In this work, a Multi-Level Artificial Bee Colony (called MLABC) for optimizing numerical test functions is presented. In MLABC, two species are used. The first species employs n colonies where each of them optimizes the complete solution vector. The cooperation between these colonies is carried out by exchanging information through a leader co lony, which contain s a set of elite bees. The sec...

Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee ...

In this study, segmentation of medical images using a fuzzy artificial bee colony algorithm with a cooling schedule is created. In this study, we embed ed fuzzy inference strategy into the artificial bee colony system to construct a segmentation system named Fuzzy Artificial Bee Colony System (FABCS). A conventional FCM algorithm did not utilize the spatial information in the image. We set a lo...

This paper proposes a new mathematical model to evaluate the effects of artificial feeding on bee colony population dynamics. The proposed model is based on a classical framework and contains differential equations that describe the changes in the number of hive bees, forager bees, and brood cells, as a function of amounts of natural and artificial food. The model includes the following element...

This paper sets up an Artificial Bee Colony (ABC) algorithm for evolving ant direction Ant Colony Optimization (ACO) to solve the Economic Power Dispatch (EPD) problem. The method ABC-ACO employs the bee colony to find a suitable and the best values operators to improve the ACO. The feasibility of the proposed approach was tested on IEEE 57-bus system. The proposed approach simulation results, ...

This paper suggests a dynamic multi-colony multi-objective artificial bee colony algorithm (DMCMOABC) by using the multi-deme model and a dynamic information exchange strategy. In the proposed algorithm, K colonies search independently most of the time and share information occasionally. In each colony, there are S bees containing equal number of employed bees and onlooker bees. For each food s...

Nature-inspired methods are used in various fields for solving a number of problems. This study uses a nature-inspired method, artificial bee colony optimization that is based on the foraging behaviour of bees, for a financial classification problem. Financial decisions are often based on classification models, which are used to assign a set of observations into predefined groups. One important...

This paper is based on swarm intelligence and chaotic dynamics for learning. We address this issue by considering the problem of path planning for mobile robots. Autonomous systems assume intelligent behavior with ability of dealing in complex and changing environments. Path planning problem, which can be studied as an optimization problem, seems to be of high importance for arising of intellig...

Social learning occurs when one individual learns from another, mainly conspecific, often by observation, imitation, or communication.Using artificial flowers, we studied social learning by allowing test bumblebees to (a) see dead bumblebees arranged in foraging positions or (b) watch live bumblebees actually foraging or (c) communicate with nestmates within their colony without having seen for...

So many techniques are used in the software testing environment and in this paper we survey the ABC algorithmic approach and its advantages over the GA (Genetic Algorithms). Artificial bee colony (ABC) algorithm is one of the most recently introduced swarm–based algorithms. These optimization approaches helps in memorization and also support the global optima. This algorithm is based on colony ...