Multi-objective optimization using genetic algorithms: A tutorial

Konak, Abdullah; Coit, David W.; Smith, Alice E.

doi:10.1016/j.ress.2005.11.018

Cited by 2,816 publications

(1,400 citation statements)

References 32 publications

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“…[29][30][31][32][33] GAs have been successfully used in force field development, including fitting of dihedral angle 34,35 and van der Waals 17,25 parameters, atomic polarizabilities, 16 parametrization of coarse-grained 36 and reactive 37,38 force fields, and applied in numerous ad hoc force field parameter optimizations. [39][40][41][42][43] Interestingly, although the assignment of the fixed point charges is a critical part of many force fields, the application of GAs and other evolutionary/stochastic optimization techniques to the MEP pointcharge fitting has not been explored, to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

2015

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Evolutionary methods, such as genetic algorithms (GAs), provide powerful tools for optimization of the force field parameters, especially in the case of simultaneous fitting of the force field terms against extensive reference data. However, GA fitting of the nonbonded interaction parameters that includes point charges has not been explored in the literature, likely due to numerous difficulties with even a simpler problem of the least-squares fitting of the atomic point charges against a reference molecular electrostatic potential (MEP), which often demonstrates an unusually high variation of the fitted charges on buried atoms. Here, we examine the performance of the GA approach for the least-squares MEP point charge fitting, and show that the GA optimizations suffer from a magnified version of the classical buried atom effect, producing highly scattered yet correlated solutions. This effect can be understood in terms of the linearly independent, natural coordinates of the MEP fitting problem defined by the eigenvectors of the least-squares sum Hessian matrix, which are also equivalent to the eigenvectors of the covariance matrix evaluated for the scattered GA solutions. GAs quickly converge with respect to the high-curvature coordinates defined by the eigenvectors related to the leading terms of the multipole expansion, but have difficulty converging with respect to the low-curvature coordinates that mostly depend on the buried atom charges. The performance of the evolutionary techniques dramatically improves when the point charge optimization is performed using the Hessian or covariance matrix eigenvectors, an approach with a significant potential for the evolutionary optimization of the fixed-charge biomolecular force fields.

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Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

2015

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“…There are two approaches to solve the multi-objective optimization problems: converting to a singleobjective problem, and determining the entire Pareto solution [34]. While the former approach returns a single solution, the latter provide a Pareto optimal set as a set of solutions that are not dominated with each other.…”

Section: Methodsmentioning

confidence: 99%

An accurate method for the PV model identification based on a genetic algorithm and the interior-point method

2014

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Citation: Dizqah, Arash, Maheri, Alireza and Busawon, Krishna (2014) Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) An Accurate Method for the PV Model Identification Based on a GeneticAlgorithm and the Interior-Point Method AbstractDue to the PV module simulation requirements as well as recent applications of model-based controllers, the accurate photovoltaic (PV) model identification method is becoming essential to reduce the PV power losses effectively. The classical PV model identification methods use the manufacturers provided maximum power point (MPP) at the standard test condition (STC). However, the nominal operating cell temperature (NOCT) is the more practical condition and it is shown that the extracted model is not well suited to it. The proposed method in this paper estimates an accurate equivalent electrical circuit for the PV modules using both the STC and NOCT information provided by the manufacturers. A multi-objective global optimization problem is formulated using only the main equation of the PV module at these two conditions that restrains the errors due to employing the experimental temperature coefficients. A novel combination of a genetic algorithm (GA) and the interior-point method (IPM) allows the proposed method to be fast and accurate regardless the PV technology. It is shown that the overall error, which is defined by the sum of the MPP errors of both the STC and the NOCT conditions, is improved by a factor between 5.1% to 31% depending on the PV technology.

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“…The solutions found by MOEAs must then be decoded, meaning to map them back to the domainspecific multi-objective optimization problem. This makes it very challenging to properly apply MOEAs and may require developers to focus more on the encoding of a problem than on the problem itself (Konak et al, 2006). The continuous design process of today's software systems makes it even more difficult to implement and especially to maintain MOEAs.…”

Section: Introductionmentioning

confidence: 99%

“…This requires a mapping, called encoding, between the domain specific solution space and chromosomes. Usually, the encoding step is complex and can be even more complicated than the actual optimization problem itself (Konak et al, 2006). EAs operate on a collection of chromosomes, called a population.…”

Section: Introductionmentioning

confidence: 99%

Polymer - A Model-driven Approach for Simpler, Safer, and Evolutive Multi-objective Optimization Development

Moawad

Hartmann

Fouquet

et al. 2015

Proceedings of the 3rd International Conference on Model-Driven Engineering and Software Development

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Abstract:Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully used to optimize various domains such as finance, science, engineering, logistics and software engineering. Nevertheless, MOEAs are still very complex to apply and require detailed knowledge about problem encoding and mutation operators to obtain an effective implementation. Software engineering paradigms such as domain-driven design aim to tackle this complexity by allowing domain experts to focus on domain logic over technical details. Similarly, in order to handle MOEA complexity, we propose an approach, using model-driven software engineering (MDE) techniques, to define fitness functions and mutation operators without MOEA encoding knowledge.Integrated into an open source modelling framework, our approach can significantly simplify development and maintenance of multi-objective optimizations. By leveraging modeling methods, our approach allows reusable optimizations and seamlessly connects MOEA and MDE paradigms. We evaluate our approach on a cloud case study and show its suitability in terms of i) complexity to implement an MOO problem, ii) complexity to adapt (maintain) this implementation caused by changes in the domain model and/or optimization goals, and iii) show that the efficiency and effectiveness of our approach remains comparable to ad-hoc implementations.

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Multi-objective optimization using genetic algorithms: A tutorial

Cited by 2,816 publications

References 32 publications

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

An accurate method for the PV model identification based on a genetic algorithm and the interior-point method

Polymer - A Model-driven Approach for Simpler, Safer, and Evolutive Multi-objective Optimization Development

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