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 ...

Nature inspired metaheuristics proved to be very successful when applied to hard optimization problems, combinatorial as well as global. For all these algorithms, with very different basic ideas, parameters and implementation details, the common problem that ultimately determines the successfulness of a particular algorithm is balance between exploitation and exploration. Exploitation refers to...

In this paper the performance of four well-known metaheuristics consisting of Artificial Bee Colony (ABC), Biogeographic Based Optimization (BBO), Harmony Search (HS) and Teaching Learning Based Optimization (TLBO) are investigated on optimal domain decomposition for parallel computing. A clique graph is used for transforming the connectivity of a finite element model (FEM) into that of the cor...

Low frequency oscillation problems are very difficult to solve because power systems are very large, complex and geographically distributed. Therefore, it is necessary to utilize most efficient optimization methods to take full advantages in simplifying the problem and its implementation. From this perspective, many successful and powerful optimization methods and algorithms have been employed ...

There is a trend in the scientific community to model and solve complex optimization process by employing natural metaphors. In this area, Artificial Bee Colony optimization (ABC) tries to model natural behaviour of real honeybees in food foraging. ABC algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC is used for solving multivariabl...

This paper systematically presents the Swarm Intelligence (SI) methods for optimization of multiple and many objective problems. The fundamental difference of Multiple andMany Objective Optimization problems have been studied very rigorously. The three forefront swarm intelligence methods, i.e., Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony Optimiza...

This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and optimization. Whereas optimization has been popular academic topic for decades, swarm intelligence is relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed i...