A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods

Tabatabaei, Mohammad; Hakanen, Jussi; Hartikainen, Markus; Miettinen, Kaisa; Sindhya, Karthik

doi:10.1007/s00158-015-1226-z

Cited by 102 publications

(58 citation statements)

References 88 publications

(228 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the first approach has already been covered extensively in several surveys [1][2][3], we only give a brief overview over the existing methods and the corresponding references.…”

Section: Solution Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Recent Trends in Multiobjective Optimal Control -- Surrogate Models, Feedback Control and Objective Reduction

Peitz¹,

Dellnitz²

2018

Preprint

View full text Add to dashboard Cite

Abstract:Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto optimal solutions have led to a wide range of new applications related to optimal and feedback control which results in new challenges such as expensive models or real-time applicability. Since the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview over recent developments in accelerating multiobjective optimal control for complex problems where either PDE constraints are present or where a feedback behavior has to be achieved. In the first case, surrogate models yield significant speed-ups. Besides classical meta-modeling techniques for multiobjective optimization, a promising alternative for control problems is to introduce a surrogate model for the system dynamics. In the case of real-time requirements, various promising model predictive control approaches have been proposed, using either fast online solvers or offline-online decomposition. We also briefly comment on dimension reduction in many-objective optimization problems as another technique for reducing the numerical effort.

show abstract

“…Since the first approach has already been covered extensively in several surveys [1][2][3], we only give a brief overview over the existing methods and the corresponding references.…”

Section: Solution Methodsmentioning

confidence: 99%

“…Tabatabei et al [2], Chugh et al [3] Extensive surveys on meta modeling for MOEAs Voutchkov & Keane [74], Surveys on meta modeling approaches from statistics (RSM, Knowles & Nakayama [1], Jin [75] RBF) and machine learning in combination with MOEAs…”

Section: Surveysmentioning

confidence: 99%

A Survey of Recent Trends in Multiobjective Optimal Control -- Surrogate Models, Feedback Control and Objective Reduction

Peitz¹,

Dellnitz²

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…More often than not, acquisition of data is either costly or computationally intensive, seriously limiting the number of function evaluations. One widely adopted technique to achieve acceptable solutions using a small number of function evaluations is to use surrogates, also known as metamodels or approximate function evaluations [8], [21] to replace in part the exact function evaluations in optimization. Management of the surrogate, including when to use and update the surrogates, plays a key role in surrogate-assisted optimization [22].…”

Section: Data-driven Evolutionary Optimizationmentioning

confidence: 99%

“…To reduce the needed number of expensive function evaluations, surrogate-assisted evolutionary algorithms (SAEAs) [22], [8], [21] can be used, where the main idea is to use one [23], [24] or multiple surrogates [25], [26], [27] to approximate the expensive function evaluation globally or locally [28], [29], [30]. Note that an implicit assumption here is that the computational cost for constructing and using the surrogates is much less than that for fitness evaluations using the original expensive function.…”

Section: B Surrogate Models and Surrogate Managementmentioning

confidence: 99%

Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System

Wang

Jin

Jansen

2016

IEEE Trans. Evol. Computat.

214

102

View full text Add to dashboard Cite

Abstract-Most existing work on evolutionary optimization assumes that there are analytic functions for evaluating the objectives and constraints. In the real-world, however, the objective or constraint values of many optimization problems can be evaluated solely based on data and solving such optimization problems is often known as data-driven optimization. In this paper, we divide data-driven optimization problems into two categories, i.e., off-line and on-line data-driven optimization, and discuss the main challenges involved therein. An evolutionary algorithm is then presented to optimize the design of a trauma system, which is a typical off-line data-driven multi-objective optimization problem, where the objectives and constraints can be evaluated using incidents only. As each single function evaluation involves large amount of patient data, we develop a multi-fidelity surrogate management strategy to reduce the computation time of the evolutionary optimization. The main idea is to adaptively tune the approximation fidelity by clustering the original data into different numbers of clusters and a regression model is constructed to estimate the required minimum fidelity. Experimental results show that the proposed algorithm is able to save up to 90% of computation time without much sacrifice of the solution quality.

show abstract

“…To overcome this problem, dedicated expensive MOO algorithms can be employed. Expensive MOO algorithms accept the fact that it is extremely costly to evaluate the objective functions and efficiently utilize limited amount of evaluations to build a model and suggest the point for consecutive evaluation [23].…”

Section: Consider a Cost Functionmentioning

confidence: 99%

Computer architectures to close the loop in real-time optimization

Kerrigan

Constantinides

Suardi

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

Abstract-Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as 'fast' optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other.

show abstract

A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods

Cited by 102 publications

References 88 publications

A Survey of Recent Trends in Multiobjective Optimal Control -- Surrogate Models, Feedback Control and Objective Reduction

A Survey of Recent Trends in Multiobjective Optimal Control -- Surrogate Models, Feedback Control and Objective Reduction

Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System

Computer architectures to close the loop in real-time optimization

Contact Info

Product

Resources

About