Reference point-based evolutionary multi-objective optimization for reversible logic circuit synthesis

Wang, Xiaoxiao

doi:10.1109/bmei.2014.7002910

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…One application that gained some publicity was reported in Chikumbo et al (2012) and concerned solving a land use management problem in New Zealand, which comprised 14 objectives and 3,150 integer variables. Some other applications using RP-based EMO, in particular R-NSGA-II that can be found are logistics network design (Rajabalipour et al , 2014), reversible logic circuit design (Wang and Wang, 2014) and work-flow scheduling in computing grid (Garg, 2011; Garg and Singh, 2012; Navaz and Ansari, 2012) or cloud computing (Verma and Kaushal, 2015). In manufacturing, there are very few reported applications of R-NSGA-II, except, for instance, tool sequence and parameter optimization of rough milling (Churchill et al , 2013a, 2013b).…”

Section: Dr-augmented R-nsga-iimentioning

confidence: 99%

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

Siegmund

Deb

2018

JSIT

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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.

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Section: Dr-augmented R-nsga-iimentioning

confidence: 99%

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

Siegmund

Deb

2018

JSIT

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Guided Search-Based Multi-Objective Evolutionary Algorithm for Grid Workflow Scheduling

Garg

2019

Exploring Critical Approaches of Evolutionary Computation

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The computational grid provides the global computing infrastructure for users to access the services over a network. However, grid service providers charge users for the services based on their usage and QoS level specified. Therefore, in order to optimize the grid workflow execution, a robust multi-objective scheduling algorithm is needed considering economic cost along with execution performance. Generally, in multi-objective problems, simulations rely on running large number of evaluations to obtain the accurate results. However, algorithms that consider the preferences of decision maker, convergence to optimal tradeoff solutions is faster. Thus, in this chapter, the author proposed the preference-based guided search mechanism into MOEAs. To obtain solutions near the pre-specified regions of interest, the author has considered two MOEAs, namely R-NSGA-II and R-ε-MOEA. Further, to improve the diversity of solutions, a modified form called M-R-NSGA-II is used. Finally, the experimental settings and performance metrics are presented for the evaluation of the algorithms.

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Reference point-based evolutionary multi-objective optimization for reversible logic circuit synthesis

Cited by 2 publications

References 21 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

Guided Search-Based Multi-Objective Evolutionary Algorithm for Grid Workflow Scheduling

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