SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget

In the context of industrial engineering, it is important to integrate efficient computational optimization methods in the product development process. Some of the most challenging simulation-based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradients, highly non-linear objectives and a limited function evaluation budget. Although a huge variety of different optimization algorithms is available, the development and selection of efficient algorithms for problems with these industrial relevant characteristics, remains a challenge. In this communication, a hybrid variant of Differential Evolution (DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG) methods within the framework of DE, in order to improve optimization efficiency on problems with the previously mentioned characteristics. The performance of the resulting derivative-free algorithm is compared with other state-of-the-art DE variants on 25 commonly used benchmark functions, under tight function evaluation budget constraints of 1000 evaluations. The experimental results indicate that the new algorithm performs excellent on the 'difficult' (high dimensional, multi-modal, inseparable) test functions. The operations used in the proposed mutation scheme, are computationally inexpensive, and can be easily implemented in existing differential evolution variants or other population-based optimization algorithms by a few lines of program code as an non-invasive optional setting. Besides the applicability of the presented algorithm by itself, the described concepts can serve as a useful and interesting addition to the algorithmic operators in the frameworks of heuristics and evolutionary optimization and computing

Multi-objective optimization of gate location and processing conditions in injection molding using MOEAs: experimental assessment

The definition of the gate location in injection molding is one of the most important factors in achieving dimensionally accuracy of the parts. This paper presents an optimization methodology for addressing this problem based on a Multi-objective Evolutionary Algorithm (MOEA). The algorithm adopted here is named Reduced Pareto Set Genetic Algorithm (RPSGA) and was used to create a balanced filling pattern using weld line characterization. The optimization approach proposed in this paper is an integration of evolutionary algorithms with Computer-Aided Engineering (CAE) software (Autodesk Moldflow Plastics software). The performance of the proposed optimization methodology was illustrated with an example consisting in the injection of a rectangular part with a non-symmetrical hole. The numerical results were experimentally assessed. Physical meaning was obtained which guaranteed a successful process optimization.This work was supported by the Portuguese Fundação para a Ciência e Tecnologia under grant SFRH/BD/28479/2006 and IPC/I3N – Institute for Polymers and Composites, University of Minho.info:eu-repo/semantics/publishedVersio

Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling

In decision theory, the weighted sum model (WSM) is the best known Multi-Criteria Decision Analysis (MCDA) approach for evaluating a number of alternatives in terms of a number of decision criteria. Assigning weights is a difficult task, especially if the number of criteria is large and the criteria are very different in character. There are some problems in the real world which utilize conflicting criteria and mutual effect. In the field of automotive, the knocking phenomenon in internal combustion or spark ignition engines limits the efficiency of the engine. Power and fuel economy can be maximized by optimizing some factors that affect the knocking phenomenon, such as temperature, throttle position sensor, spark ignition timing, and revolution per minute. Detecting knocks and controlling the above factors or criteria may allow the engine to run at the best power and fuel economy. The best decision must arise from selecting the optimum trade-off within the above criteria. The main objective of this study was to proposed a new Non-Weighted Aggregate Evaluation Function (NWAEF) model for non-linear multi-objectives function which will simulate the engine knock behavior (non-linear dependent variable) in order to optimize non-linear decision factors (non-linear independent variables). This study has focused on the construction of a NWAEF model by using a curve fitting technique and partial derivatives. It also aims to optimize the nonlinear nature of the factors by using Genetic Algorithm (GA) as well as investigate the behavior of such function. This study assumes that a partial and mutual influence between factors is required before such factors can be optimized. The Akaike Information Criterion (AIC) is used to balance the complexity of the model and the data loss, which can help assess the range of the tested models and choose the best ones. Some statistical tools are also used in this thesis to assess and identify the most powerful explanation in the model. The first derivative is used to simplify the form of evaluation function. The NWAEF model was compared to Random Weights Genetic Algorithm (RWGA) model by using five data sets taken from different internal combustion engines. There was a relatively large variation in elapsed time to get to the best solution between the two model. Experimental results in application aspect (Internal combustion engines) show that the new model participates in decreasing the elapsed time. This research provides a form of knock control within the subspace that can enhance the efficiency and performance of the engine, improve fuel economy, and reduce regulated emissions and pollution. Combined with new concepts in the engine design, this model can be used for improving the control strategies and providing accurate information to the Engine Control Unit (ECU), which will control the knock faster and ensure the perfect condition of the engine

