A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives

Seada, Haitham; Deb, Kalyanmoy

doi:10.1109/tevc.2015.2459718

Cited by 110 publications

(58 citation statements)

References 36 publications

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“…Algorithm Reference GA DE [38] NSGA-II [12] RNSGA-II [14] NSGA-III [10,26,4] UNSGA-III [43] RNSGA-III [47] MOEAD [52] to 1. This can be achieved by supplying this as a parameter in the initialization method as shown in Section 3.…”

Section: Algorithmsmentioning

confidence: 99%

Pymoo: Multi-Objective Optimization in Python

2020

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A B S T R A C TPython has become the programming language of choice for research and industry projects related to data science, machine learning, and deep learning. Since optimization is an inherent part of these research fields, more optimization related frameworks have arisen in the past few years. Only a few of them support optimization of multiple conflicting objectives at a time, but do not provide comprehensive tools for a complete multi-objective optimization task. To address this issue, we have developed pymoo, a multi-objective optimization framework in Python. We provide a guide to getting started with our framework by demonstrating the implementation of an exemplary constrained multiobjective optimization scenario. Moreover, we give a high-level overview of the architecture of pymoo to show its capabilities followed by an explanation of each module and its corresponding sub-modules. The implementations in our framework are customizable and algorithms can be modified/extended by supplying custom operators. Moreover, a variety of single, multi and many-objective test problems are provided and gradients can be retrieved by automatic differentiation out of the box. Also, pymoo addresses practical needs, such as the parallelization of function evaluations, methods to visualize low and high-dimensional spaces, and tools for multi-criteria decision making. For more information about pymoo, readers are encouraged to visit: https://pymoo.org

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Section: Algorithmsmentioning

confidence: 99%

Pymoo: Multi-Objective Optimization in Python

2020

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“…There are several genetic algorithms among which we have: Nondominated Sorting Genetic Algorithm (NSGA), NSGA-II, NSGA-III and U-NSGA-III. The U-NSGA-III is developed in 2015 by Seada and Deb [17]. U-NSGA-III begins with a set of N randomly generated population individuals and a set of reference points.…”

Section: The Genetic Algorithm U-nsga-iiimentioning

confidence: 99%

Optimal Placement of an Unified Power Flow Controller in a Transmission Network by Unified Non Dominated Sorting Genetic Algorithm-III and Differential Evolution Algorithm

Oloulade¹,

Adolphe²,

François-Xavier³

et al. 2019

IJECEC

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The interconnected transmission network of the Benin Electric Community (CEB) presents excessive losses that will increase further by 2025 and severely degrade the power quality supplying. This paper presents the quarto-criterion optimization of the positioning of a UPFC in the CEB's HTB network by U-NSGA III and DE. The positioning of an UPFC on line 3 and node 6 of this network has qualitatively improved the technical performance of this transmission network with an optimal installation cost of US $ 13126657.03. The hypervolumes of the two calculated methods showed that the U-NSGA-III is more accurate in terms of trend toward global solutions than DE. These convincing results confirm the performance of the U-NSGA-III method compared to the DE which also resulted in results close to those obtained by the U-NSGA-III with more or less narrow difference.

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“…The "NSGA-II" algorithm is a very popular MOO implementation [54]. However, the more recent Universal-NSGA-III improves scalability to many objectives, which can otherwise lead to a cumbersomely large number of similar solutions [55], Figure 2E. We provide an opensource implementation of Universal-NSGA-III at https://github.com/marknormanread/unsga3.…”

Section: Single Metrics Alone May Not Fully Distinguish Complex Systemsmentioning

confidence: 99%

Strategies for calibrating models of biology

Read

Alden

Timmis

et al. 2018

Briefings in Bioinformatics

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Computational and mathematical modelling has become a valuable tool for investigating biological systems. Modelling enables prediction of how biological components interact to deliver system-level properties and extrapolation of biological system performance to contexts and experimental conditions where this is unknown. A model's value hinges on knowing that it faithfully represents the biology under the contexts of use, or clearly ascertaining otherwise and thus motivating further model refinement. These qualities are evaluated through calibration, typically formulated as identifying model parameter values that align model and biological behaviours as measured through a metric applied to both. Calibration is critical to modelling but is often underappreciated. A failure to appropriately calibrate risks unrepresentative models that generate erroneous insights. Here, we review a suite of strategies to more rigorously challenge a model's representation of a biological system. All are motivated by features of biological systems, and illustrative examples are drawn from the modelling literature. We examine the calibration of a model against distributions of biological behaviours or outcomes, not only average values. We argue for calibration even where model parameter values are experimentally ascertained. We explore how single metrics can be non-distinguishing for complex systems, with multiple-component dynamic and interaction configurations giving rise to the same metric output. Under these conditions, calibration is insufficiently constraining and the model non-identifiable: multiple solutions to the calibration problem exist. We draw an analogy to curve fitting and argue that calibrating a biological model against a single experiment or context is akin to curve fitting against a single data point. Though useful for communicating model results, we explore how metrics that quantify heavily emergent properties may not be suitable for use in calibration. Lastly, we consider the role of sensitivity and uncertainty analysis in calibration and the interpretation of model results. Our goal in this manuscript is to encourage a deeper consideration of calibration, and how to increase its capacity to either deliver faithful models or demonstrate them otherwise.

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A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives

Cited by 110 publications

References 36 publications

Pymoo: Multi-Objective Optimization in Python

Pymoo: Multi-Objective Optimization in Python

Optimal Placement of an Unified Power Flow Controller in a Transmission Network by Unified Non Dominated Sorting Genetic Algorithm-III and Differential Evolution Algorithm

Strategies for calibrating models of biology

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