A Practical Approach to Subset Selection for Multi-objective Optimization via Simulation

We describe a practical two-stage algorithm, BootComp, for multi-objective optimization via simulation. Our algorithm finds a subset of good designs that a decision-maker can compare to identify the one that works best when considering all aspects of the system, including those that cannot be modeled. BootComp is designed to be straightforward to implement by a practitioner with basic statistical knowledge in a simulation package that does not support sequential ranking and selection. These requirements restrict us to a two-stage procedure that works with any distributions of the outputs and allows for the use of common random numbers. Comparisons with sequential ranking and selection methods suggest that it performs well, and we also demonstrate its use analyzing a real simulation aiming to determine the optimal ward configuration for a UK hospital.

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Replicated Computational Results (RCR) Report for“A Practical Approach to Subset Selection for Multi-Objective Optimization via Simulation”

ACM Transactions on Modeling and Computer Simulation ◽

10.1145/3453987 ◽

2021 ◽

Vol 31 (4) ◽

pp. 1-2

Author(s):

Philipp Andelfinger

Keyword(s):

Subset Selection ◽

Ranking And Selection ◽

Practical Approach ◽

Multi Objective Optimization ◽

Multi Objective ◽

Optimization Via Simulation ◽

Stage Scheme ◽

Simulation Based ◽

Selection For ◽

Configuration Problem

In “A Practical Approach to Subset Selection for Multi-Objective Optimization via Simulation,” Currie and Monks propose an algorithm for multi-objective simulation-based optimization. In contrast to sequential ranking and selection schemes, their algorithm follows a two-stage scheme. The approach is evaluated by comparing the results to those obtained using the existing OCBA-m algorithm for synthetic problems and for a hospital ward configuration problem. The authors provide the Python code used in the experiments in the form of Jupyter notebooks. The code successfully reproduced the results shown in the article.

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Efficient parent selection for Approximation-Guided Evolutionary multi-objective optimization

2013 IEEE Congress on Evolutionary Computation ◽

10.1109/cec.2013.6557784 ◽

2013 ◽

Cited By ~ 5

Author(s):

Markus Wagner ◽

Tobias Friedrich

Keyword(s):

Multi Objective Optimization ◽

Multi Objective ◽

Parent Selection ◽

Selection For

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A Multi-Objective Optimization via Simulation Framework for Restructuring Traffic Networks Subject to Increases in Population

Operations Research/Computer Science Interfaces Series - Recent Developments in Metaheuristics ◽

10.1007/978-3-319-58253-5_13 ◽

2017 ◽

pp. 199-218 ◽

Cited By ~ 1

Author(s):

Enrique Gabriel Baquela ◽

Ana Carolina Olivera

Keyword(s):

Traffic Networks ◽

Multi Objective Optimization ◽

Simulation Framework ◽

Multi Objective ◽

Optimization Via Simulation

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Multi-Objective Optimization of Two-Stage Thermo-Electric Cooler Using Differential Evolution

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Handbook of Research on Computational Simulation and Modeling in Engineering ◽

10.4018/978-1-4666-8823-0.ch004 ◽

2016 ◽

pp. 139-170

Author(s):

Doan V. K. Khanh ◽

Pandian Vasant ◽

Irraivan Elamvazuthi ◽

Vo N. Dieu

Keyword(s):

Genetic Algorithm ◽

Differential Evolution ◽

Cooling Rate ◽

Input Current ◽

Coefficient Of Performance ◽

Multi Objective Optimization ◽

Two Stage ◽

Thermo Electric ◽

Multi Objective ◽

Technical Issues

In this chapter, the technical issues of two-stage TEC were discussed. After that, a new method of optimizing the dimension of TECs using differential evolution to maximize the cooling rate and coefficient of performance was proposed. A input current to hot side and cold side of and the number ratio between the hot stage and cold stage are searched the optima solutions. Thermal resistance is taken into consideration. The results of optimization obtained by using differential evolution were validated by comparing with those obtained by using genetic algorithm and show better performance in terms of stability, computational efficiency, robustness. This work revealed that differential evolution more stable than genetic algorithm and the Pareto front obtained from multi-objective optimization balances the important role between cooling rate and coefficient of performance.

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