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In this paper, a novel ranking-system-based switching particle swarm optimizer (RSPSO) is proposed. In particular, according to a ranking system, the swarm is divided into elite and normal group, then each particle has been assigned a fitness-based (for normal group member) or a distance-based neighborhood (for elite group member). It is remarkable that neighborhood of a particle is time-varying so that communication among swarm during whole searching process is greatly enhanced. In addition, searching process is divided into four stages by the switching framework, where learning strategies and parameter settings are changed in an adaptive way. Moreover, a newly proposed dimensional learning strategy has been hybridized in RSPSO so as to preserve useful information in the swarm and differential evolution algorithm is employed for a further exploration and also diversifying the swarm. Proposed …