A Modified Artificial Bee Colony (JA-ABC) Optimization Algorithm
Noorazliza Sulaiman, Junita Mohamad–Saleh, Abdul Ghani Abro · 2013
Artificial Bee Colony (ABC) is a swarm intelligence-based optimization algorithm. The algorithm employs honeybees’ intelligent foraging behavior. It is one among recently proposed techniques for optimization. ABC has been shown to outperform other population-based algorithms such as Genetic Algorithm (GA), Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm. Despite its excellent performance, ABC suffers from slow convergence speed and premature convergence tendency. This has motivated ABC variants proposed by numerous researchers. Nevertheless, none of the variants could avert both problems simultaneously. This research work has proposed a new ABC algorithm that converges faster and has the ability to overcome local-optima traps. The proposed algorithm concentrates on enhancing average fitness of population by mutating poor possible solutions around the fittest solution. The proposed algorithm has been compared with other ABC variants on many benchmark functions. The results have revealed that the proposed algorithm converges faster and is able to avoid premature convergence. Keywords—ABC variant; swarm-based optimization; computational Intelligence.