A Tabu Search Approach for the Resource ConstrainedProject Scheduling Problem
Journal of Heuristics
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
Protein Conformational Search Using Bees Algorithm
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
A random key based genetic algorithm for the resource constrained project scheduling problem
Computers and Operations Research
An efficient hybrid algorithm for resource-constrained project scheduling
Information Sciences: an International Journal
A Neurogenetic approach for the resource-constrained project scheduling problem
Computers and Operations Research
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
The performance and sensitivity of the parameters setting on the best-so-far ABC
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Artificial bee colony algorithm: a survey
International Journal of Advanced Intelligence Paradigms
A hybrid metaheuristic for the cyclic antibandwidth problem
Knowledge-Based Systems
Hi-index | 0.00 |
This work investigates the application of bee algorithms for resource constrained project scheduling problem (or RCPSP). Three methods are developed based on recently introduced bee algorithms known as bee algorithm (BA), artificial bee colony (ABC), and bee swarm optimization (BSO). The proposed algorithms iteratively solve the RCPSP by utilizing intelligent behaviors of honey bees. Each algorithm has three main phases: initialization, update, and termination. At first phase, a set of schedules are generated randomly in order to initialize the population of the algorithms. Then, the initial population will be improved iteratively until termination condition is met. The update phase constitutes the body of each algorithm. Each algorithm uses different types of bees to provide appropriate level of exploration over search space while maintaining exploitation of good solutions. Three new local search methods are incorporated into the proposed methods in order to find more efficiency. Also, an efficient constraint handling method is introduced to resolve the infeasible solutions. The performances of the proposed algorithms are compared against a set of state-of-art algorithms. The simulation results showed that bee algorithms provides an efficient way for solving RCPSP and produce competitive results compared to other algorithms investigated in this work.