Monarch butterfly optimization

By Gai-ge Wang, Suash Deb, Zhihua Cui. Neural computing & applications (Print), 2015

937 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Evolution, Behavior and Systematics, Genetics

View Paper: https://www.semanticscholar.org/paper/c9f12428de693b57e7f5689d45bc4827d43a5a16

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. Tansel Dökeroğlu
2. Tong Han
3. Zhuoran Zhang
4. Andi Tang
5. Bo Han
6. Lei Xie
7. Huan Zhou
8. Ayça Deniz

In Ecology, Evolution, Behavior and Systematics

1. Ayça Deniz
2. Tansel Dökeroğlu
3. Hakan Ezgi Kiziloz

In Genetics

1. Tansel Dökeroğlu
2. Ayça Deniz
3. Hakan Ezgi Kiziloz
4. Gaige Wang
5. Tong Han
6. Zhuoran Zhang
7. Andi Tang
8. Bo Han

Citations in the corpus

• Coronavirus herd immunity optimizer (CHIO) (2020, neutral): “Evaluation-based Genetic algorithm (GA) [9], evolution strategy (ES) [16], genetic programming (GP) [17], and biogeography-based optimizer (BBO) [18] Swarm-based Particle swarm optimization (PSO) [10], ant colony optimization (ACO) [11], cuckoo search (CS) [19], bat algorithm (BA) [20], ant lion opt”
• Elephant Herding Optimization (2015, background): “…ant lion optimizer (ALO) [30], big bang-big crunch algorithm (BB-BC) [31-34], charged system search (CSS) [35-37], chaotic swarming of particles (CSP) [38], monarch butterfly optimization (MBO) [39], krill herd (KH) [40-47], multi-verse optimizer (MVO) [48], dragonfly algorithm (DA) [49], grey…”
• Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization (2021, methodology): “Some other algorithms of this category include: grey wolf optimizer (GWO) [30], monarch butterfly optimization (MBO) [31], elephant herding optimization (EHO) [32], moth search algorithm (MSA) [33], manta ray foraging optimization (MRFO) [34],earthworm optimization algorithm (EOA) [35], etc.”
• Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems (2016, background): “These two are respectively inspired by the flocking behavior of birds and the pheromone of ants, and had since been followed by more and more effective SI methods, e.g., artificial bee colony (ABC) [9,10], monarch butterfly optimization (MBO) [11], elephant herding optimization (EHO) [12], and krill he”
• A comprehensive survey on recent metaheuristics for feature selection (2022, methodology): “developed a Monarch Butterfly Optimization (MBO) for feature selection [215].”