ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Image compression and recovery through compressive sampling and particle swarm
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A hybrid search strategy to enhance multiple objective optimization
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
A modified particle swarm optimizer for engineering design
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
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Many engineering design problems are characterized by presence of several conflicting objectives. This requires efficient search of the feasible design region for optimal solutions which simultaneously satisfy multiple design objectives. The search is further complicated in view of the fact that because of inherent manufacturing variations it is often necessary to allocate tolerances to design variables while guaranteeing low variances for product/process performance measures. Particle swarm optimization (PSO) is a powerful search technique with faster convergence rates than traditional evolutionary algorithms. This paper introduces a new PSO-based approach to multiobjective engineering design by incorporating the central qualitycontrol notion of tolerance design Unlike classical optimization techniques which rely on single-point representation of designs, the modified PSO algorithm allocates tolerances to design variables and flies a swarm of hypercubic particles through the feasible space. To demonstrate the utility of the proposed method, the multiobjective design of an I-beam will be presented.