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Artificial Bee Colony (ABC) is a metaheuristic approach in which a colony of artificial bees cooperates in finding good solutions for numerical optimization problems. ABC is adopted widely for use in several domains of solution optimization. However, the algorithm generally requires a considerably large computational time and resources. In order to enhance the performance of this algorithm for a large...
In this paper, a communication strategy for the parallelized Artificial Bee Colony (ABC) optimization is proposed for solving numerical optimization problems. The artificial agents are split into several independent subpopulations based on the original structure of the ABC, and the proposed communication strategy provides the information flow for the agents to communicate in different subpopulations...
In this paper, we propose Fish Migration Optimization (FMO) method based on a verified equation of the fish swim in the fish biology for solving numerical optimization problems. Inspired by the fish migration, the migration and the swim model are integrated into the optimization process. Four benchmark functions are used to test the convergence, the accuracy and the speed of FMO, and the experimental...
The artificial bee colony (ABC) algorithm is a metaheuristic algorithm for numerical optimization. It is based on the intelligent foraging behavior of honey bees. This paper presents a parallel version of the algorithm for shared memory architectures. The entire colony of bees is divided equally among the available processors. A set of solutions is placed in the local memory of each processor. A copy...
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