Hybrid Swarms Optimization Based Image Segmentation

Elaziz, Mohamed Abd; Ewees, Ahmed A.; Hassanien, Aboul Ella

doi:10.1007/978-3-319-47223-2_1

Cited by 28 publications

(10 citation statements)

References 40 publications

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“…They employed SSO to maximize Kapur's and Otsu's functions values. Furthermore, there are many other swarm algorithms, that were used for image segmentation including: bacterial foraging algorithm (BFO) (Bakhshali & Shamsi, 2014) (Sanyal et al, 2011), honey bee mating optimization A C C E P T E D M A N U S C R I P T (HBMO) (Horng, 2010), wind driven optimization (WDO) (Bayraktar et al, 2013), cuckoo search (CS) (Agrawal et al, 2013), artificial bee colony (ABC) (Akay, 2013;Bhandari et al, 2015), harmony search (HS) algorithm (Oliva et al, 2013), and hybrid swarm (FASSO) (Abd El-Aziz et al, 2016). However, most of these algorithms have slow convergences to the global optimal solution (i.e.…”

Section: Introductionmentioning

confidence: 99%

Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

Elaziz

Ewees

Hassanien

2017

Expert Systems with Applications

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502

189

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Section: Introductionmentioning

confidence: 99%

Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

Elaziz

Ewees

Hassanien

2017

Expert Systems with Applications

Self Cite

502

189

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“…MAs, which seek the optimal solutions by initialising the optimisation operation with a collection of random so-lutions and improving them by predefined procedures, is considered one of the best techniques and used to solve many optimisation problems [3], [14], [18]- [21]. MAs are widely applied for solving MOO problems since they may achieve a solution after only one run.…”

Section: Introductionmentioning

confidence: 99%

“…(14) B. DESCRIPTION OF THE TEST PROBLEMSTo evaluate the proposed MBSA method, some experiments were performed using a set of different MO problems, which consists of 17 problems that are classified into three groups of functions[29],[36]: 1) Unconstrained test functions: These functions are ZDT1, ZDT2, ZDT3, and ZDT2 with three objectives (well-known ZDT suite) and ZDT1 with linear PF.…”

mentioning

confidence: 99%

Solving Multi-Objective Problems Using Bird Swarm Algorithm

et al. 2021

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This paper introduces an effective method by combining the multi-objective technique with the bird swarm algorithm (BSA) to obtain a new method called MBSA. The MBSA obtains some of the different non-dominated techniques that maintain variety amongst the optimal solutions. To verify and evaluate the effectiveness of the MBSA, collections of constrained, unconstrained, and engineering problems are measured. These problems have various Pareto front (PF) properties, including non-convex, convex, and discrete PFs. The results show that the MBSA has a good ability to obtain both a better solution spread and better convergence near the true PF. Furthermore, the quantitative and qualitative results indicate that the MBSA provides high convergence and good results in all experiments and with real-world problems against well-known algorithms in the literature. INDEX TERMS Bird swarm algorithm; Multi-objective optimization; Constrained optimization problems; UnConstrained optimization problems; Engineering optimization problems; Pareto front; Pareto optimal solutions.

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“…To overcome these limitations, several hybrid metaheuristics have been proposed. For example, in Reference [ 23 ], a multi-level threshold method based on a hybrid of social spider optimization (SSO) and FA is presented. The developed FASSO method uses the power of both FA and SSO to avoid individual MH limitations.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy

Alwerfali

Al-qaness

Elaziz

et al. 2020

Entropy

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Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.

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Hybrid Swarms Optimization Based Image Segmentation

Cited by 28 publications

References 40 publications

Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

Solving Multi-Objective Problems Using Bird Swarm Algorithm

Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy

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