An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold algorithm

Dhawale, Dinesh; Kamboj, Vikram Kumar; Anand, Priyanka

doi:10.1007/s00366-021-01409-4

Cited by 28 publications

(37 citation statements)

References 134 publications

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“…To perform the experiments six problems (benchmarks), which are commonly used in the literature, are selected. See for instance the following references [18,22,23,56]. The selected problems are of different dimensions and contains several types of transcendental functions, for instance: polynomials and trigonometric functions.…”

Section: Methodsmentioning

confidence: 99%

“…In their next work, Nandi and Kamboj [54] hybridized the HHO with sine-cosine algorithm (SCA) using memetic algorithm approach to solve the photovoltaic constrained UCP of an electric power system. Other works have been proposed by improving different optimization algorithms, including SMA algorithm, to solve different numerical and engineering design challenges such as in [55] and [56].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Approach for Solving Systems of Nonlinear Equations Using Harris Hawks Optimization and Newton’s Method

et al. 2021

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Systems of nonlinear equations are known as the basis for many models of engineering and data science, and their accurate solutions are very critical in achieving progress in these fields. However, solving a system with multiple nonlinear equations, usually, is not an easy task. Consequently, finding a robust and accurate solution can be a very challenging problem in complex systems. In this work, a novel hybrid method namely Newton-Harris hawks optimization (NHHO) for solving systems of nonlinear equations is proposed. The proposed NHHO combines Newton's method, with a second-order convergence where the correct digits roughly double in every step, and the Harris hawks optimization (HHO) to enhance the search mechanism, avoid local optima, improve convergence speed, and find more accurate solutions. We tested a group of six well-known benchmark systems of nonlinear equations to evaluate the efficiency of NHHO. Further, comparisons between NHHO and other optimization algorithms, including the original HHO algorithm, Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Butterfly Optimization Algorithm (BOA), and Equilibrium Optimization (EO) were performed. The norm of the equation system was calculated as a fitness function to measure the optimization algorithms' performance. A solution with less fitness value is considered a better solution. Furthermore, the experimental results confirmed the superiority of NHHO over the other optimization algorithms, in the comparisons, in different aspects, including best solution, average fitness value, and convergence speed. Accordingly, the proposed NHHO is powerful and more effective in all benchmark problems in solving systems of nonlinear equations compared to the other optimization algorithms. Finally, NHHO overcomes the limitations of Newton's method, including selecting the initial point and divergence problems.INDEX TERMS Hybridization; Harris hawks optimization; Newton's method; optimization; systems of nonlinear equations.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Solving Systems of Nonlinear Equations Using Harris Hawks Optimization and Newton’s Method

et al. 2021

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“…They are widely employed to solve many types of optimization problems. The number of studies using meta‐heuristics (MHs) to successfully solve real‐world optimization like shape design, topology optimization, manufacturing problems has been steadily growing over the last few decades (Abderazek et al, 2019; Abedinpourshotorban et al, 2016; Abualigah et al, 2021; Cheraghalipour et al, 2018; Coello Coello, 2000; Coello Coello, 2002; Coello Coello & Montes, 2002; Deb, 2000; Devaney, 1987; Dhawale et al, 2021a; Eberhart & Kennedy, 1995; Erramilli et al, 1994; Faramarzi et al, 2020; Gandomi et al, 2013; Gupta et al, 2021a, 2021b; He et al, 2001; He & Wang, 2007; Hilborn, 2004; Houssein et al, 2020; Karaduman et al, 2019; Kaur & Arora, 2018; Kaveh et al, 2021; Kaveh & Dadras, 2017; Kohli & Arora, 2018; Krasnogor & Smith, 2005; Kumar et al, 2019; Kumar et al2021a; Kumar et al2021b; Kumar et al2021c; Kumar et al2021d; Kumar et al2021e; Kunakote et al, 2021; Li et al, 2011; Liu et al, 2010; May, 1976; Mirjalili, 2015; Mirjalili, 2016; Mirjalili et al, 2016; Mirjalili et al, 2017; Mirjalili & Lewis, 2016; Panagant et al, 2020; Peitgen et al, 1992; Ragsdell & Phillips, 1976; Ray & Saini, 2001; Rizk‐Allah et al, 2018; Saremi et al, 2017; Sayed et al, 2019; Sayed et al2018a; Sayed et al2018b; Siddall, 1972; Talatahari et al, 2011; Tang et a...…”

