Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped ...

Artificial bee colony (ABC) algorithm has been introduced for solving numerical optimization problems, inspired collective behavior of honey bee colonies. ABC algorithm has three phases named as employed bee, onlooker bee and scout bee. In the model of ABC, only one design parameter of the optimization problem is updated by the artificial bees at the ABC phases by using interaction in the bees....

In recent years several bee inspired optimization techniques have been proposed. These methods are either based on the bees’ foraging or mating behavior. Both foraging and mating regulate distributions outside (foraging) or within a colony (mating). Foraging determines the ratio of individuals that explore the surroundings for new food sources and those that exploit known food sources, while ma...

An improved version of the Bees Algorithm is proposed for solving dynamic optimisation problems. This new formulation of the Bees Algorithm includes new search operators, and a new selection procedure that enhances the survival probability of newly formed individuals. The proposed algorithm is tested on six benchmark dynamic optimisation problems. The benchmark problems include minimisation and...

The K-mean algorithm is one of the popular clustering techniques. The algorithm requires user to state and initialize centroid values of each group in advance. This creates problem for novice users especially to those who have no or little knowledge on the data. Trial-error attempt might be one of the possible preference to deal with this issue. In this paper, an optimization algorithm inspired...

The Weighted Set Covering Problem is a formal model for many practical optimization problems. In this problem the goal is to choose a subset of columns of minimal cost covering every row. Here, we present a novel application of the Artificial Bee Colony algorithm to solve the Weighted Set Covering Problem. The Artificial Bee Colony algorithm is a recent Swarm Metaheuristic technique based on th...

This paper presents artificial bee colony optimization for solving multi-area economic dispatch (MAED) problem with tie line constraints considering transmission losses, multiple fuels, valve-point loading and prohibited operating zones. Artificial bee colony optimization is a swarm-based algorithm inspired by the food foraging behavior of honey bees. The effectiveness of the proposed algorithm...

The Artificial Bee Colony (ABC) algorithm recently gained high popularity by providing a robust and efficient approach for solving continuous optimization problems. In order to apply ABC in discrete landscape, a binary version of artificial bee colony (BABC) algorithm is proposed in this manuscript. Unlike the original ABC algorithm, the proposed BABC represents a food source as a discrete bina...

Despite the predictive performance of AnalogyBased Estimation (ABE) in generating better effort estimates, there is no consensus on: (1) how to predetermine the appropriate number of analogies, (2) which adjustment technique produces better estimates. Yet, there is no prior works attempted to optimize both number of analogies and feature distance weights for each test project. Perhaps rather th...

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic techniqu...