A comprehensive survey of sine cosine algorithm: variants and applications
TLDR
Sine Cosine Algorithm (SCA) as mentioned in this paper is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions, which has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields.Abstract:
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.read more
Citations
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Journal ArticleDOI
Novel Improved Salp Swarm Algorithm: An Application for Feature Selection
Miodrag Zivkovic,Catalin Stoean,Amit Chhabra,Nebojsa Budimirovic,Aleksandar Petrovic,Nebojsa Bacanin +5 more
TL;DR: A modified version of the salp swarm algorithm for feature selection is proposed and the performance of the algorithm is compared to the best algorithms with the same test setup resulting in better number of features and classification accuracy for the proposed solution.
Journal ArticleDOI
Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application
Nebojsa Bacanin,Miodrag Zivkovic,Fadi Al-Turjman,K. Venkatachalam,P. Trojovský,Ivana Strumberger,Timea Bezdan +6 more
TL;DR: In this paper , the authors proposed an automated framework based on the hybridized sine cosine algorithm for tackling the overfitting problem in convolutional neural networks (CNNs).
Journal ArticleDOI
Boosting Marine Predators Algorithm by Salp Swarm Algorithm for Multilevel Thresholding Image Segmentation
TL;DR: In this paper , a hybrid Marine Predators Algorithm (MPA) with Salp Swarm Algorithm(SSA) was proposed to determine the optimal multilevel threshold image segmentation MPASSA.
Journal ArticleDOI
Nodes placement in wireless mesh networks using optimization approaches: a survey
Sylia Mekhmoukh Taleb,Yassine Meraihi,Asma Benmessaoud Gabis,Seyedali Mirjalili,Amar Ramdane-Cherif +4 more
Journal ArticleDOI
An Improved Teaching-Learning-Based Optimization Algorithm with Reinforcement Learning Strategy for Solving Optimization Problems
TL;DR: A new learning mode considering the effect of the teacher is presented and the Q-Learning method in reinforcement learning (RL) is introduced to build a switching mechanism between two different learning modes in the learner phase to improve the local optima avoidance ability of RLTLBO.
References
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Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Journal ArticleDOI
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
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.
Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Journal ArticleDOI
No free lunch theorems for optimization
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.