Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Soft computing methods applied to the control of a flexible robot manipulator
Applied Soft Computing
Soft computing and cooperative strategies for optimization
Applied Soft Computing
An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem
Applied Soft Computing
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Applied Soft Computing
Size optimization of space trusses using Big Bang-Big Crunch algorithm
Computers and Structures
A new optimization method: Big Bang-Big Crunch
Advances in Engineering Software
Fusion of soft computing and hard computing for large-scale plants: a general model
Applied Soft Computing
Parameter identification of chaotic dynamic systems through an improved particle swarm optimization
Expert Systems with Applications: An International Journal
Review article: A review of soft computing applications in supply chain management
Applied Soft Computing
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
A hybrid 'bee(s) algorithm' for solving container loading problems
Applied Soft Computing
The best-so-far selection in Artificial Bee Colony algorithm
Applied Soft Computing
Soft-computing models for soot-blowing optimization in coal-fired utility boilers
Applied Soft Computing
Artificial Bee Colony algorithm for optimization of truss structures
Applied Soft Computing
Expert Systems with Applications: An International Journal
Bee colony optimization for the p-center problem
Computers and Operations Research
A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem
Information Sciences: an International Journal
On the performance of bee algorithms for resource-constrained project scheduling problem
Applied Soft Computing
A soft computing approach for privacy requirements engineering: The PriS framework
Applied Soft Computing
SAR image segmentation based on Artificial Bee Colony algorithm
Applied Soft Computing
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimal design of constraint engineering systems: application of mutable smart bee algorithm
International Journal of Bio-Inspired Computation
Enhanced combination modeling method for combustion efficiency in coal-fired boilers
Applied Soft Computing
An efficient and robust artificial bee colony algorithm for numerical optimization
Computers and Operations Research
Artificial bee colony algorithm and pattern search hybridized for global optimization
Applied Soft Computing
Artificial bee colony algorithm: a survey
International Journal of Advanced Intelligence Paradigms
A novel artificial bee colony algorithm with Powell's method
Applied Soft Computing
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
A hybrid metaheuristic for the cyclic antibandwidth problem
Knowledge-Based Systems
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Artificial bee colony algorithm (ABC), which is inspired by the foraging behavior of honey bee swarm, is a biological-inspired optimization. It shows more effective than genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). However, ABC is good at exploration but poor at exploitation, and its convergence speed is also an issue in some cases. For these insufficiencies, we propose an improved ABC algorithm called I-ABC. In I-ABC, the best-so-far solution, inertia weight and acceleration coefficients are introduced to modify the search process. Inertia weight and acceleration coefficients are defined as functions of the fitness. In addition, to further balance search processes, the modification forms of the employed bees and the onlooker ones are different in the second acceleration coefficient. Experiments show that, for most functions, the I-ABC has a faster convergence speed and better performances than each of ABC and the gbest-guided ABC (GABC). But I-ABC could not still substantially achieve the best solution for all optimization problems. In a few cases, it could not find better results than ABC or GABC. In order to inherit the bright sides of ABC, GABC and I-ABC, a high-efficiency hybrid ABC algorithm, which is called PS-ABC, is proposed. PS-ABC owns the abilities of prediction and selection. Results show that PS-ABC has a faster convergence speed like I-ABC and better search ability than other relevant methods for almost all functions.