Swarm intelligence is a research field that models the collective intelligence in swarms of insects or animals. Many algorithms that simulates these models have been proposed in order to solve a wide range of problems. The Artificial Bee Colony algorithm is one of the most recent swarm intelligence based algorithms which simulates the foraging behaviour of honey bee colonies. In this work, modi...

This paper presents an artificial bee colony (ABC) algorithm adjusted for the capacitated vehicle routing problem. The vehicle routing problem is an NP-hard problem and capacitated vehicle routing problem variant (CVRP) is considered here. The artificial bee metaheuristic was successfully used mostly on continuous unconstrained and constrained problems. Here this algorithm has been implemented ...

Bee colony optimization (BCO) is one of the most recent algorithms in swarm intelligence that can be used in optimization problems this algorithm is based on the intelligent behavior of honey bees in foraging process. In this paper bee colony optimization is applied to solve the task scheduling problem which tasks have dependency with each other. Scheduling of tasks that represents by directed ...

Various natural systems teach us that very simple individual organisms can create systems able to perform highly complex tasks by dynamically interacting with each other. The Bee Colony Optimization Metaheuristic (BCO) is proposed in this paper. The artificial bee colony behaves partially alike, and partially differently from bee colonies in nature. The BCO is capable to solve deterministic com...

Artificial Bee Colony algorithm is a global optimization algorithm which is motivated by the foraging behavior of honey bee swarms. Basic Artificial Bee Colony algorithm (ABC) has the advantages of strong robustness, fast convergence and high flexibility, fewer setting parameters, but it has the disadvantages premature convergence in the later search period and the accuracy of the optimal value...

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. The Artificial Bee Colony algorithm is an optimization algorithm based on the intelligent behavior ...

Proteins interact with each other in a highly specific manner, and protein interactions play a key role in many cellular processes. Since protein interactions determine the outcome of most cellular processes, so identifying and characterizing Protein– Protein interactions and their networks are essential for understanding the mechanisms of biological processes on a molecular level. This paper e...

Artificial bee colony algorithms belong to the paradigm of bio-inspired, population-based, algorithms that have been widely used to solve optimization problems. These algorithms use population of individuals/particles/bees/ants in order to explore a search space of potential solutions to a given problem and to be able to quickly converge to a global solution, or at least to a good solution. The...