Abstract
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 of each solution is also maintained in a global shared memory. During each cycle, the set of bees at a processor improves the solutions in the local memory. At the end of the cycle, the solutions are copied into the corresponding slots in the shared memory and made available to all other bees. It is shown that the proposed parallelization strategy does not degrade the quality of solutions obtained, but achieves substantial speedup.
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