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This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation(More)
Article Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents a variation on the original ABC algorithm, namely the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. In this work, CABC(More)
Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria. However, the original BFO algorithm possesses a poor convergence behavior compared to the other successful nature-inspired algorithms. This paper presents a variation on the original BFO algorithm, namely the Cooperative(More)
Artificial Bee Colony ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony CABC, which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data(More)
Bacterial foraging optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques like genetic algorithm (GA) and particle swarm(More)