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nIn this paper, a binary artificial bee colony (BABC) algorithm is proposed to solve the 0-1 Knapsack problem. Unlike the original ABC algorithm, the proposed BABC represents a food source as a discrete binary variable and applies discrete operators to change the foraging trajectories of the employed bees, onlookers and scouts in the probability that a coordinate will take on a zero or one value. To verify the performance of the proposed algorithms, we made a comparison between algorithms of the proposed BABC with the standard binary particle swarm optimizer (BPSO) and the genetic algorithm (BGA). The comparison results showed that the proposed algorithms outperform the BPSO and BGA in terms of quality of solution found.

The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another's operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. This paper, therefore, aims to use a successful multi-swarm cooperative optimizer called PS2O to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS2O algorithm is proposed in this study. Numerical results for four test cases with different scales, which ranging from 30 readers to 200 readers, have been presented to demonstrate the performance of the proposed methodology.

The Artificial Bee Colony (ABC) algorithm recently gained high popularity by providing a robust and efficient approach for solving continuous optimization problems. In order to apply ABC in discrete landscape, a binary version of artificial bee colony (BABC) algorithm is proposed in this manuscript. Unlike the original ABC algorithm, the proposed BABC represents a food source as a discrete binary variable and applies discrete operators to change the foraging trajectories of the employed bees, onlookers and scouts in the probability that a coordinate will take on a zero or one value. With four mathematical benchmark functions, BABC is proved to have significantly better performance than the other two successful discrete optimizer, namely the genetic algorithm (GA) and particle swarm optimization (PSO).