Antlion optimization algorithm
The antlion optimization algorithm (ALO) is a population-based metaheuristic for numerical optimization, introduced by Seyedali Mirjalili in 2015, which mimics how antlion larvae trap and consume ants to guide candidate solutions toward an optimum.
Artificial bee colony algorithm
The artificial bee colony (ABC) algorithm is a swarm intelligence optimization method that mimics honey bee foraging, proposed by Dervis Karaboga in 2005 and published with Bahriye Basturk in 2007.
Artificial fish swarm algorithm
The artificial fish swarm algorithm (AFSA), also called fish swarm algorithm, is a swarm intelligence optimization method mimicking fish behaviors like preying, swarming, and following.
Bacterial foraging optimization algorithm
The bacterial foraging optimization algorithm, introduced by K.M. Passino in 2002, is a gradient-free, population-based swarm intelligence method that mimics E. coli foraging through chemotaxis, reproduction, and elimination-dispersal.
Bat algorithm
The bat algorithm is a population-based metaheuristic introduced by Xin-She Yang in 2010 that mimics the frequency tuning, loudness, and pulse rate of bats' echolocation to find global optima of continuous functions, especially in engineering design.
Beluga whale optimization algorithm
The beluga whale optimization algorithm (BWO) is a swarm-based metaheuristic for numerical optimization, introduced in Knowledge-Based Systems in 2022, modeling beluga swimming, foraging, and whale fall.
Butterfly optimization algorithm
The butterfly optimization algorithm is a swarm-based metaheuristic introduced by Sankalp Arora and Satvir Singh in 2018, which models butterfly foraging and mating behavior to solve global optimization problems.
Cat swarm optimization
Cat swarm optimization (CSO) is a swarm intelligence metaheuristic that mimics cat behavior to optimize continuous functions, proposed in 2006 and splitting its population into seeking and tracing modes.
Chimp optimization algorithm
The chimp optimization algorithm (ChOA) is a swarm-based metaheuristic for continuous optimization, introduced by Khishe and Mosavi in 2020 and modeled on chimpanzee group hunting.
Consensus-based optimization
Consensus-based optimization (CBO) is a derivative-free swarm method for non-convex objectives, introduced by René Pinnau and colleagues in 2017, with provable convergence and applications in machine learning.
Crayfish optimization algorithm
The crayfish optimization algorithm (COA) is a swarm-based metaheuristic that models crayfish sheltering, competition, and foraging behaviors, introduced by Heming Jia and colleagues in 2023.
Crow search algorithm
The crow search algorithm (CSA) is a population-based metaheuristic introduced by Alireza Askarzadeh in 2016 that imitates how crows hide and retrieve food to optimize engineering design problems.
Cuckoo search
Cuckoo search, also known as the cuckoo search algorithm, is a metaheuristic optimization algorithm introduced by Xin-She Yang and Suash Deb in 2009, inspired by cuckoo brood parasitism.
Dragonfly algorithm
The Dragonfly algorithm is a swarm intelligence metaheuristic that solves single-objective, discrete, and multi-objective optimization problems by mimicking the static and dynamic swarming behavior of dragonflies.
Dung beetle optimization algorithm
The dung beetle optimization algorithm is a swarm-based metaheuristic introduced by Jiankai Xue and Bo Shen in 2022 that mimics dung beetle behaviors to solve optimization problems.
Dwarf mongoose optimization
Dwarf mongoose optimization (DMO) is a swarm-based metaheuristic, introduced by Agushaka, Ezugwu, and Abualigah in 2022, that mimics dwarf mongoose foraging and sleeping-site behavior to solve optimization problems.
Elephant herding optimization
Elephant herding optimization (EHO) is a swarm-based metaheuristic algorithm for continuous global optimization, introduced in 2015, that mimics elephant herds led by a matriarch, with males separating from the group.
Firefly algorithm
The firefly algorithm is a nature-inspired metaheuristic introduced by Xin-She Yang in 2009 that solves continuous, discrete, and multiobjective optimization problems by simulating fireflies attracted to brighter candidates.
Fruit fly optimization algorithm
The fruit fly optimization algorithm (FOA) is a swarm intelligence metaheuristic for numerical optimization, introduced by Wen-Tsao Pan in 2011 and inspired by how fruit flies forage by smell and then sight.
Gazelle optimization algorithm
The gazelle optimization algorithm (GOA) is a swarm-based metaheuristic, introduced by Agushaka, Ezugwu, and Abualigah in 2022, that mimics gazelles' predator-escape strategies to solve numerical and engineering optimization problems.
Glowworm swarm optimization
Glowworm swarm optimization (GSO) is a swarm intelligence algorithm proposed by K. N. Krishnanand and Debasish Ghose in 2005 that locates multiple optima of a multimodal function simultaneously.
Golden jackal optimization
Golden jackal optimization, or jackal optimization, is a swarm-based metaheuristic algorithm introduced in 2022 that mimics golden jackal hunting to solve continuous engineering optimization problems.
Grasshopper optimization algorithm
The grasshopper optimization algorithm (GOA) is a population-based metaheuristic introduced in 2017 by Saremi, Mirjalili, and Lewis, modeling grasshopper swarming to solve continuous optimization problems.
Gray wolf optimizer
The gray wolf optimizer is a swarm intelligence metaheuristic introduced by Mirjalili, Mirjalili, and Lewis in 2014, mimicking wolf pack hierarchy and hunting to minimize continuous optimization problems.
Honey badger algorithm
The honey badger algorithm (HBA) is a nature-inspired metaheuristic for numerical optimization that mimics honey badger foraging, introduced by Fatma A. Hashim and colleagues in 2021.
Jellyfish search optimizer
The jellyfish search optimizer, also written JSO, is a swarm-based metaheuristic mimicking jellyfish ocean-current and swarm motion, introduced by Chou and Truong in 2020 and needing almost no parameter tuning.
Manta ray foraging optimization
Manta ray foraging optimization (MRFO) is a nature-inspired metaheuristic algorithm that mimics manta rays' chain, cyclone, and somersault feeding behaviors to solve engineering optimization problems.
Marine predators algorithm
The marine predators algorithm (MPA) is a nature-inspired metaheuristic for numerical and engineering optimization, introduced by Afshin Faramarzi and colleagues in 2020, that models Lévy and Brownian foraging of ocean predators.
Moth-flame optimization
Moth-flame optimization (MFO) is a swarm intelligence metaheuristic for numerical optimization, proposed by Seyedali Mirjalili in 2015 and inspired by moths' spiral flight around lights.
Moth-flame optimization algorithm
Moth-flame optimization (MFO) is a population-based metaheuristic for continuous, derivative-free optimization, introduced by Seyedali Mirjalili in 2015, in which candidate solutions called moths move along logarithmic spirals toward flames, the best positions found so far.