Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking

Nenavath, Hathiram; Jatoth, Ravi Kumar

doi:10.1016/j.asoc.2017.09.039

Cited by 263 publications

(87 citation statements)

References 49 publications

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“…Some scholars introduced search strategies of other algorithms or improved the setting of the parameters of the SCA algorithm itself: Long et al [49] introduced nonlinear weight factor and inertial weight based on Gaussian distribution, respectively, to improve the ability to avoid falling into the trap of local optimization and convergence speed; Qu et al [50] proposed an improved SCA algorithm based on neighborhood search and Greedy Levy Mutation to better balance the phases of local exploitation and global exploration of the algorithm. Some scholars have also integrated the SCA algorithm with other algorithms to further improve the optimization ability of the algorithm: Chegini et al [51] proposed to mix the SCA algorithm with the PSO algorithm to improve the global search capability of the algorithm and the calculation accuracy; Nenavath and Jatoth [52] mixed the SCA algorithm and the DE algorithm to further improve the convergence speed of the algorithm and the ability to avoid local optimization.…”

Section: Relatedmentioning

confidence: 99%

Sine Cosine Algorithm with Multigroup and Multistrategy for Solving CVRP

Yang

Chu

Pan

et al. 2020

Mathematical Problems in Engineering

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Sine Cosine Algorithm (SCA) has been proved to be superior to some existing traditional optimization algorithms owing to its unique optimization principle. However, there are still disadvantages such as low solution accuracy and poor global search ability. Aiming at the shortcomings of the sine cosine algorithm, a multigroup multistrategy SCA algorithm (MMSCA) is proposed in this paper. e algorithm executes multiple populations in parallel, and each population executes a different optimization strategy. Information is exchanged among populations through intergenerational communication. Using 19 different types of test functions, the optimization performance of the algorithm is tested. Numerical experimental results show that the performance of the MMSCA algorithm is better than that of the original SCA algorithm, and it also has some advantages over other intelligent algorithms. At last, it is applied to solving the capacitated vehicle routing problem (CVRP) in transportation. e algorithm can get better results, and the practicability and feasibility of the algorithm are also proved. [29,30], bat algorithm (BA) [31,32], symbiotic organism search algorithm (SOS) [33,34], and QUATRE [35][36][37].At present, there are so many optimization algorithms, which can solve some complex optimization problems well. Why do we need so many optimization algorithms? As demonstrated by the No Free Lunch (NFL) [38] proposed by Wolpert and Macready, no single optimization algorithm is applicable to all problems. Inspired by this, a new intelligent optimization algorithm was proposed by Australian scholar Mirjalili in 2016, which is called Sine Cosine Algorithm (SCA) [39]. e SCA algorithm iterates through the properties of the sine and cosine functions to achieve optimization. It has fewer parameter settings, is easy to implement, and has a strong optimization ability. It has been proved that it is better than PSO algorithm, genetic algorithm (GA), and firefly algorithm (FA) in convergence with accuracy and speed [39].With the rapid development of software and hardware, parallel computing has become a form of high-performance computing. In evolutionary computing, parallelism often represents the iterative updating of multiple populations at the same time. e advantage of this method is to ensure population diversity, to further improve the search ability and performance of the algorithm. Especially when solving complex optimization problems, parallelizing the algorithm is an effective way to improve the efficiency and accuracy of the algorithm. At present, many existing algorithms have successfully applied the parallel mechanism, such as parallel PSO [40], parallel ACO [41], and parallel QUATRE [42]. Inspired by this, this paper introduces the multigroup and multistrategy optimization mechanism to further improve the SCA algorithm. It is called MMSCA. When the algorithm is solved, multiple populations execute in parallel, and each population adopts different updating strategies. By comparing the results of test functions, MMSCA is be...

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Section: Relatedmentioning

confidence: 99%

Sine Cosine Algorithm with Multigroup and Multistrategy for Solving CVRP

Yang

Chu

Pan

et al. 2020

Mathematical Problems in Engineering

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“…Subsequently, some improved methods were proposed [40][41][42][43]. Especially, Mirjalili presented that the SCA could be hybridized with other algorithms in the field of stochastic optimization to improve its performance in [37], and lots of hybrid methods had been applied [44][45][46]. Thus, a hybrid EALO-SCA is proposed in order to solve abrupt motion tracking in the paper (see Fig.…”

Section: Introductionmentioning

confidence: 99%

Hybridizing extended ant lion optimizer with sine cosine algorithm approach for abrupt motion tracking

Zhang

Gao

Zhang

et al. 2020

J Image Video Proc.

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In view of the problem that the conventional tracker does not adapt to abrupt motion, we propose a tracking algorithm based on the hybrid extended ant lion optimizer with sine cosine algorithm (EALO-SCA) in this paper. Firstly, the multiple elites is used to replace the single elite in the standard ant lion optimizer (ALO). The extended ALO (EALO) can enhance the global exploration ability, which can handle abrupt motion. Secondly, considering that sine cosine algorithm (SCA) has strong local exploitation operator, a hybrid EALO-SCA tracker is proposed using the advantages of both EALO and SCA. The proposed approach can improve tracking accuracy and efficiency. Finally, extensive experimental results in both quantitative and qualitative measures prove that the proposed algorithm is very competitive compared to 7 state-of-the-art trackers, especially for abrupt motion tracking.

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“…Sine cosine algorithm (SCA) is a new swarm intelligence method that was proposed recently by Mirjalili [12]. Since its introduction, SCA has successfully found its applications for many practical problems [13][14][15][16][17][18]. However, like other intelligent algorithms [19][20][21][22][23][24][25], the original SCA is easy to fall into the local optimum when solving the practical problems.…”

Section: Introductionmentioning

confidence: 99%

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

Lin

Chen

et al. 2019

Mathematical Problems in Engineering

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Under the background of "innovation and entrepreneurship," how to scientifically and rationally choose employment or independent entrepreneurship according to their own comprehensive situation is of great significance to the planning and development of their own career and the social adaptation of university personnel training. This study aims to develop an adaptive support vector machine framework, called RF-CSCA-SVM, for predicting college students' entrepreneurial intention in advance; that is, students choose to start a business or find a job after graduation. RF-CSCA-SVM combines random forest (RF), support vector machine (SVM), sine cosine algorithm (SCA), and chaotic local search. In this framework, RF is used to select the most important factors; SVM is employed to establish the relationship model between the factors and the students' decision to choose to start their own business or look for jobs. SCA is used to tune the optimal parameters for SVM. Additionally, chaotic local search is utilized to enhance the search capability of SCA. A total of 300 students were collected to develop the predictive model. To validate the developed method, other four meta-heuristic based SVM methods were used for comparison in terms of classification accuracy, Matthews Correlation Coefficients (MCC), sensitivity, and specificity. The experimental results demonstrate that the proposed method can be regarded as a promising success with the excellent predictive performance. Promisingly, the established adaptive SVM framework might serve as a new candidate of powerful tools for entrepreneurial intention prediction.

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Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking

Cited by 263 publications

References 49 publications

Sine Cosine Algorithm with Multigroup and Multistrategy for Solving CVRP

Sine Cosine Algorithm with Multigroup and Multistrategy for Solving CVRP

Hybridizing extended ant lion optimizer with sine cosine algorithm approach for abrupt motion tracking

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

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