An ABC Algorithm with Recombination

Abstract

Artificial bee colony (ABC) is an efficient swarm intelligence algorithm, which has shown good exploration ability. However, its exploitation capacity needs to be improved. In this paper, a novel ABC variant with recombination (called RABC) is proposed to enhance the exploitation. RABC firstly employs a new search model inspired by the updating equation of particle swarm optimization (PSO). Then, both the new search model and the original ABC model are recombined to build a hybrid search model. The effectiveness of the proposed RABC is validated on ten famous benchmark optimization problems. Experimental results show RABC can significantly improve the quality of solutions and accelerate the convergence speed.

A Cyclical Non-Linear Inertia-Weighted Teaching–Learning-Based Optimization Algorithm

“…Step 7: Execute mutation strategy. Calculate the probability of variation P c using Equation (9). If P c > rand(0, 1), then the two individuals with the worst solution will be randomly mutated into new individuals.…”

“…In the past decades, many classical nature-inspired optimization algorithms based on population have been proposed. These algorithms have been proven effective in solving global optimization problems and specific types of engineering optimization problems, such as GA (genetic algorithm) [1,2], ACO (ant colony optimization) [3,4], PSO (particle swarm optimization) [5,6], ABC (artificial bee colony) [7][8][9] and DE (differential evolution) [10][11][12], etc. However, any algorithm has its own merits and demerits in solving diverse problems.…”