Exposing the grey wolf, moth‐flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by bestial metaphors

DOI10.1111/itor.13176OpenAlexW4288040592MaRDI QIDQ6056878

Marco Dorigo, Christian L. Camacho-Villalón, Thomas Stützle

Publication date: 4 October 2023

Published in: International Transactions in Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/itor.13176

zbMATH Keywords

swarm intelligenceevolution strategiesparticle swarm optimizationnature-inspired algorithmnovel algorithm

Mathematics Subject Classification ID

Operations research, mathematical programming (90-XX)

Related Items (3)

Initialization of metaheuristics: comprehensive review, critical analysis, and research directions ⋮ Metaheuristics for bilevel optimization: a comprehensive review ⋮ Quay partitioning problem

Cites Work

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• Optimization by Simulated Annealing
• Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms
• A rank based particle swarm optimization algorithm with dynamic adaptation
• Thermodynamical approach to the travelling salesman problem: An efficient simulation algorithm
• Continuous lunches are free plus the design of optimal optimization algorithms
• Revisiting simulated annealing: a component-based analysis
• An analysis of why cuckoo search does not bring any novel ideas to optimization
• A New Metaheuristic Bat-Inspired Algorithm
• Firefly Algorithms for Multimodal Optimization
• Metaheuristics—the metaphor exposed
• Handbook of metaheuristics
• A critical analysis of the “improved Clarke and Wright savings algorithm”

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