Reference Point Based NSGA-III for Preferred Solutions

Vesikar, Yash; Deb, Kalyanmoy; Blank, Julian

doi:10.1109/ssci.2018.8628819

Cited by 49 publications

(22 citation statements)

References 12 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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“…However, in DSE problems, NSGA-II explores and tries to optimize the entire search space which is not always necessary in practical applications. Many variants of NSGA have been proposed to address this problem [7,9,29,39]. However, all of these methods explore the targeted space based on a reference direction or reference solutions of the objective function.…”

Section: Related Workmentioning

confidence: 99%

Region of Interest-Based Parameter Optimization for Approximate Image Processing on FPGAs

Manuel

Kreddig²,

Conrady

et al. 2021

IJNC

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In recent years, technological advancements in computer hardware systems have been lagging behind the demand for increased computational power, especially in application domains such as signal and image processing. Approximate computing is a design paradigm for efficient system design to overcome this bottleneck by exploiting the resilience of such applications to inaccuracy in their computations and trading off quality for hardware resource savings. Over the years, many approximation techniques have been proposed on various abstraction layers and demonstrated their effectiveness in different applications. Combining multiple methods in a larger system can further increase the resulting benefits. However, this often leads to a non-trivial optimization task of finding the best parameterization across all employed methods. The interaction and influence of error propagation between individual components demand a global optimization of parameters that simultaneously considers all the parameters for each of the approximation techniques used. In this work, we propose a methodology for exploring such highly complex design spaces using a multi-objective genetic algorithm in an FPGA-based system. Simple models are used for the estimation of resource demands in terms of power together with the anticipated quality degradation. The optimization is carried out to determine the trade-off between these objectives. We demonstrate the effectiveness of our approach on a typical color processing pipeline by tailoring the encoding and genetic operations to the needs of this application. To focus the optimization into a relevant region of interest, we propose ROI-NSGA, a novel variant of nondominated solution selection, and compare its optimization efficiency with the traditional NSGA-II approach for the examined case study. Our results show that the models are able to guide the optimization, and that the genetic operations and selections are capable to find Pareto-optimal solutions, among which the desired quality-resource trade-off can be chosen. Besides, the ROI-NSGA based optimization outperforms the results obtained for the case study using the NSGA-II approach within the region of interest.

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“…NSGA-II, whose basic architecture has been proposed in [8], has become a de-facto standard for multi-objective optimization. It has evolved through many contributions [9][10][11] into a comprehensive and flexible computational framework, namely Pymoo [12]. Among the many applications of NSGA-II, beyond those quoted above in the specific water network domain, we mention [13] which considers a multiply constrained bounded knapsack problem arising in the resource allocation in scheduling of the casting in a foundry which is known to be NP-hard.…”

Section: Related Workmentioning

confidence: 99%

Risk aware optimization of water sensor placement

Candelieri

Ponti

Archetti

2021

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Optimal sensor placement (SP) usually minimizes an impact measure, such as the amount of contaminated water or the number of inhabitants affected before detection. The common choice is to minimize the minimum detection time (MDT) averaged over a set of contamination events, with contaminant injected at a different location. Given a SP, propagation is simulated through a hydraulic software model of the network to obtain spatio-temporal concentrations and the average MDT. Searching for an optimal SP is NP-hard: even for mid-size networks, efficient search methods are required, among which evolutionary approaches are often used. A bi-objective formalization is proposed: minimizing the average MDT and its standard deviation, that is the risk to detect some contamination event too late than the average MDT. We propose a data structure (sort of spatio-temporal heatmap) collecting simulation outcomes for every SP and particularly suitable for evolutionary optimization. Indeed, the proposed data structure enabled a convergence analysis of a population-based algorithm, leading to the identification of indicators for detecting problemspecific converge issues which could be generalized to other similar problems. We used Pymoo, a recent Python framework flexible enough to incorporate our problem specific termination criterion. Results on a benchmark and a real-world network are presented. CCS CONCEPTS• Computing methodologies → Machine learning, Modeling and simulation; • Applied computing → Operations research.

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Reference Point Based NSGA-III for Preferred Solutions

Cited by 49 publications

References 12 publications

Pymoo: Multi-Objective Optimization in Python

Pymoo: Multi-Objective Optimization in Python

Region of Interest-Based Parameter Optimization for Approximate Image Processing on FPGAs

Risk aware optimization of water sensor placement

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