In the basic Artificial Bee Colony (ABC) algorithm, if the fitness value associated with a food source is not improved for a certain number of specified trials then the corresponding bee becomes a scout to which a random value is assigned for finding the new food source. Basically, it is a mechanism of pulling out the candidate solution which may be entrapped in some local optimizer due to whic...

In order to resolve the multi-parameter optimization problem of finite impulse response (FIR) digital filter design, a quantum bee colony algorithm (QBCA) is proposed. The proposed QBCA is a multi-dimensional search algorithm for optimization of real numbers, and uses mechanisms of quantum evolution to update the positions of different quantum bees. Computer simulations have showed that FIR dig...

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behavio...

The Artificial Bee Colony (ABC) algorithm is one of most popular stochastic, swarm based algorithm proposed by Karaboga in 2005 inspired from the foraging behaviour of honey bees. ABC has been applied to solve several problems in various fields and also many researchers have attempted to improve ABC’s performance by making some modifications. This paper proposes a new variant of ABC algorithm b...

This paper empirically evaluates four meta-heuristic search techniques namely particle swarm optimization, artificial bee colony algorithm, Genetic Algorithm and Big Bang Big Crunch Algorithm for automatic test data generation for procedure oriented programs using structural symbolic testing method. Test data is generated for each feasible path of the programs. Experiments on ten benchmark prog...