This paper presents a novel optimization algorithm, namely hierarchical artificial bee colony optimization (HABC) for multilevel threshold image segmentation, which employs a pool of optimal foraging strategies to extends the classical artificial bee colony framework to a cooperative and hierarchical fashion. In the proposed hierarchical model, the higher-level species incorporates the enhanced...

This paper empirically evaluates four meta-heuristic search techniques namely particle swarm optimization, artificial bee colony algorithm, Genetic Algorithm and Big Bang Big Crunch Algorithm for automatic test data generation for procedure oriented programs using structural symbolic testing method. Test data is generated for each feasible path of the programs. Experiments on ten benchmark prog...

The reliability is the factor that can't be ignored in the optimization design of the mechanics. In the large complex mechanical systems, especially in the closet-state function systems, the reliability based optimization has the features that the convergence speed is slow, and process easily get trapped in the local optima because of the stronger nonlinearity of the systems. In order to conque...

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...

Artificial bee colony (ABC) is a relatively new swarm intelligence based metaheuristic. It was successfully applied to various, mostly continuous, optimization problems. For all such heuristically guided search algorithms balance between exploitation and exploration is the determining factor for success. It is generally considered that in the ABC algorithm exploitation is performed by employed ...

Artificial bee colony is a population-based search algorithm that mimics a natural behavior of real honey bees to find rich food sources to obtain maximum quantity of nectar and share the information of food sources with other bees in the hive. This paper concerns primarily about how to use artificial bee colony to solve examination timetabling problems which it is known as a NP-hard problem. I...

The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and str...

An approach for quick assessment of the exposure to ergonomic risks on computer-aided office workplaces in a company is developed. It includes a checklist considering the following ergonomic dimensions: work organization, displays, input devices, furniture, work space, environment, software and health hazards. By the checklist, data on workplace exposure to ergonomic risk factors are collected....

Tuning of the system and the controller parameters always constituted an important part of the control applications in power system. These processes need complex mathematical calculations including derivative elements classically. However, these nowadays accomplished more easily and in shorter times, using by iterative optimization approaches such as evolutionary or swarm intelligence based met...

This paper presents an improved artificial bee colony (IABC) algorithm for solving the blocking flowshop problem with the objective of minimizing makespan. The proposed IABC algorithm utilizes discrete job permutations to represent solutions and applies insert and swap operators to generate new solutions for the employed and onlooker bees. The differential evolution algorithm is employed to obt...