A Bayesian approach to constrained single- and multi-objective optimization

This article addresses the problem of derivative-free (single- or multi-objective) optimization subject to multiple inequality constraints. Both the objective and constraint functions are assumed to be smooth, non-linear and expensive to evaluate. As a consequence, the number of evaluations that can be used to carry out the optimization is very limited, as in complex industrial design optimization problems. The method we propose to overcome this difficulty has its roots in both the Bayesian and the multi-objective optimization literatures. More specifically, an extended domination rule is used to handle objectives and constraints in a unified way, and a corresponding expected hyper-volume improvement sampling criterion is proposed. This new criterion is naturally adapted to the search of a feasible point when none is available, and reduces to existing Bayesian sampling criteria---the classical Expected Improvement (EI) criterion and some of its constrained/multi-objective extensions---as soon as at least one feasible point is available. The calculation and optimization of the criterion are performed using Sequential Monte Carlo techniques. In particular, an algorithm similar to the subset simulation method, which is well known in the field of structural reliability, is used to estimate the criterion. The method, which we call BMOO (for Bayesian Multi-Objective Optimization), is compared to state-of-the-art algorithms for single- and multi-objective constrained optimization

Tree-based ensembles unveil the microhabitat suitability for the invasive bleak (Alburnus alburnus L.) and pumpkinseed (Lepomis gibbosus L.): Introducing XGBoost to eco-informatics

[EN] Random Forests (RFs) and Gradient Boosting Machines (GBMs) are popular approaches for habitat suitability modelling in environmental flow assessment. However, both present some limitations theoretically solved by alternative tree-based ensemble techniques (e.g. conditional RFs or oblique RFs). Among them, eXtreme Gradient Boosting machines (XGBoost) has proven to be another promising technique that mixes subroutines developed for RFs and GBMs. To inspect the capabilities of these alternative techniques, RFs and GBMs were compared with: conditional RFs, oblique RFs and XGBoost by modelling, at the micro-scale, the habitat suitability for the invasive bleak (Alburnus alburnus L.) and pumpkinseed (Lepomis gibbosus L). XGBoost outperformed the other approaches, particularly conditional and oblique RFs, although there were no statistical differences with standard RFs and GBMs. The partial dependence plots highlighted the lacustrine origins of pumpkinseed and the preference for lentic habitats of bleak. However, the latter depicted a larger tolerance for rapid microhabitats found in run-type river segments, which is likely to hinder the management of flow regimes to control its invasion. The difference in the computational burden and, especially, the characteristics of datasets on microhabitat use (low data prevalence and high overlapping between categories) led us to conclude that, in the short term, XGBoost is not destined to replace properly optimised RFs and GBMs in the process of habitat suitability modelling at the micro-scale.This project had the support of Fundacion Biodiversidad, of Spanish Ministry for Ecological Transition. We want to thank the volunteering students of the Universitat Politecnica de Valencia, Marina de Miguel, Carlos A. Puig-Mengual, Cristina Barea, Rares Hugianu, and Pau Rodriguez. R. Munoz-Mas benefitted from a postdoctoral Juan de la Cierva fellowship from the Spanish Ministry of Science, Innovation and Universities (ref. FJCI-2016-30829). This research was supported by the Government of Catalonia (ref. 2017 SGR 548).Muñoz-Mas, R.; Gil-Martínez, E.; Oliva-Paterna, FJ.; Belda, E.; Martinez-Capel, F. (2019). Tree-based ensembles unveil the microhabitat suitability for the invasive bleak (Alburnus alburnus L.) and pumpkinseed (Lepomis gibbosus L.): Introducing XGBoost to eco-informatics. Ecological Informatics. 53:1-12. https://doi.org/10.1016/j.ecoinf.2019.100974S1125

A Fast Multi-Objective Optimization Approach to Solve the Continuous Network Design Problem with Microscopic Simulation

The capacity of microscopic traffic simulation to estimate the environmental and road safety impacts opens the possibility to address the Network Design Problem from a new multi-objective point of view. Computation time, however, has hindered the use of this tool. The aim of this thesis was to find a continuous optimization method that would require only a very limited number of evaluations, and thus reduce the computation time. For this purpose, the most recent optimization literature was studied and two algorithms were selected: PAL and SMS-EGO. Both these algorithms rely on Gaussian process meta-models, but they are distinct with respect to the assumptions, criteria and methods used. They were then compared on a real-world case-study with NSGA-II, a genetic algorithm considered as state-of-the-art. Within the very limited computational budget allowed, SMS-EGO was found to outperform PAL and NSGA-II in the three configurations studied. However, the computational time required was still too important to allow for large scale optimization. To further accelerate the optimization process, three main adjustments were proposed, based on variable noise modeling, gradient-based optimization and conditional updates of the meta-models. Considering 20 runs for each optimization process, only variable noise modeling exhibited a statistically significant positive impact. The two other modifications also accelerated the optimization process on average, but high variability in the results led to p-values in the order of 0.15. Overall, the proposed optimization methodology represents a useful tool for transportation researchers to solve multi-objective optimization problems of limited scale