A new meta-heuristic method: Ray Optimization

By Ali Kaveh, M. Khayatazad. 2012

815 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/1fba68eecd7a4e443686fc96a9894e0506870d56

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Seyedali Mirjalili
2. Gaurav Dhiman
3. Pavel Trojovský
4. Mohammad Dehghani
5. Vijay Kumar
6. Seyed Mohammad Mirjalili
7. Kashif Hussain
8. Yongquan Zhou

In Biomedical Engineering

1. Seyedali Mirjalili
2. Andrew Lewis
3. Yongquan Zhou
4. Kashif Hussain
5. Shi Cheng
6. M. N. Mohd Salleh
7. Yuhui Shi
8. Gaurav Dhiman

In Computational Theory and Mathematics

1. Seyedali Mirjalili
2. Gaurav Dhiman
3. Pavel Trojovský
4. Mohammad Dehghani
5. Vijay Kumar
6. Seyed Mohammad Mirjalili
7. Yongquan Zhou
8. Ali Asghar Heidari

Citations in the corpus

• 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”
• Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (2015, methodology): “The most recent algorithms in this category are: Gravitational Search Algorithm (GSA) [30], Chemical Reaction Optimization (CRO) [31], Artificial Chemical Reaction Optimization Algorithm (ACROA) [32], Charged System Search (CSS) algorithm [33], Ray Optimization (RO) [34], Black Hole (BH) algorithm [”
• STOA: A bio-inspired based optimization algorithm for industrial engineering problems (2019, background): “, 2006), and Ray Optimization (RO) algorithm (Kaveh and Khayatazad, 2012).”
• Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems (2020, background): “…from the discipline of Physics are: charged system search (CSS) [22] follows Coulomb and Newtonian’s laws, gravitational search algorithm (GSA) [23] based on gravitational theory, ray optimization (RO) [24] observes ray theory and Henry gas solubility optimization (HGSO) based on Henry law [25].”
• Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, methodology): “From physics based algorithms-galaxy based search algorithm [91], curved space optimization [97], ray optimization [98], lightning search algorithm [105], thermal exchange optimization [111], find fix finish exploit analyze [112].”
• 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”
• Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems (2016, methodology): “Some of the latest algorithms are Artificial Bee Colony (ABC) algorithm [9], Bat Algorithm (BA) [10], Cuckoo Search (CS) algorithm [11], Cuckoo Optimization Algorithm (COA) [12], Gravitational Search Algorithm (GSA) [13], Charged System Search (CSS) [14], Firefly Algorithm (FA) [15], and Ray Optimiz”