CENTRAL FORCE OPTIMIZATION: A NEW META-HEURISTIC WITH APPLICATIONS IN APPLIED ELECTROMAGNETICS

By R. Formato. 2007

425 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Electronic, Optical and Magnetic Materials, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/51f861abddccc1fb8e6024a1f86194c1daa25dcb

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In Electrical and Electronic Engineering

In Electronic, Optical and Magnetic Materials

In Mechanical Engineering

Citations in the corpus

Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, background): “There are algorithms inspired by the behavior of animals, birds, and insects, such as gray wolf optimization (GWO) [113], flower pollination (FP) [114], cuckoo search (CS) [115], firefly (FF) [116], etc. Similarly, there are algorithms inspired by some phenomenon observed in the physics and chemistr”
Slime mould algorithm: A new method for stochastic optimization (2020, neutral): “The first kind mainly simulate physical phenomena, apply mathematical rules or methodologies including: Multi-Verse Optimizer (MVO) [3], Gravitational Local Search Algorithm (GLSA) [4], Charged System Search (CSS) [5], Gravitational Search Algorithm (GSA) [6], Sine Cosine Algorithm (SCA) [7], Simula”
GSA: A Gravitational Search Algorithm (2009, methodology): “Note that we compare both GSA and CFO only for the functions that reported by Formato [9], because we need some a priori information about the function to be optimized to apply to CFO.”
A survey on new generation metaheuristic algorithms (2019, methodology): “Some of other the recent algorithms are Artificial Chemical Reaction Optimization Algorithm (Alatas, 2011), Exchange Market Algorithm (Ghorbani and Babaei, 2014), Group Counseling Optimization (Eita and Fahmy, 2014), Probability-Based Incremental Learning (Dasgupta and Michalewicz, 2013), Gravitatio”
Grey Wolf Optimizer (2014, methodology): “Some of the most popular algorithms are Gravitational Local Search (GLSA) [22], Big-Bang Big-Crunch (BBBC) [23], Gravitational Search Algorithm (GSA) [24], Charged System Search (CSS) [25], Central Force Optimization (CFO) [26], Artificial Chemical Reaction Optimization Algorithm (ACROA) [27], Black”
The Whale Optimization Algorithm (2016, methodology): “The most popular algorithms are Simulated Annealing (SA) 4,5] , Gravitational Local Search (GLSA) [6] , Big-Bang Big-Crunch BBBC) [7] , Gravitational Search Algorithm (GSA) [8] , Charged ystem Search (CSS) [9] , Central Force Optimization (CFO) [10] , rtificial Chemical Reaction Optimization Algorit”
Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems (2022, background): “Other famous works in this category are charged system search (CSS) [21], curved space optimization [22], central force optimization (CFO) [23], and galaxy-based search algorithm (GbSA) [24].”
Harris hawks optimization: Algorithm and applications (2019, neutral): “Some examples of these algorithms are Big-Bang Big-Crunch (BBBC) [23], Central Force Optimization (CFO) [24], and Gravitational Search Algorithm (GSA) [25].”
Evolutionary population dynamics and grey wolf optimizer (2014, background): “There are also a massive number of recently proposed algorithms in the literature: gravitational local search (GLSA) [1], big bang– big crunch (BBBC) [2], gravitational search algorithm (GSA) [3], central force optimization (CFO) [4], artificial chemical reaction optimization algorithm (ACROA) [5], ”