Prof. Gary G. Yen, Ph.D., IEEE Fellow Oklahoma State University, USA

Aug 31, 2011 3:00 PM





Evolutionary computation is the study of biologically motivated computational paradigms which exert novel ideas and inspiration from natural evolution and adaptation.  The applications of population-based heuristics in solving constrained and dynamic optimization problems have been receiving a growing interest from computational intelligence community.  Most practical optimization problems are with the existence of constraints and uncertainties in which the fitness function changes through time and is subject to multiple constraints.  In this study, we propose the cultural-based particle swarm optimization (PSO) to solve these problems with real-world complications.  A cultural framework is introduced that incorporates the required information from the PSO into five sections of the belief space, namely situational knowledge, temporal knowledge, domain knowledge, normative knowledge, and spatial knowledge.  The archived information is exploited to detect the changes in the environment and assists response to the change and constraints through a diversity based repulsion among particles and migration among swarms in the population space, also helps in selecting the leading particles in three different levels, personal, swarm, and global level.  Comparison of the proposed cultural based PSO over numerous challenging constrained and dynamic benchmark problems demonstrates the competitive, if not appreciably much better, performance with respect to selected state-of-the-art PSO heuristics. In addition, an ensemble method on performance metrics is proposed, knowing no single metric alone can faithfully quantify the performance of a given design under real-world scenarios. A collection of performance metrics, measuring the spread across the Pareto-optimal front and the ability to attain the global trade-off surface closeness, could be incorporated into the ensemble approach. This design allows a comprehensive measure and more importantly reveals additional insight pertaining to specific problem characteristics that the underlying MOEA could perform the best.



This page is maintained by Yumi Yi (
Last update: Oct 31, 2011