Artificial bee colony algorithm for large-scale problems and engineering design optimization

By Bahriye Akay, Dervis Karaboga. Journal of Intelligent Manufacturing, 2010

561 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6ca1e71ea0c486b4d60dd1b38f1b25707ce0fd3f

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. Amir H. Gandomi
2. Seyedali Mirjalili
3. Laith Mohammad Abualigah
4. R. Venkata Rao
5. Absalom El-Shamir Ezugwu
6. Jeffrey O. Agushaka
7. Vimal J. Savsani
8. Tansel Dökeroğlu

In Computational Theory and Mathematics

1. Amir H. Gandomi
2. Seyedali Mirjalili
3. Laith Mohammad Abualigah
4. R. Venkata Rao
5. Vimal J. Savsani
6. Absalom El-Shamir Ezugwu
7. Jeffrey O. Agushaka
8. Sedigheh Mahdavi

In Computer Vision and Pattern Recognition

1. Amir H. Gandomi
2. Laith Mohammad Abualigah
3. Seyedali Mirjalili
4. Absalom El-Shamir Ezugwu
5. Sedigheh Mahdavi
6. Vimal J. Savsani
7. R. Venkata Rao
8. Shahryar Rahnamayan

Citations in the corpus

• Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer (2022, methodology): “Popular in this category are particle swarm optimization (PSO) [35], artificial bee colony (ABC) [9, 10], and ant colony optimization (ACO) [17].”
• Metaheuristics in large-scale global continues optimization: A survey (2015, methodology): “Moreover, the modified ABC algorithm [4], a locust swarms [21], a particle swarm optimization based on utilizing guided reinitialization [17], an immune algorithm [31], and a hybrid BFA-PSO algorithm [29] were proposed to solve LSGO problems.”
• Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems (2012, methodology): “Table 8 Details of benchmark functions considered by Akay and Karaboga [2].”
• Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems (2011, unknown): “…design problems were attempted by (µ + λ) -Evolutionary Strategy (ES) [19], Unified Particle Swarm Optimization (UPSO) [20], Co-evolutionary Particle Swarm Optimization (CPSO) [21], Co-evolutionary Differential Evolution (CoDE) [17], Hybrid PSO-DE [13] and Artificial Bee Colony (ABC) [22].”
• Prairie Dog Optimization Algorithm (2022, methodology): “The most popular algorithm in this category is particle swarm optimization (PSO); others include the red fox optimization algorithm [35], salp swarm algorithm [36], ant colony optimization [13], and artificial bee colony [14].”