By Kalyanmoy Deb (auth.), Lihui Wang, Amos H. C. Ng, Kalyanmoy Deb (eds.)

With the expanding complexity and dynamism in today’s product layout and production, extra optimum, powerful and useful techniques and structures are had to aid product layout and production actions. Multi-objective Evolutionary Optimisation for Product layout and Manufacturing provides a targeted selection of caliber chapters on cutting-edge study efforts in multi-objective evolutionary optimisation, in addition to their useful functions to built-in product layout and production.

Multi-objective Evolutionary Optimisation for Product layout and Manufacturing contains significant sections. the 1st offers a broad-based evaluation of the major parts of analysis in multi-objective evolutionary optimisation. the second one offers in-depth remedies of chosen methodologies and structures in clever layout and built-in manufacturing.

Recent advancements and concepts in multi-objective evolutionary optimisation make Multi-objective Evolutionary Optimisation for Product layout and Manufacturing an invaluable textual content for a extensive readership, from educational researchers to working towards engineers.

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Extra resources for Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

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As the number of objectives increase, EMO 22 K. Deb Fig. 18 The attainment surface is created for a number of non-dominated solutions methodologies exhibit difficulties in converging close to the Pareto-optimal front and the a posteriori approaches become a difficult proposition. In the interactive approach, decison maker (DM) preference information is integrated to an EMO algorithm during the optimisation run. In the progressively interactive EMO approach [39], the DM is called after every s generations and is presented with a few well-diversified solutions chosen from the current nondominated front.

Dynamic multi-objective optimisation and decision-making using modified NSGA-II: A case study on hydro-thermal power scheduling bi-objective optimisation problems. In Proceedings of the fourth international conference on evolutionary multi-criterion optimisation (EMO-2007). 61. , & Padmanabhan, D. (2009). Reliabilitybased optimisation using evolutionary algorithms. IEEE Transactions on Evolutionary Computation 13(5):1054–1074 62. , & Gupta, H. (2006). Introducing robustness in multi-objective optimisation.

30. Sauer, C. G. (1973). Optimization of multiple target electric propulsion trajectories. In AIAA 11th aerospace science meeting (pp. 73–205). 32 K. Deb 31. Knowles, J. , & Corne, D. W. (2002). On metrics for comparing nondominated sets. In Congress on evolutionary computation (CEC-2002) (pp. 711–716). Piscataway, NJ: IEEE Press. 32. Hansen, M. , & Jaskiewicz, A. (1998). Evaluating the quality of approximations to the nondominated set IMM-REP-1998-7. Lyngby: Institute of Mathematical Modelling Technical University of Denmark.

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