr-JADE [17], LSHADE-EpSin [18], EsDEr-NR [21]) and 6 non-DE-based algorithms (i.e. MFO [42], MVO [43], DA [34], ALO [44], WOA [45] and SCA [46]), which are tested on CEC2005, CEC2015 and CEC2017 benchmark functions.…”

Section: Parameter Settings and Involved Algorithmsmentioning

confidence: 99%

“…Wang and Zhao [20] proposed a differential evolution (DE) algorithm with self-adaptive population resizing mechanism (called SapsDE). Award and Ali [21] et al proposed ensemble sinusoidal differential evolution with niching-based population reduction (called EsDEr-NR).…”

Section: Introductionmentioning

confidence: 99%

Hybridizing Dragonfly Algorithm with Differential Evolution for Global Optimization

Duan

Yang

et al. 2019

IEICE Trans. Inf. & Syst.

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Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.

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“…Sun et al [19] adopted a novel Gaussian mutation operator and a modified common mutation operator to collaboratively produce new mutant vectors. Awad et al [20] proposed ensemble of parameters in a sinusoidal differential evolution with niching-based population reduction. EsDE r -NR uses a mixture of two sinusoidal formulas and a Cauchy distribution to balance the exploration and exploitation of best solutions ever found.…”

Section: Introductionmentioning

confidence: 99%

Elite Representative Based Individual Adaptive Regeneration Framework for Differential Evolution

Sun

Deng

et al. 2020

IEEE Access

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Differential evolution (DE) is a popular optimization algorithm which attracts numerous research studies and is applied widely. To address the problem of population stagnation in DE, this paper proposes an elite representative based individual adaptive regeneration framework (EIR) that can be incorporated into any DE variant easily. EIR is able to renovate stagnated individuals to regions centering around the weighted value of the individuals themselves and the elite representatives based on the Gaussian distribution with distinct crossover rates. Elite representatives are sampled from dynamically varied elite swarms for different individuals. A less greedy substitution mechanism considering the proximity of fitness values is introduced to avoid exclusion of some valuable information from inferior solutions. The whole regeneration and substitution mechanism is dynamically adapted according to the evolutionary process and the fitness ranking status of current individuals to balance between exploitation and exploration capability. Experimental results on CEC 2014 benchmark functions of 30D, 50D, 100D and two real world optimization problems show that the proposed EIR framework can significantly improve the performance of two standard DEs and six state-of-the-art DE algorithms.

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Ensemble of parameters in a sinusoidal differential evolution with niching-based population reduction

Cited by 63 publications

References 33 publications

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

Hybridizing Dragonfly Algorithm with Differential Evolution for Global Optimization

Elite Representative Based Individual Adaptive Regeneration Framework for Differential Evolution

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