A Multi-swarm Artificial Bee Colony Algorithm for Dynamic Optimization Problems
An enhanced Artificial Bee Colony (ABC) optimization algorithm, which is called the Interactive Artificial Bee Colony (IABC) optimization, for numerical optimization problems, is proposed in this paper. The onlooker bee is designed to move straightly to the picked coordinate indicated by the employed bee and evaluates the fitness values near it in the original Artificial Bee Colony algorithm in order to reduce the computational complexity. Hence, the exploration capacity of the ABC is constrained in a zone. Based on the framework of the ABC, the IABC introduces the concept of universal gravitation into the consideration of the affection between employed bees and the onlooker bees. By assigning different values of the control parameter, the universal gravitation should be involved for the IABC when there are various quantities of employed bees and the single onlooker bee. Therefore, the exploration ability is redeemed about on average in the IABC. Five benchmark functions are simulated in the experiments in order to compare the accuracy/quality of the IABC, the ABC and the PSO. The experimental results manifest the superiority in accuracy of the proposed IABC to other methods.