A ranking and selection strategy for preference-based evolutionary multi-objective optimization of variable-noise problems

Siegmund, Florian; Ng, Amos H. C.; Deb, Kalyanmoy

doi:10.1109/cec.2016.7744173

Cited by 5 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sampling allocation problem that is to be solved by D-OCBA-m is stated in equation (14) (Siegmund et al , 2016). The corresponding notation is given in Table I.…”

Section: Dynamic Resamplingmentioning

confidence: 99%

See 1 more Smart Citation

Reference point based evolutionary multi-objective optimization with dynamic resampling for production systems improvement

Siegmund

Deb

2018

JSIT

Self Cite

View full text Add to dashboard Cite

Purpose Stochastic simulation is a popular tool among practitioners and researchers alike for quantitative analysis of systems. Recent advancement in research on formulating production systems improvement problems into multi-objective optimizations has provided the possibility to predict the optimal trade-offs between improvement costs and system performance, before making the final decision for implementation. However, the fact that stochastic simulations rely on running a large number of replications to cope with the randomness and obtain some accurate statistical estimates of the system outputs, has posed a serious issue for using this kind of multi-objective optimization in practice, especially with complex models. Therefore, the purpose of this study is to investigate the performance enhancements of a reference point based evolutionary multi-objective optimization algorithm in practical production systems improvement problems, when combined with various dynamic re-sampling mechanisms. Design/methodology/approach Many algorithms consider the preferences of decision makers to converge to optimal trade-off solutions faster. There also exist advanced dynamic resampling procedures to avoid wasting a multitude of simulation replications to non-optimal solutions. However, very few attempts have been made to study the advantages of combining these two approaches to further enhance the performance of computationally expensive optimizations for complex production systems. Therefore, this paper proposes some combinations of preference-based guided search with dynamic resampling mechanisms into an evolutionary multi-objective optimization algorithm to lower both the computational cost in re-sampling and the total number of simulation evaluations. Findings This paper shows the performance enhancements of the reference-point based algorithm, R-NSGA-II, when augmented with three different dynamic resampling mechanisms with increasing degrees of statistical sophistication, namely, time-based, distance-rank and optimal computing buffer allocation, when applied to two real-world production system improvement studies. The results have shown that the more stochasticity that the simulation models exert, the more the statistically advanced dynamic resampling mechanisms could significantly enhance the performance of the optimization process. Originality/value Contributions of this paper include combining decision makers’ preferences and dynamic resampling procedures; performance evaluations on two real-world production system improvement studies and illustrating statistically advanced dynamic resampling mechanism is needed for noisy models.

show abstract

“…The sampling allocation problem that is to be solved by D-OCBA-m is stated in equation (14) (Siegmund et al , 2016). The corresponding notation is given in Table I.…”

Section: Dynamic Resamplingmentioning

confidence: 99%

“…Equation (17) can be used because N i should be assigned its correct share of N total . For more details of the iterative D-OCBA-m procedure, readers are referred to Siegmund et al (2016).…”

Section: Dynamic Resamplingmentioning

confidence: 99%

Reference point based evolutionary multi-objective optimization with dynamic resampling for production systems improvement

Siegmund

Deb

2018

JSIT

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition to Ong et al (), thorough investigations have studied multiobjective optimization problems (MOPs) applied to variable noise landscapes. Siegmund et al () proposed multiobjective evolutionary algorithms (MOEAs) to intelligently sample objective function realizations based on various metrics which define a proportional sampling rate including: (1) time based dynamic resampling—where more samples are assigned as the algorithm progresses, (2) rank based dynamic sampling—where more samples are assigned to solutions with lower Pareto rank, (3) standard error based dynamic sampling—where more samples are assigned to those solutions with the highest variability, (4) progress based dynamic sampling, and (5) distance based dynamic sampling—where more samples are assigned to the solutions according to their distance or progress toward a predefined reference point. The aforementioned dynamic sampling methods all outperform static sampling methods where a constant number of samples are prescribed for each candidate solution.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Scenario Evaluation in Evolutionary Algorithms Used for Robust Scenario‐Based Optimization

Sankary

Ostfeld

2018

Water Resources Research

View full text Add to dashboard Cite

This paper focuses on evaluating a scenario‐based multiobjective evolutionary algorithm for real‐world design problems in which the environment where a system will operate is dynamic, and uncertain. Subsequently, the performance of a stochastic scenario selection scheme, inspired by methods to reduce overfitting in genetic programming, is investigated for scenario‐based optimization. Using a scenario‐based scheme to address uncertainty in a real‐world system's operational environment, system designs are developed via aggregating the performance of a solution evaluated across many scenarios. Within each generation of the evolutionary algorithm the evaluation suite is resampled and evaluated by the current generation's solutions. This scheme is evaluated on two historical noisy test problems and two real‐world water resources design problem instances. For each case, the stochastic scenario selection scheme is compared to a static selection scheme at various evaluation suite sizes. Results show the proposed scenario selection scheme to outperform static sampling schemes and increase efficiency of a multiobjective evolutionary algorithm for robust optimization objectives.

show abstract

A Comparative Study of Fast Adaptive Preference-Guided Evolutionary Multi-objective Optimization

Siegmund

Deb

2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A ranking and selection strategy for preference-based evolutionary multi-objective optimization of variable-noise problems

Cited by 5 publications

References 30 publications

Reference point based evolutionary multi-objective optimization with dynamic resampling for production systems improvement

Reference point based evolutionary multi-objective optimization with dynamic resampling for production systems improvement

Stochastic Scenario Evaluation in Evolutionary Algorithms Used for Robust Scenario‐Based Optimization

A Comparative Study of Fast Adaptive Preference-Guided Evolutionary Multi-objective Optimization

Contact Info

Product

Resources

About