01-01-2013, 01:02 PM
ENHANCED ARTIFICIAL BEE COLONY OPTIMIZATION
Abstract.
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.
Keywords: Swarm Intelligence, Bee colony algorithm, Numerical optimization, Interactive
artificial bee colony, Particle swarm optimization
Introduction.
In recent years, swarm intelligence becomes more and more attractive
for the researchers, who work in the related research field. It can be classified as one of the
branches in evolutionary computing. Swarm intelligence can be defined as the measure
introducing the collective behavior of social insect colonies or other animal societies to
design algorithms or distributed problem-solving devices.[1] Generally, the algorithms in
swarm intelligence are applied to solve optimization problems. The classical algorithm
in evolutionary computing and is used to solve problems of optimization is the Genetic
Algorithm (GA) [11-12, 17, 19, 21]. Later then, many swarm intelligence algorithms
for solving problems of optimization are proposed such as the Cat Swarm Optimization
(CSO) [5-6], the Parallel Cat Swarm Optimization (PCSO) [23], the Artificial Bee Colony
(ABC) [15-16], the Particle Swarm Optimization (PSO) [2, 4, 14, 22], the Fast Particle
Swarm Optimization (FPSO) [3], and the Ant Colony Optimization (ACO) [7-9]. Moreover,
several applications of optimization algorithms based on computational intelligence
or swarm intelligence are also presented one after another [10, 18, 20].
The Interactive Artificial Bee Colony (IABC).
In general, the ABC algorithm
works well on finding the better solution of the object function. However, the original
design of the onlooker bee’s movement only considers the relation between the employed
bee, which is selected by the roulette wheel selection, and the one selected randomly.
Therefore, it is not strong enough to maximize the exploitation capacity.
The Interactive Artificial Bee Colony algorithm is proposed based on the structure of
ABC algorithm. By employing the Newtonian law of universal gravitation [13] described
in the equation (4), the universal gravitations between the onlooker bee and the selected
employed bees are exploited.