Fast Nash Hybridized Evolutionary Algorithms for Single and Multi-objective Design Optimization in Engineering
Evolutionary Algorithms (EAs) are one of advanced intelligent systems and they occupied an important position in the class of optimizers for solving single-objective/reverse/inverse design and multi-objective/multi physics design problems in engineering. The chapter hybridizes the Genetic Algorithms (GAs) based computational intelligent system (CIS) with the concept of Nash-Equilibrium as an optimization pre-conditioner to accelerate the optimization procedure. Hybridized GAs and simple GAs are validated through solving five complex single-objective and multi-objective mathematical design problems. For real-world design problems, the hybridized GAs (Hybrid Intelligent System) and the original GAs coupled to the Finite Element Analysis (FEA) tool and one type of Computer Aided Design (CAD) system; the GiD software is used to solve reconstruction/inverse and multi-objective design optimization of High Lift Systems (HLS). Numerical results obtained by the hybridized GAs and the original GAs are compared in terms of optimization efficiency and solution quality. The benefits of using the concept of Nash-Equilibrium are clearly demonstrated in terms of solution accuracy and optimization efficiency.
KeywordsComputational Intelligence System (CIS) Reconstruction Inverse Design Multi-Objective Design Evolutionary Optimization Game Coalition Pareto-Optimality Nash-Equilibrium Hybridized Games
The authors would like to thank E. Tercero and the GiD team, R. Flores and E. Ortega at CIMNE for their support and fruitful discussions on the GiD package and PUMI software.
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