Collaborative multi-objective optimization for distributed design of complex products

Duro, João A.; Yan, Yiming; Purshouse, Robin C.; Fleming, Peter J.

doi:10.1145/3205455.3205579

Cited by 4 publications

(3 citation statements)

References 14 publications

(29 reference statements)

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“…The Sellar problem has been extensively discussed within the MDO literature, although finds little use beyond basic architecture testing due to its small size and simplicity. Other problems such as those presented in [11] and solved in [12] are more applicable to distributed architectures.…”

Section: Overview Of Multi-objective and Multidisciplinary Test Problemsmentioning

confidence: 99%

Toward scalable benchmark problems for multi-objective multidisciplinary optimization

Johnson

Duro

Kadirkamanathan

et al. 2022

2022 IEEE Symposium Series on Computational Intelligence (SSCI)

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Scalability in disciplines is an important consideration for multidisciplinary design optimization (MDO). Very few benchmark problems for multi-objective MDO exist in the literature, none of which are readily scalable. In this study, we introduce a new scalable benchmark problem that extends an existing well-known multi-objective benchmark problem. We show that scaling the number of disciplines in the problem, implying an increasing number of decision variables, does produce substantive changes in convergence ability. We also show that the accuracy of the multidisciplinary analysis (MDA) solver has an impact on the convergence ability of the multi-objective optimization algorithm, which is particularly noticeable when moving from 7 to 14 disciplines. Modification of standard (non-MDO) multi-objective benchmark problems is a promising approach to developing scalable multi-objective MDO benchmarks.

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Section: Overview Of Multi-objective and Multidisciplinary Test Problemsmentioning

confidence: 99%

Toward scalable benchmark problems for multi-objective multidisciplinary optimization

Johnson

Duro

Kadirkamanathan

et al. 2022

2022 IEEE Symposium Series on Computational Intelligence (SSCI)

Self Cite

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“…The economic modelling will also estimate the accrual profile of health, wellbeing and non-health outcomes over time. WS7 draws on distributed, robust multi-objective optimization techniques from engineering 1, 44 and methods for adaptive policy design which have yet to be used in public health 9 . The latter identify strategies that perform well across different plausible future scenarios, updating projections as uncertainties resolve.…”

Section: Sipher Worktreamsmentioning

confidence: 99%

The SIPHER Consortium: Introducing the new UK hub for systems science in public health and health economic research

Meier¹,

Purshouse²,

Bain³

et al. 2019

Wellcome Open Res

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The conditions in which we are born, grow, live, work and age are key drivers of health and inequalities in life chances. To maximise health and wellbeing across the whole population, we need well-coordinated action across government sectors, in areas including economic, education, welfare, labour market and housing policy. Current research struggles to offer effective decision support on the cross-sector strategic alignment of policies, and to generate evidence that gives budget holders the confidence to change the way major investment decisions are made. This open letter introduces a new research initiative in this space. The SIPHER ( Systems Science in Public Health and Health Economics Research) Consortium brings together a multi-disciplinary group of scientists from across six universities, three government partners at local, regional and national level, and ten practice partner organisations. The Consortium’s vision is a shift from health policy to healthy public policy, where the wellbeing impacts of policies are a core consideration across government sectors. Researchers and policy makers will jointly tackle fundamental questions about: a) the complex causal relationships between upstream policies and wellbeing, economic and equality outcomes; b) the multi-sectoral appraisal of costs and benefits of alternative investment options; c) public values and preferences for different outcomes, and how necessary trade-offs can be negotiated; and d) creating the conditions for intelligence-led adaptive policy design that maximises progress against economic, social and health goals. Whilst our methods will be adaptable across policy topics and jurisdictions, we will initially focus on four policy areas: Inclusive Economic Growth, Adverse Childhood Experiences, Mental Wellbeing and Housing.

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“…A large proportion of the literature or distributed multi-objective MDO centres around multi-objective concurrent subspace optimization (CSSO) [9,13,14,26] and CO [10,23,27,29].…”

Section: Related Literature 21 Introduction To Multi-objective Mdomentioning

confidence: 99%

A distributed multi-disciplinary design optimization benchmark test suite with constraints and multiple conflicting objectives

Johnson

Duro

Kadirkamanathan

et al. 2023

Proceedings of the Companion Conference on Genetic and Evolutionary Computation

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Collaborative multi-objective optimization for distributed design of complex products

Cited by 4 publications

References 14 publications

Toward scalable benchmark problems for multi-objective multidisciplinary optimization

Toward scalable benchmark problems for multi-objective multidisciplinary optimization

The SIPHER Consortium: Introducing the new UK hub for systems science in public health and health economic research

A distributed multi-disciplinary design optimization benchmark test suite with constraints and multiple conflicting objectives

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