Chaotic oppositional sine–cosine method for solving global optimization problems

Liang, Xi; Cai, Zhennao; Wang, Mingjing; Zhao, Xuehua; Chen, Huiling; Li, Chengye

doi:10.1007/s00366-020-01083-y

Cited by 60 publications

(22 citation statements)

References 104 publications

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“…Experimental results demonstrated that the proposed approach yields satisfactory and better results compared to other methods in terms of Peak-Signal-to-Noise-Ratio (PSNR), PSNR degradation ratio (D PSNR ) , and the number of search points. Liang et al (2020) proposed a chaotic oppositional SCA (COSCA) for solving global optimization problems. Opposition-based learning method was used to optimize the exploration and exploitation capability of the original SCA.…”

Section: Chaotic Sine Cosine Algorithmmentioning

confidence: 99%

A comprehensive survey of sine cosine algorithm: variants and applications

et al. 2021

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Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA.

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Section: Chaotic Sine Cosine Algorithmmentioning

confidence: 99%

A comprehensive survey of sine cosine algorithm: variants and applications

et al. 2021

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“…ere are still many problems that need to be solved with the existing intelligent algorithms [32][33][34][35][36][37][38]. For example, when the convergence speed of an algorithm is slow, it is easy to fall into local optima [4,[39][40][41][42][43][44][45]. In order to solve these problems, many scholars have carried out related researches [41,[46][47][48][49][50][51][52][53].…”

Section: Literature Reviewmentioning

confidence: 99%

An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization

Zhou

et al. 2020

Complexity

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Sine cosine algorithm (SCA) is a new meta-heuristic approach suggested in recent years, which repeats some random steps by choosing the sine or cosine functions to find the global optimum. SCA has shown strong patterns of randomness in its searching styles. At the later stage of the algorithm, the drop of diversity of the population leads to locally oriented optimization and lazy convergence when dealing with complex problems. Therefore, this paper proposes an enriched SCA (ASCA) based on the adaptive parameters and chaotic exploitative strategy to alleviate these shortcomings. Two mechanisms are introduced into the original SCA. First, an adaptive transformation parameter is proposed to make transformation more flexible between global search and local exploitation. Then, the chaotic local search is added to augment the local searching patterns of the algorithm. The effectiveness of the ASCA is validated on a set of benchmark functions, including unimodal, multimodal, and composition functions by comparing it with several well-known and advanced meta-heuristics. Simulation results have demonstrated the significant superiority of the ASCA over other peers. Moreover, three engineering design cases are employed to study the advantage of ASCA when solving constrained optimization tasks. The experimental results have shown that the improvement of ASCA is beneficial and performs better than other methods in solving these types of problems.

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“…Hence, more attention should be paid to the accuracy and efficacy of the procedure used to tackle the model [ 21 , 22 ]. The swarm intelligence optimization algorithm has shown great potential in solving a multitude of practical problems, including but not limited to, detection of feature selection issues [ [23] , [24] , [25] ], parameter optimization [ [26] , [27] , [28] ], engineering problems [ [29] , [30] , [31] ], PID optimization control [ [32] , [33] , [34] ], prediction problems in educational field [ [35] , [36] , [37] ], the hard maximum satisfiability problem [ 38 , 39 ], foreign fiber in cotton [ 40 , 41 ], medical diagnosis [ [42] , [43] , [44] ], scheduling problem [ 45 , 46 ], wind speed prediction [ 47 ], bankruptcy prediction [ [48] , [49] , [50] ], fault diagnosis of rolling bearings [ 51 , 52 ], and gate resource allocation [ 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

Liu

Zhao

et al. 2021

Computers in Biology and Medicine

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This paper focuses on the study of multilevel COVID-19 X-ray T image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present the first ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was succefully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 X-ray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com .

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Chaotic oppositional sine–cosine method for solving global optimization problems

Cited by 60 publications

References 104 publications

A comprehensive survey of sine cosine algorithm: variants and applications

A comprehensive survey of sine cosine algorithm: variants and applications

An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

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