Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
A Note on the Extended Rosenbrock Function
Evolutionary Computation
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
Structural inverse analysis by hybrid simplex artificial bee colony algorithms
Computers and Structures
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The best-so-far selection in Artificial Bee Colony algorithm
Applied Soft Computing
A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem
Information Sciences: an International Journal
A modified artificial bee colony algorithm
Computers and Operations Research
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, we propose a novel greedy position update strategy for the ABC algorithm. The greedy position update strategy is implemented mainly in two steps. In the first step, good solutions randomly chosen from the top t solutions in the current population are used to guide the search process of onlooker bees. In the second step, the new parameter t is adaptively adjusted in each iteration of the algorithm. The adjustment is simply based on determining whether the globally best solution is obtained by the employed bees or the onlooker bees. The effect of the proposed greedy position update strategy is evaluated on a set of benchmark functions. Experimental results show that the proposed strategy can significantly improve the performance of the classic ABC algorithm. In addition, ABC using the proposed strategy exhibits very competitive performance when compared with some existing ABC variants.