A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking
TL;DR: Comparison studies of tracking accuracy and speed of the Hybrid SCA-PSO based tracking framework and other trackers, viz., Particle filter, Mean-shift, Particle swarm optimization, Bat algorithm, Sine Cosine Algorithm (SCA) and Hybrid Gravitational Search Al algorithm (HGSA) is presented.
Abstract: Due to its simplicity and efficiency, a recently proposed optimization algorithm, Sine Cosine Algorithm (SCA), has gained the interest of researchers from various fields for solving optimization problems. However, it is prone to premature convergence at local minima as it lacks internal memory. To overcome this drawback, a novel Hybrid SCA-PSO algorithm for solving optimization problems and object tracking is proposed. The P b e s t and G b e s t components of PSO (Particle Swarm Optimization) is added to traditional SCA to guide the search process for potential candidate solutions and PSO is then initialized with P b e s t of SCA to exploit the search space further. The proposed algorithm combines the exploitation capability of PSO and exploration capability of SCA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 23 classical, CEC 2005 and CEC 2014 benchmark functions. Statistical parameters are employed to observe the efficiency of the Hybrid SCA-PSO qualitatively and results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms. The Hybrid SCA-PSO algorithm is applied for object tracking as a real thought-provoking case study. Experimental results show that the Hybrid SCA-PSO-based tracker can robustly track an arbitrary target in various challenging conditions. To reveal the capability of the proposed algorithm, comparative studies of tracking accuracy and speed of the Hybrid SCA-PSO based tracking framework and other trackers, viz., Particle filter, Mean-shift, Particle swarm optimization, Bat algorithm, Sine Cosine Algorithm (SCA) and Hybrid Gravitational Search Algorithm (HGSA) is presented.
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TL;DR: A novel optimizer is proposed to effectively approximate the unknown parameters of solar cells and PV modules and the results indicate that the proposed method can be treated as an effective, promising tool for parameter detection of the solar cells modules in dealing with practical cases.
208 citations
TL;DR: An attempt has been made towards the eradication of low diversity, stagnation in local optima and skipping of true solutions of Sine Cosine Algorithm by proposing a modified version of SCA.
Abstract: Real-world optimization problems demand an efficient meta-heuristic algorithm which maintains the diversity of solutions and properly exploits the search space of the problem to find the global optimal solution. Sine Cosine Algorithm (SCA) is a recently developed population-based meta-heuristic algorithm for solving global optimization problems. SCA uses the characteristics of sine and cosine trigonometric functions to update the solutions. But, like other population-based optimization algorithms, SCA also suffers the problem of low diversity, stagnation in local optima and skipping of true solutions. Therefore, in the present work, an attempt has been made towards the eradication of these issues, by proposing a modified version of SCA. The proposed algorithm is named as modified Sine Cosine Algorithm (m-SCA). In m-SCA, the opposite population is generated using opposite numbers based on perturbation rate to jump out from the local optima. Secondly, in the search equations of SCA self-adaptive component is added to exploit all the promising search regions which are pre-visited. To evaluate the effectiveness in solving the global optimization problems, m-SCA has been tested on two sets of benchmark problems – classical set of 23 well-known benchmark problems and standard IEEE CEC 2014 benchmark test problems. In the paper, the performance of proposed algorithm m-SCA is also tested on five engineering optimization problems. The conducted statistical, convergence and average distance analysis demonstrate the efficacy of the proposed algorithm to determine the efficient solution of real-life global optimization problems.
188 citations
TL;DR: The Sine Cosine Algorithm (SCA) as mentioned in this paper is a population-based optimization algorithm introduced by Mirjalili in 2016, motivated by the trigonometric sine and cosine functions.
Abstract: The Sine Cosine Algorithm (SCA) is a population-based optimization algorithm introduced by Mirjalili in 2016, motivated by the trigonometric sine and cosine functions. After providing an overview of the SCA algorithm, we survey a number of SCA variants and applications that have appeared in the literature. We then present the results of a series of computational experiments to validate the performance of the SCA against similar algorithms.
179 citations
TL;DR: Modeling results revealed that the MFO algorithm can capture better hyper-parameters of the SVM model in predicting TBM AR among all three hybrid models, confirming that this hybrid S VM model is a powerful and applicable technique addressing problems related to TBM performance with a high level of accuracy.
175 citations
TL;DR: This research aims to develop six hybrid models of extreme gradient boosting (XGB) which are optimized by gray wolf optimization (GWO), particle swarm optimization (PSO), social spider optimization (SSO), sine cosine algorithm (SCA), multi verse optimization (MVO) and moth flame optimized (MFO) for estimation of the TBM penetration rate (PR).
Abstract: A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. This research aims to develop six hybrid models of extreme gradient boosting (XGB) which are optimized by gray wolf optimization (GWO), particle swarm optimization (PSO), social spider optimization (SSO), sine cosine algorithm (SCA), multi verse optimization (MVO) and moth flame optimization (MFO), for estimation of the TBM penetration rate (PR). To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength (BTS), rock mass weathering, the uniaxial compressive strength (UCS), revolution per minute and trust force per cutter (TFC), were set as inputs and TBM PR was selected as model output. Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes. These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index. Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of (0.1453, and 0.1325), R2 of (0.951, and 0.951), mean absolute percentage error (4.0689, and 3.8115), and a10-index of (0.9348, and 0.9496) in training and testing phases, respectively. The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction. By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.
140 citations
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TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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IBM1
TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
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TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
10,082 citations