Category: Nature-inspired metaheuristics

Ant colony optimization algorithms
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths thro
HUMANT (HUManoid ANT) algorithm
HUMANT (HUManoid ANT) algorithm belongs to Ant colony optimization algorithms. It is a Multi-Objective Ant Colony Optimization (MOACO) with a priori approach to Multi-Objective Optimization (MOO), bas
Cuckoo search
In operations research, cuckoo search is an optimization algorithm developed by Xin-She Yang and Suash Debin 2009. It was inspired by the obligate brood parasitism of some cuckoo species by laying the
List of metaphor-based metaheuristics
This is a chronologically ordered list of metaphor-based metaheuristics and swarm intelligence algorithms, sorted by decade of proposal.
Fish School Search
Fish School Search (FSS), proposed by Bastos Filho and Lima Neto in 2008 is, in its basic version, an unimodal optimization algorithm inspired on the collective behavior of fish schools. The mechanism
Bio-inspired computing
Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behav
Firefly algorithm
In mathematical optimization, the firefly algorithm is a metaheuristic proposed by Xin-She Yang and inspired by the flashing behavior of fireflies.
Biogeography-based optimization
Biogeography-based optimization (BBO) is an evolutionary algorithm (EA) that optimizes a function by stochastically and iteratively improving candidate solutions with regard to a given measure of qual
Dispersive flies optimisation
Dispersive flies optimisation (DFO) is a bare-bones swarm intelligence algorithm which is inspired by the swarming behaviour of flies hovering over food sources. DFO is a simple optimiser which works
Imperialist competitive algorithm
In computer science, imperialist competitive algorithms are a type of computational method used to solve optimization problems of different types. Like most of the methods in the area of evolutionary
Artificial ants
No description available.
Stochastic diffusion search
Stochastic diffusion search (SDS) was first described in 1989 as a population-based, pattern-matching algorithm. It belongs to a family of swarm intelligence and naturally inspired search and optimisa
Swarm intelligence
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was intro
Bat algorithm
The Bat algorithm is a metaheuristic algorithm for global optimization. It was inspired by the echolocation behaviour of microbats, with varying pulse rates of emission and loudness. The Bat algorithm
Spiral optimization algorithm
In mathematics, the spiral optimization (SPO) algorithm is a metaheuristic inspired by spiral phenomena in nature. The first SPO algorithm was proposed for two-dimensional unconstrained optimizationba
Bees algorithm
In computer science and operations research, the bees algorithm is a population-based search algorithm which was developed by Pham, Ghanbarzadeh et al. in 2005. It mimics the food foraging behaviour o
Dual-phase evolution
Dual phase evolution (DPE) is a process that drives self-organization within complex adaptive systems. It arises in response to phase changes within the network of connections formed by a system's com
Fly algorithm
The Fly Algorithm is a type of cooperative coevolution based on the Parisian approach. The Fly Algorithm has first been developed in 1999 in the scope of the application of Evolutionary algorithms to
Artificial bee colony algorithm
In computer science and operations research, the artificial bee colony algorithm (ABC) is an optimization algorithm based on the intelligent foraging behaviour of honey bee swarm, proposed by Derviş K
Cultural algorithm
Cultural algorithms (CA) are a branch of evolutionary computation where there is a knowledge component that is called the belief space in addition to the population component. In this sense, cultural