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...

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...

In this paper, a new discrete artificial bee colony algorithm is used to solve the symmetric traveling salesman problem (TSP). The concept of Swap Operator has been introduced to the original artificial bee colony (ABC) algorithm which can help the bees to generate a better candidate tour by greedy selection. By taken six typical TSP instances as examples, the proposed algorithm is compared wit...

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...

Over the past few years, swarm intelligence based optimization techniques such as ant colony optimization and particle swarm optimization have received considerable attention from engineering researchers. These algorithms have been used in the solution of various structural optimization problems where the main goal is to minimize the weight of structures while satisfying all design requirements...

Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the intelligent behavior of honey bees when searching for food sources. The various versions of the ABC algorithm have been widely used to solve continuous and discrete optimization problems in different fields. In this paper a new binary version of the ABC algorithm inspired by quantum computing, c...

Nature is the immense gifted source for solving complex problems. It always helps to find the optimal solution to solve the problem. Mobile Ad Hoc NETwork (MANET) is a wide research area of networks which has set of independent nodes. The characteristics involved in MANET’s are Dynamic, does not depend on any fixed infrastructure or centralized networks, High mobility. The Bio-Inspired algorith...

A Modified Artificial Bee Colony (ABC) algorithm for Economic Dispatch (ED) problem has been proposed. The Artificial Bee Colony (ABC) algorithm which is inspired by the foraging behavior of honey bee swarm gives a solution procedure for solving economic dispatch problem. It provides solution more effective than Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimizati...

Feature selection plays an important role in data mining and pattern recognition, especially in the case of large scale data. Feature selection is done due to large amount of noise and irrelevant features in the original data set. Hence, the efficiency of learning algorithms will increase incredibly if these irrelevant data are removed by this procedure. A novel approach for feature selection i...