Grey wolf optimizer with cellular topological structure

Lu, Chao; Gao, Liang; Yi, Jin

doi:10.1016/j.eswa.2018.04.012

Cited by 127 publications

(42 citation statements)

References 54 publications

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“…Its fitness function is given in (14). = ( )= 10 * 10 (255 2 /MSE) (14) The mean square error is given in (15) .…”

Section: Ga Based Optimization Techniquementioning

confidence: 99%

“…GWO [15] is a swarm intelligence algorithm developed by Mirjalili imitating the natural characteristics shown by grey wolf. The grey wolves survives as groups and are categorized into four groups: α (alpha), the highest authority that is responsible for taking decisions, β (beta) that support in taking decisions, ω(omega) that submit to all other wolves and δ that dominateω and report to α and β.…”

Section: Grey Wolf Optimization Techniquementioning

confidence: 99%

See 1 more Smart Citation

Denoising Images by Dual–Tree Complex Wavelet Transform Combined With Meta Heuristic Optimization Algorithms

Lavanya*,

Narasimhulu,

Prasad

2020

IJITEE

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Denoising is a prime objective technique for processing images. Image denoising techniques removes the noises present in an image without interrupting its features and contents. The image gets interrupted by channel or processing noise depending on the applications. Thus, the contaminated noises produce degradable image qualities with respect to subjective and objective approach. To overcome this, image denoising approaches were suggested. In the present research, Dual–Tree Complex Wavelet transform (DTCWT) is utilized to achieve image denoising since they perform multi resolution decomposition by two DWT trees. Soft and hard thresholding methods are used to threshold wavelet coefficients. The present research proposes a novel technique to denoise images which gives image information clearly by thresholding and optimization technique. The optimization is carried through different Meta-heuristic optimization Algorithms Genetic Algorithm (GA) and Grey-wolf optimization (GWO) algorithm. Optimization of threshold value is performed after Bayesian method and the observed output produces better results when compared to other techniques involving Visu shrink, Sure shrink and Bayes shrinkbased on peak signal to noise ratio (PSNR) and visual qualities.

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“…Its fitness function is given in (14). = ( )= 10 * 10 (255 2 /MSE) (14) The mean square error is given in (15) .…”

Section: Ga Based Optimization Techniquementioning

confidence: 99%

Section: Grey Wolf Optimization Techniquementioning

confidence: 99%

Denoising Images by Dual–Tree Complex Wavelet Transform Combined With Meta Heuristic Optimization Algorithms

Lavanya*,

Narasimhulu,

Prasad

2020

IJITEE

View full text Add to dashboard Cite

show abstract

“…The GWO algorithm is considered for learning method due to its advantages, including high accuracy, effectiveness, and competitiveness [17]. The paramount challenge in GWO is that it is prone to stagnation in local optima [18]. Differential evolution (DE) as a popular stochastic optimizer, proposed by Storn and Price [19], is to exhibit consistent and reliable performance in nonlinear and multimodal environment and has proven to be effective for constrained optimization problems [20].…”

Section: Introductionmentioning

confidence: 99%

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Sun

Chen

et al. 2019

Complexity

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Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.

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“…Compared with single-solution-based algorithms, the research and applications of population-based algorithms are more extensive because of the following three main advantages [11,12]: more information can be obtained to guide the trial solutions toward a promising area within the search space by a set of trial solutions; local optimization can be e ectively avoided because of the interaction of a set of trial solutions; and in terms of exploration ability, population-based heuristic algorithms are superior to singlesolution-based heuristic algorithms. e genetic algorithm (GA) is used to address the characterization of hyperelastic materials [12,13]. Particle swarm optimization (PSO) is attributed to improving and evaluating the performance of automated engineering design optimization [13,14].…”

Section: Introductionmentioning

confidence: 99%

Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information

Jia

Yun

2019

Complexity

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The grey wolf optimizer (GWO) algorithm is a recently developed, novel, population-based optimization technique that is inspired by the hunting mechanism of grey wolves. The GWO algorithm has some distinct advantages, such as few algorithm parameters, strong global optimization ability, and ease of implementation on a computer. However, the paramount challenge is that there are some cases where the GWO is prone to stagnation in local optima. This drawback of the GWO algorithm may be attributed to an insufficiency in its position-updated equation, which disregards the positional interaction information about the three best grey wolves (i.e., the three leaders). This paper proposes an improved version of the GWO algorithm that is based on a dynamically dimensioned search, spiral walking predation technique, and positional interaction information (referred to as the DGWO). In addition, a nonlinear control parameter strategy, i.e., the control parameter that is nonlinearly increased with an increase in iterations, is designed to balance the exploration and exploitation of the GWO algorithm. The experimental results for 23 general benchmark functions and 3 well-known engineering optimization design applications validate the effectiveness and feasibility of the proposed DGWO algorithm. The comparison results for the 23 benchmark functions show that the proposed DGWO algorithm performs significantly better than the GWO and its improved variant for most benchmarks. The DGWO provides the highest solution precision, strongest robustness, and fastest convergence rate among the compared algorithms in almost all cases.

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Grey wolf optimizer with cellular topological structure

Cited by 127 publications

References 54 publications

Denoising Images by Dual–Tree Complex Wavelet Transform Combined With Meta Heuristic Optimization Algorithms

Denoising Images by Dual–Tree Complex Wavelet Transform Combined With Meta Heuristic Optimization Algorithms

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information

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