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...

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...

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...

Many different earning algorithms used for getting high performance in mathematics and statistical tasks. Recently, an Artificial Bee Colony (ABC) developed by Karaboga is a nature inspired algorithm, which has been shown excellent performance with some standard algorithms. The hybridization and improvement strategy made ABC more attractive to researchers. The two famous improved algorithms are...

Segmentation is one of the essential tasks in image processing. Thresholding is one of the simplest techniques for performing image segmentation. Multilevel thresholding is a simple and effective technique. The primary objective of bi-level or multilevel thresholding for image segmentation is to determine a best thresholding value. To achieve multilevel thresholding various techniques has been ...

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...

In this paper an improved version of the Artificial Bee Colony (ABC) algorithm adjusted for constrained optimization problems is presented. It has been implemented and tested on several engineering benchmarks which contain discrete and continuous variables. Our results were compared to the results obtained by Simple Constrained Particle Swarm optimization algorithm (SiC-PSO) which showed a very...

In order to improve the convergence and diversity of multiobjective optimization algorithms, the harmonic average distance is employed to improve the aggregating function combined L-rank value. Selection model and searching scheme of artificial bee colony algorithm and diversity maintaining scheme are improved in this paper. This novel many objectives optimization method based on improved artif...

This paper describes an object-oriented software system for continuous optimization by a modified artificial bee colony (ABC) metaheuristic. Karaboga’s ABC algorithm was successfully used on many optimization problems and there is also a corresponding program in C. We implemented a modified version in C# which is easier for maintenance since it is object-oriented and which uses threads and sign...

This paper presents an artificial bee colony (ABC) algorithm adjusted for the capacitated vehicle routing problem. The vehicle routing problem is an NP-hard problem and capacitated vehicle routing problem variant (CVRP) is considered here. The artificial bee metaheuristic was successfully used mostly on continuous unconstrained and constrained problems. Here this algorithm has been implemented ...