Properties of Nucleon Resonances by means of a Genetic Algorithm

We present an optimization scheme that employs a Genetic Algorithm (GA) to determine the properties of low-lying nucleon excitations within a realistic photo-pion production model based upon an effective Lagrangian. We show that with this modern optimization technique it is possible to reliably assess the parameters of the resonances and the associated error bars as well as to identify weaknesses in the models. To illustrate the problems the optimization process may encounter, we provide results obtained for the nucleon resonances $\Delta$(1230) and $\Delta$(1700). The former can be easily isolated and thus has been studied in depth, while the latter is not as well known experimentally.Comment: 12 pages, 10 figures, 3 tables. Minor correction

Constraint Handling in Efficient Global Optimization

This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.Real-world optimization problems are often subject to several constraints which are expensive to evaluate in terms of cost or time. Although a lot of effort is devoted to make use of surrogate models for expensive optimization tasks, not many strong surrogate-assisted algorithms can address the challenging constrained problems. Efficient Global Optimization (EGO) is a Kriging-based surrogate-assisted algorithm. It was originally proposed to address unconstrained problems and later was modified to solve constrained problems. However, these type of algorithms still suffer from several issues, mainly: (1) early stagnation, (2) problems with multiple active constraints and (3) frequent crashes. In this work, we introduce a new EGO-based algorithm which tries to overcome these common issues with Kriging optimization algorithms. We apply the proposed algorithm on problems with dimension d ≤ 4 from the G-function suite [16] and on an airfoil shape example.This research was partly funded by Tekes, the Finnish Funding Agency for Innovation (the DeCoMo project), and by the Engineering and Physical Sciences Research Council [grant numbers EP/N017195/1, EP/N017846/1]

Analytical Benchmark Problems for Multifidelity Optimization Methods

The paper presents a collection of analytical benchmark problems specifically selected to provide a set of stress tests for the assessment of multifidelity optimization methods. In addition, the paper discusses a comprehensive ensemble of metrics and criteria recommended for the rigorous and meaningful assessment of the performance of multifidelity strategies and algorithms

An Efficient Bi-Level Surrogate Approach for Optimizing Shock Control Bumps under Uncertainty

The assessment of uncertainties is essential in aerodynamic shape optimization problems in order to come up with configurations that are more robust. The influence of aleatory fluctuations in flight conditions and manufacturing tolerances is of primary concern when designing shock control bumps, as their effectiveness is highly sensitive to the shock wave location. However, exploring the stochastic design space for the global robust optimum increases the computational cost, especially when dealing with nonconvex design spaces and multiple local optima. The aim of this paper is to develop a framework for efficient aerodynamic shape optimization under uncertainty by means of a bi-level surrogate approach and to apply it to the robust design of a retrofitted shock control bump over an airfoil. The framework combines a surrogate-based optimization algorithm with an efficient surrogate-based approach for uncertainty quantification. The surrogate-based optimizer efficiently finds the global optimum of a given quantile of the drag coefficient. It outperforms traditional evolutionary algorithms by effectively balancing exploration and exploitation through the combination of adaptive sampling and a moving trust region. At each iteration of the optimization, the surrogate-based uncertainty quantification uses an active infill criterion in order to accurately quantify the quantile of the drag at a reduced number of function evaluations. Two different quantiles of the drag are chosen, the 95% to increase the robustness at off-design conditions, and the 50% for a configuration that is best for day to day operations. In both cases, the optimum configurations lead to an airfoil that is more robust to geometrical and operational uncertainties, compared to the configuration obtained through classical deterministic optimization

An evaluation of three DoE-guided meta-heuristic-based solution methods for a three-echelon sustainable distribution network

This article evaluates the efficiency of three meta-heuristic optimiser (viz. MOGA-II, MOPSO and NSGA-II)-based solution methods for designing a sustainable three-echelon distribution network. The distribution network employs a bi-objective location-routing model. Due to the mathematically NP-hard nature of the model a multi-disciplinary optimisation commercial platform, modeFRONTIER®, is adopted to utilise the solution methods. The proposed Design of Experiment (DoE)-guided solution methods are of two phased that solve the NP-hard model to attain minimal total costs and total CO2 emission from transportation. Convergence of the optimisers are tested and compared. Ranking of the realistic results are examined using Pareto frontiers and the Technique for Order Preference by Similarity to Ideal Solution approach, followed by determination of the optimal transportation routes. A case of an Irish dairy processing industry’s three-echelon logistics network is considered to validate the solution methods. The results obtained through the proposed methods provide information on open/closed distribution centres (DCs), vehicle routing patterns connecting plants to DCs, open DCs to retailers and retailers to retailers, and number of trucks required in each route to transport the products. It is found that the DoE-guided NSGA-II optimiser based solution is more efficient when compared with the DoE-guided MOGA-II and MOPSO optimiser based solution methods in solving the bi-objective NP-hard three-echelon sustainable model. This efficient solution method enable managers to structure the physical distribution network on the demand side of a logistics network, minimising total cost and total CO2 emission from transportation while satisfying all operational constraints

Viscous single and multicomponent airfoil design with genetic algorithms

An optimization procedure aimed at thedesig of multicomponent airfoils forhigHMMSB applications is described. The procedure is based on a multiobjectivegultio algiobjec two #ow solvers have been coupled with thege8][W algWH8;M] a viscous}inviscid interaction method, based on an Euler #ow solver and an integGx boundary layer routine, and a method based on a full potential #ow solver. The "rst model is used forhigxBSW8 con"gSMBHH8;M whereas the second is used to optimize transonic performances for cruise con"g[S]]8;MH The applications described include bothsingM and multiobjectivedesig of ahigM]B8; multicomponent airfoil, and a multipointdesig where the transonic andhigMWMGS requirements are taken into account simultaneously. # 2001 Elsevier Science B.V. AllrigMH reserved. KeyworRn Multiobjective optimization; Multi-pointdesig- Hig-poin Geneticalgic8M[[S Multielement airfoils 1. I56352351 The ge8 of the aerodynamicdesig of a wing for a transport aircraft is to minimize thedrag in cruise condition, whilesatisfying constraints onlifting force,pitching moment andwing structure requirements. The main gn8 of a higxWB8; system, on the other hand, is the maximization of lift; in this case, the objective of the aerodynamicdesig is to achieve maximum lift without massive #ow separation [1]. When numerical optimization is used to approach thedesig problem, the reliability of the results that can be obtained is the same of the aerodynamic analysis models that are used. Common cruise conditions are in the transonicregson and the main contribution todrag comes from the gthe8GHG shock waves. Therefore, from a numerical point of view, a non-viscous #ow "eld solver that can compute thedrag contribution due to weak shock waves is an adequate tool to provide the 0168-874X/01/$ - see fro..