Comprehensive Learning Particle Swarm Optimizer (CLPSO) is a state-of-the-art variant of PSO, which maintains the diversity of its swarm by learning from different exemplars on different dimensions. Preserving the swarm diversity enables CLPSO to address the premature convergence problem associated with the canonical PSO. In this paper, the performance of the recently proposed fuzzy-controlled CLPSO (FC-CLPSO) is investigated on 24 problems; five of them are real-world engineering problems and six high-dimensional problems. In addition, two new CLPSO variants inspired by the Artificial Bee Colony (ABC) algorithm are proposed, CLPSO-ABC and FC-CLSPO-ABC. These two methods are compared with CLPSO and FC-CLPSO. The results show that FC-CLPSO-ABC outperforms the other three methods. FC-CLSPO-ABC is then compared with three other state-of-the-art swarm intelligence approaches on 24 problems. The results show that FC-CLPSO-ABC generally outperforms the other approaches.
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