Section: Introductionmentioning

confidence: 99%

“…Thus hybridization of LFD with Taguchi method methodology has been examined and found better and robust than the elementary LFD technique (Yıldız et al, 2021). It is evident from the literature that chaotic maps application in numerous algorithms leads to performance improvement (Devaney, 1987; Dhawale et al, 2021a; Erramilli et al, 1994; He et al, 2001; Hilborn, 2004; Kaur & Arora, 2018; Kaveh et al, 2021; Kohli & Arora, 2018; Li et al, 2011; May, 1976; Peitgen et al, 1992; Rizk‐Allah et al, 2018; Sayed et al, 2019; Sayed et al, 2018a; Sayed et al, 2018b; Talatahari et al, 2011; Tavazoei & Haeri, 2007; Tomida, 2008; Wang et al, 2014; Wang et al, 2016; Zhao & Gao, 2020). Hence the present works endeavour the chaotic maps integration in LFD (i.e., termed as CLFD) to analyse its performance with numerous mechanical design problems.…”

Section: Introductionmentioning

confidence: 99%

A new chaotic Lévy flight distribution optimization algorithm for solving constrained engineering problems

et al. 2022

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This work proposed a new metaheuristic dubbed as Chaotic Lévy flight distribution (CLFD) algorithm, to address physical world engineering optimization problems that incorporate the chaotic maps in the elementary Lévy flight distribution (LFD). Hybridization aims to increase the LFD rate of convergence while also providing a problemfree optimization approach. The proposed methodology is investigated for five case studies of constrained optimization issues followed by shape optimization of structural design. The outcomes from the CFLD algorithm are further contrasted with its fundamental version and other distinguished recently introduced algorithms. The computational analysis illustrates the dominance of CLFD over other considered optimizers. Moreover, the present investigation shows that CLFD is a robust technique that can efficiently find optimal mechanical design problems with a proper chaotic map selection.

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“…Thus, many contributed works are proposed to enhance the performance of SMA. Dhawale et al [ 42 ] suggested an improved SMA based on a chaotic strategy for solving global optimization and constrained engineering problems. Mostafa et al [ 43 ] presented a modified SMA by adaptive weight to estimate the PV panel parameters.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures

Lin

Jia

Abualigah

et al. 2021

Entropy

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Image segmentation is a fundamental but essential step in image processing because it dramatically influences posterior image analysis. Multilevel thresholding image segmentation is one of the most popular image segmentation techniques, and many researchers have used meta-heuristic optimization algorithms (MAs) to determine the threshold values. However, MAs have some defects; for example, they are prone to stagnate in local optimal and slow convergence speed. This paper proposes an enhanced slime mould algorithm for global optimization and multilevel thresholding image segmentation, namely ESMA. First, the Levy flight method is used to improve the exploration ability of SMA. Second, quasi opposition-based learning is introduced to enhance the exploitation ability and balance the exploration and exploitation. Then, the superiority of the proposed work ESMA is confirmed concerning the 23 benchmark functions. Afterward, the ESMA is applied in multilevel thresholding image segmentation using minimum cross-entropy as the fitness function. We select eight greyscale images as the benchmark images for testing and compare them with the other classical and state-of-the-art algorithms. Meanwhile, the experimental metrics include the average fitness (mean), standard deviation (Std), peak signal to noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM), and Wilcoxon rank-sum test, which is utilized to evaluate the quality of segmentation. Experimental results demonstrated that ESMA is superior to other algorithms and can provide higher segmentation accuracy.

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An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold algorithm

Cited by 28 publications

References 134 publications

A Hybrid Approach for Solving Systems of Nonlinear Equations Using Harris Hawks Optimization and Newton’s Method

A Hybrid Approach for Solving Systems of Nonlinear Equations Using Harris Hawks Optimization and Newton’s Method

A new chaotic Lévy flight distribution optimization algorithm for solving constrained engineering problems

Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures

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