Salīdzināt metodes

	Mākslīgās bites kolonijas (ABC) optimizācija	Ģenētiskais algoritms
Nozare	Optimizācija	Optimizācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2007	1975
Autors	Dervis Karaboga & Bahriye Basturk	John Henry Holland
Tips	Swarm Intelligence Metaheuristic	Population-based metaheuristic
Pirmavots	Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
Citi nosaukumi	ABC Algorithm, Bee Colony Optimization, Swarm-Based Bee Search, Yapay Arı Kolonisi	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Saistītās	3	5
Kopsavilkums	Artificial Bee Colony (ABC) is a population-based swarm intelligence metaheuristic introduced by Karaboga and Basturk in 2007. It models the cooperative foraging behavior of a honey bee colony to search for optimal solutions in continuous numerical optimization problems. The algorithm divides candidate solutions among three bee types — employed, onlooker, and scout — and iteratively refines them through local search and probabilistic selection, making it well-suited for researchers and engineers tackling complex, multimodal optimization landscapes.	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.