In restructuring the electric power industry, the load had an important role for market managers and participants when they develop strategies or make decisions to maximize their profit. Therefore, accurate short term load forecasting (STLF) becomes more and more vital for all market participants such as customer or producer in competitive electricity markets. In this paper, a new hybrid algori...

This paper presents an extensive study on the application of Artificial Bee Colony (ABC) algorithm for load frequency control (LFC) in multi-area power system with multiple interconnected generators. The LFC model incorporates various possible physical constraints and non-linearities such as generation rate constraint, time delay, dead zone and boiler. The ABC algorithm is used to find the opti...

The objective of the p-center problem is to locate p-centers on a network such that the maximum of the distances from each node to its nearest center is minimized. The artificial bee colony algorithm is a swarm-based meta-heuristic algorithm that mimics the foraging behavior of honey bee colonies. This study proposes a modified ABC algorithm that benefits from a variety of search strategies to ...

Cluster examination is data mining task for the assignment of collection a set of items in such a path, to the point that questions in the same gathering (called a cluster) are more like one another than to those in different gatherings (clusters). K-means grouping is a technique for group investigation which intends to parcel n perceptions into k groups in which every perception fits in with t...

The Artificial Bee Colony (ABC) algorithm is an active field of optimization based on swarm intelligence in recent years. Inspired by the mutation strategies used in Differential Evolution (DE) algorithm, this paper introduced three types strategies (“rand”, “best”, and “current-to-best”) and one or two numbers of disturbance vectors to ABC algorithm. Although individual mutation strategies in ...

Travelling salesman problem (TSP) is one the problems of NP-complete family, which means finding shortest complete close tour in graph. This study seeks to solve this using Artificial Bee Colony (ABC) Algorithm along with proposed Discrete Nearest Neighborhood (DNNA). DNNA finds path among points by starting from an arbitrary point. In next steps links will be a guide make tour. other words par...

Artificial Bee Colony (ABC) is one of the most recent nature inspired (NIA) algorithms based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization problems over continuous space. However, it has been observed that the structure of ABC is such that it supports exploration more in comparison to exploitation. In or...

In this study, diagnosis of diabetes disease, which is one of the most important diseases, is conducted with artificial intelligence techniques. We have proposed a novel Artificial Bee Colony (ABC) algorithm in which a mutation operator is added to an Artificial Bee Colony for improving its performance. When the current best solution cannot be updated, a blended crossover operator (BLX-α) of ge...

In this paper, the performances of the Artificial Bee Colony (ABC) Algorithm and Invasive Weed Optimization (IWO) Algorithm are compared on the basis of modified versions of five well known benchmark functions. The modifications are performed on these functions in order to get rid of the symmetrical properties of the selected functions. Further a solution space is shifted so that the optimal fu...

Artificial Bee Colony (ABC) optimization algorithm is a swarm intelligence based nature inspired algorithm which has been proved a competitive algorithm with some popular nature-inspired algorithms. However, it is found that the ABC algorithm prefers exploration at the cost of the exploitation. Therefore, in this paper a self adaptive fitness based mutation strategy is presented in which the pe...