THE BEES ALGORITHM, A NOVEL TOOL FOR COMPLEX OPTIMISATION PROBLEMS

By Duc Truong Pham, A. Ghanbarzadeh, Ebubekir Koç, Sameh Otri, S. Rahim, M. Zaidi. 2006

1,275 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/db8d1b6bd680a50c2889d75cb3d3f29462848da3

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. Seyed Mohammad Mirjalili
2. Seyedali Mirjalili
3. Xin-She Yang
4. Iztok Fister
5. Dervis Karaboga
6. Bahriye Akay
7. Janez Brest
8. Bilal Alatas

In Computational Theory and Mathematics

1. Seyed Mohammad Mirjalili
2. Seyedali Mirjalili
3. Iztok Fister
4. Janez Brest
5. Bahriye Akay
6. Dusan Fister
7. Ali Wagdy Mohamed
8. Victor O. K. Li

In Industrial and Manufacturing Engineering

1. Seyed Mohammad Mirjalili
2. Bahriye Akay
3. Ali Wagdy Mohamed
4. Anas A. Hadi
5. Ali Khater Mohamed
6. Iztok Fister
7. Janez Brest
8. Dusan Fister

Citations in the corpus

• New inspirations in swarm intelligence: a survey (2011, methodology): “As a chemical substance, the pheromone evaporates along time, therefore, reducing attractiveness strength of the trail.”
• Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (2015, background): “Some of the most recent ones are Glowworm Swarm Optimization (GSO) [19], Bees Algorithm (BA) [20], Artificial Bee Colony (ABC) algorithm [21], Bat Algorithm (BA) [22], Firefly Algorithm (FA) [23], Cuckoo Search (CS) algorithm [24], Cuckoo Optimization Algorithm (COA) [25], Grey Wolf Opti-”
• A Brief Review of Nature-Inspired Algorithms for Optimization (2013, background): “[47] Gene expression Ferreira [19] Bees swarm optimization Drias et al.”
• A survey: algorithms simulating bee swarm intelligence (2009, methodology): “…for control chart pattern recognition Pham et al. 2006a, training the learning vector quantisation networks for control chart pattern recognition Pham et al. 2006d, a welded-beam structure design Pham and Ghanbarzadeh 2007, manufacturing cell formation Pham et al. 2007a, scheduling jobs for a…”
• Chaotic bee colony algorithms for global numerical optimization (2010, methodology): “A few models have been developed to model the intelligent behaviors of honeybee swarms and applied for solving the problems (Abbass, 2001; Karaboga, 2005; Karaboga & Basturk, 2008; Pai, Yang, & Chang, 2009; Pham et al., 2006; Yang, 2005).”
• Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm (2019, background): “2006 1296 [41] Cat swarm optimization Chu et al.”
• Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, methodology): “(2006) [75].”
• Chemical-Reaction-Inspired Metaheuristic for Optimization (2010, background): “Other proposed metaheuristics include particle swarm optimization (inspired by the social behavior of bird flocking) [10], bees algorithm (inspired by the behavior of honey bees in collecting nectar) [11], harmony algorithm (inspired by the improvisation process of musicians) [12], etc.”