SCA: A Sine Cosine Algorithm for solving optimization problems

By Seyedali Mirjalili. Knowledge-Based Systems, 2016

4,945 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/da33390cde1a8ec6676f64e645d8aa28d9dba661

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Citations in the corpus

Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules (2021, methodology): “Rizk-Allah (2019) Sine cosine algorithm based on orthogonal parallel information (SCA-OPI) SCI-OPI gains a highly competitive performance compared with other algorithms.”
Metaheuristic research: a comprehensive survey (2018, methodology): “Table 4 Acronyms of metaheuristics abbreviations used in this study Abbreviations Acronyms AAA Alienated Ant Algorithm (Uymaz et al. 2015) AAA2 Artificial Algae Algorithm (Bandieramonte et al. 2010) ABC Artificial Bee Colony (Karaboga 2005) ABO African Buffalo Optimization (Odili and Mohmad Kahar 20”
Evolutionary Large-Scale Multi-Objective Optimization: A Survey (2021, methodology): “…a single MOEA for solving LSMOPs, including those in the genetic algorithm, particle swarm optimization, bat algorithm [74], sine cosine algorithm [117], imperialist competitive algorithm [69], whale optimization algorithm [118], trader optimization algorithm [114], heat transfer search [130],…”
Political Optimizer: A novel socio-inspired meta-heuristic for global optimization (2020, neutral): “…examples are Simulated Annealing (SA) [32], Gravitational Search Algorithm (GSA) [33], Black Hole (BH) algorithm [34], Sine Co-sine Algorithm (SCA) [35], Big-Bang Big-Crunch (BB-BC) optimization algorithm [36], Water Cycle Algorithm (WCA) [37], Artiﬁcial Electric Field Algorithm (AEFA) [38],…”
Grey wolf optimizer: a review of recent variants and applications (2017, methodology): “SI algorithms include many algorithms such ant colony optimization (ACO) [13], particle swarm optimization (PSO) [15], cuckoo search (CS) [138], krill herd optimization (KH) [29], firefly algorithm (FA) [137], artificial bee colony (ABC) [50], multi-verse optimizer (MVO) [82], ant lion optimizer (AL”
Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems (2019, methodology): “The population initialization process of proposed SOA and other competitor algorithms (i.e., SHO, GWO, PSO, MFO, MVO, SCA, GSA, GA, and DE) requires O(no × np) time, where no represents the number of objectives and np represents the number Algorithm 1 Seagull Optimization Algorithm Input: Seagull po”