A Running Performance Metric and Termination Criterion for Evaluating Evolutionary Multi- and Many-objective Optimization Algorithms

Blank, Julian; Deb, Kalyanmoy

doi:10.1109/cec48606.2020.9185546

Cited by 31 publications

(14 citation statements)

References 14 publications

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“…The convergence of the NSGA-II algorithm is analyzed for the designed parameters. A newly proposed running performance metric based on the calculation of Inverted Generational Distance (IGD) is used to estimate the convergence of the NSGA-II algorithm 43,46 . This running metric shows the difference in the objective space from the initial generation to the current generation.…”

Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

Post-prognostics decision-making strategy for load allocation on a stochastically deteriorating multi-stack fuel cell system

Zuo

Cadet

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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This work proposes a load allocation decision strategy based on deterioration prognostics information for multi-stack fuel cell systems. The fuel cell deterioration is characterized by the overall resistance value, as it carries the key aging information of a fuel cell. The fuel cell deterioration dynamics is then modeled as an increasing stochastic process whose trend is a function of the fuel cell output power. Combining system deterioration and fuel cell consumption, a multi-objective optimization (MOO) based decision-making strategy is proposed to manage the operation of a multi-stack fuel cell system. Based on this algorithm, the optimal operating power load is computed for each stack. Finally, the performance of the proposed approach is compared to the case without post-prognostics decision for a three-stack fuel cell system. The simulation results show that the proposed post-prognostics decision-making strategy can manage fuel cell system operating in real time by scheduling the optimal load allocation among stacks.

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Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

Post-prognostics decision-making strategy for load allocation on a stochastically deteriorating multi-stack fuel cell system

Zuo

Cadet

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…According to [38] the most interesting stopping criterion is to use objective space change to decide whether to terminate the algorithm. This termination criteria uses a simple and efficient procedure to determine whether to stop the optimization or not.…”

Section: Terminationmentioning

confidence: 99%

“…This termination criteria uses a simple and efficient procedure to determine whether to stop the optimization or not. This termination procedure is called 'MultiObjectiveSpaceToleranceTermination', and is imported from pymoo as given by the actual code below: The five (5) termination parameters [38] above are described as follows:…”

Section: Terminationmentioning

confidence: 99%

Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line

Ruane

Walsh

Cosgrove

2022

Acta Tech. Jaurinensis

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Simulation in manufacturing is often applied in situations where conducting experiments on a real system is very difficult often because of cost or the time to carry out the experiment is too long. Optimization is the organized search for such designs and operating modes to find the best available solution from a set of feasible solutions. It determines the set of actions or elements that must be implemented to achieve an optimized manufacturing line. As a result of being able to concurrently simulate and optimize equipment processes, the understanding of how the actual production system will perform under varying conditions is achieved. The author has adopted an open-source simulation tool (JaamSim) to develop a digital model of an automated tray loader manufacturing system in the Johnson & Johnson Vision Care (JJVC) manufacturing facility. This paper demonstrates how a digital model developed using JaamSim was integrated with an author developed genetic algorithm optimization system and how both tools can be used for the optimization and development of an automated manufacturing line in the medical devices industry.

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“…Hypervolume (Fonseca et al 2006) and running metric (Blank & Deb 2020a) are used to assess the performance of optimisation algorithms. Hypervolume is a commonly used metric for tracking the progression of multiobjective optimisation problems.…”

Section: Optimisation Algorithmsmentioning

confidence: 99%

“…This is further validated by attempting to run the algorithms till 2000 generations, for which similar trends were observed. The running metric (Blank & Deb 2020a, 2020b) is used to track the progress of the optimisation algorithm. Two plots of the running metric for NSGA-III and C-TAEA are shown respectively in Figure 6(c) and 6(d).…”

Section: Historic Rainfall Eventmentioning

confidence: 99%

Multiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario

Dwivedula

Madhuri

Raju

et al. 2021

Water Science and Technology

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Urban floods cause massive damage to infrastructure and loss of life. This research is being carried out to study how Best Management Practices (BMPs) can mitigate the negative effects of urban floods during extreme rainfall events. Strategically placing BMPs throughout open areas and rooftops in urban areas serves multiple purposes of storage of rainwater, removal of pollutants from surface runoff and sustainable utilisation of land. This situation is framed as a multiobjective optimisation problem to analyse the trade-offs between multiple goals of runoff reduction, construction cost and pollutant load reduction. Output includes a wide range of choices to choose from for decision makers. Proposed methodology is demonstrated with a case study of Greater Hyderabad Municipal Corporation (GHMC), India. Historical extreme rainfall event of 237.5 mm which occurred in year 2016 and extreme rainfall event of 1,740.62 mm corresponding to Representative Concentration Pathway (RCP) 2.6 were considered for analysis. Two multiobjective optimisation algorithms, namely, Non-dominated Sorting Genetic Algorithm – III (NSGA-III) and Constrained Two-Archive Evolutionary Algorithm (C-TAEA) are employed to solve the BMP placement problem, following which the resulting pareto-fronts are ensembled. K-Medoids-based cluster analysis is performed on the resulting ensembled pareto-front. The proposed ensembled approach identified ten possible BMP configurations with costs ranging from Rs. to surface runoff reduction ranging from to and pollutant load removal ranging from tonnes. Use of BMPs in future event has the potential to reduce surface runoff from , while simultaneously removing tonnes of pollutants for cost ranging from The proposed framework forms an effective and novel way to characterise and solve BMP optimisation problems in context of climate change, presenting a view of the urban flooding scenario today, and the likely course of events in the future.

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A Running Performance Metric and Termination Criterion for Evaluating Evolutionary Multi- and Many-objective Optimization Algorithms

Cited by 31 publications

References 14 publications

Post-prognostics decision-making strategy for load allocation on a stochastically deteriorating multi-stack fuel cell system

Post-prognostics decision-making strategy for load allocation on a stochastically deteriorating multi-stack fuel cell system

Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line

Multiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario

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