A comprehensive survey on the sine–cosine optimization algorithm

Abstract

Metaheuristic algorithms based on intelligent rules have been successfully developed and applied to solve many optimization areas over the past few decades. The sine–cosine algorithm (SCA) imitates the behaviour of transcendental functions while the sine and cosine functions are presented to explore and exploit the search space. SCA starts by random population and executes iterative evolution processes to update the standard evolutionary algorithm’s destination or the best location. SCA used linear transition rules to balance the exploration and exploitation searches while searching for the best or optimal solutions. Since Mirjalili proposed it in 2016, SCA has attracted many researchers’ attention to deal with several optimization problems in many fields due to its strengths in solving optimization tasks that include the simple concept, easiness of implementation, and rapid convergence. This paper aims to provide researchers with a relatively comprehensive and extensive overview of the Sine–Cosine optimization algorithm in the literature to inspire further research. It examines the available publications, including improvements, binary, chaotic, hybridizations, multi-objective variants, and different applications. Some optimization formulations regarding single-objective optimization problems, multi-objective optimization problems, binary-objective optimization problems, and more classifications regarding the optimization types are discussed. An extensive bibliography is also included.

Qubit Efficient Quantum Algorithms for the Vehicle Routing Problem on Noisy Intermediate‐Scale Quantum Processors

“…algorithms [86][87][88] amongst others, [89][90][91][92][93][94][95] with many of these having been adapted for the vehicle routing problem. [96][97][98][99][100][101][102][103][104] To find solutions to the QUBO problem using traditional quantum approaches, the cost function is first mapped to an Ising Hamiltonian:…”

“…The QUBO problem is a binary optimization problem consisting of finding a binary vector $$\vec{x}^*$$ such that: $$$$\begin{equation} \vec{x}^* = \underset{\vec{x}}{\textrm {arg min}}\, \vec{x}^{\top } \mathcal {A} \vec{x} \end{equation}$$$$where $$\vec{x}\in \lbrace 0,1\rbrace ^{n_c}$$ is a vector of $$n_c$$ classical binary variables and $$\mathcal {A}^{n_c\times n_c}$$ is a real and symmetric matrix constructed from our optimization problem. Classical methods of finding solutions to QUBO problems involve a range of metaheuristic algorithms such as simulated annealing, [ 76–81 ] TABU search [ 82–85 ] or genetic algorithms [ 86–88 ] amongst others, [ 89–95 ] with many of these having been adapted for the vehicle routing problem. [ 96–104 ]…”

Advances in Slime Mould Algorithm: A comprehensive Survey

“…Rifat Md Sayed Hasan et al [125]applied the presented BSMA for solving the unit commitment problem (UCP). Rizk-Allah RM et al [132] employed the CO-SMA to optimize the wind turbines' energy cost. Khelfa C et al [133]used SMA to improve the response time for the ambulance dispatching problem.…”

Solving large-scale discrete time–cost trade-off problem using hybrid multi-verse optimizer model

“…In addition, the study shows that SCA converges significantly faster than PSO, GA, ACO, etc. SCA has been utilized to address optimization challenges in diverse domains since 2016 5 . Like MVO, SCA has limitations.…”