Artificial Bee Colony (ABC) optimization algorithm is a swarm intelligence based nature inspired algorithm, which has been proved a competitive algorithm with some popular natureinspired algorithms. It is found that ABC is more efficient in exploration as compare to exploitation. With a motivation to balance exploration and exploitation capabilities of ABC, this paper presents an adaptive versi...

Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence algorithm which has shown a competitive performance with respect to other population-based algorithms. However, there are still some insufficiencies in ABC algorithm such as slow convergence and easily trapped in local optima. These drawbacks can be even more challenging when constraints are also involved. To address t...

Challenges in many real-world optimization problems arise from limited hardware availability, particularly when the optimization must be performed on a device whose hardware is highly restricted due to cost or space. This paper proposes a new algorithm, namely Enhanced compact Artificial Bee Colony (EcABC) to address this class of optimization problems. The algorithm benefits from the search lo...

In Nigeria, there have been increases in damages due to fire outbreaks particularly in industrial and busy environments. Fire outbreak has caused serious injuries to people, loss of lives, damage of properties etc. Methods usually used in predicting fire outbreaks are fire alarm, flame detection, smoke detection algorithm, real-time fire, flame detection etc. This Research work introduces an ar...

In this paper an overview of the areas where the Bee Colony Optimization (BCO) and its variants are applied have been given. Bee System was identified by Sato and Hagiwara in 1997 and the Bee Colony Optimization (BCO) was identified by Lucic and Teodorovic in 2001. BCO has emerged as a specialized class of Swarm Intelligence with bees as agents. It is an emerging field for researchers in the fi...

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...

Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based on intelligent food foraging behaviour of honey bee swarm. This paper introduces a local search strategy that enhances exploration competence of ABC and avoids the problem of stagnation. The proposed strategy introduces two new local search phases in original ABC. One just after onlooker bee phase and one aft...

Swarm intelligence systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. Particle swarm, Ant colony, Bee colony are examples of swarm intelligence. In the field of computer science and operations research, Artificial Bee Colony Algorithm (ABC) is an optimization algorithm based on the intelligent foraging behavio...

A recently invented foraging behavior optimization algorithm which is the Artificial Bee Colony (ABC) algorithm has been widely implemented in addressing various types of optimization problems such as job shop scheduling, constraint optimization problems, complex numerical optimization problems, and mathematical function problems. However, the high exploration ability of conventional ABC has ca...