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

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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A New Multi-Objective Optimization Model of Water Resources Considering Fairness and Water Shortage Risk

Water ◽

10.3390/w13192648 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2648

Author(s):

Xiaoyu Tang ◽

Ying He ◽

Peng Qi ◽

Zehua Chang ◽

Ming Jiang ◽

...

Keyword(s):

Water Resources ◽

Optimization Model ◽

Optimal Allocation ◽

Risk Index ◽

Water Shortage ◽

Economic Benefits ◽

Selection Strategy ◽

Multi Objective Optimization ◽

Structural Water ◽

Multi Objective

Assessing the fairness of water resource allocation and structural water shortage risks is an urgent problem that needs to be solved for the optimal allocation of water resources. In this study, we established a new multi-objective optimization model of water resources based on structural water shortage risks and fairness. We propose an improved NSGA-III based on the reference point selection strategy (ARNSGA-III) to solve the optimization model. The superiority of this method was proven by comparing it with three other methods, namely, NSGA-III, MOSPO, and MOEA/D. The model was applied to optimize the allocation of water resources in Wusu City in China. The results show that the new multi-objective optimization model provides reasonable and feasible solutions for solving water conflicts. The convergence and stability of ARNSGA-III are better than those of the other three algorithms. Allocation schemes of water resources for Wusu City in normal years, dry years, and extremely dry years are proposed. In normal years, the structural water shortage risk index is reduced by 50.1%, economic benefits increased by 0.2%, and fairness is reduced by 60.5%. This study can provide new ideas for solving the multi-objective optimization of regional water resources.

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A Multi-Objective Optimization Method for System Reliability Allocation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.860-863.2766 ◽

2013 ◽

Vol 860-863 ◽

pp. 2766-2773

Author(s):

Wei Li ◽

Qiang Zeng ◽

Ling Shen ◽

Ze Bin Zhang

Keyword(s):

System Reliability ◽

Gene Segment ◽

Optimization Method ◽

Selection Strategy ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Reliability Allocation ◽

Multi Objective ◽

Function Unit ◽

Elitist Strategy

A multi-objective optimization method for system reliability allocation was proposed. Firstly, a constrained multi-objective optimization model for system reliability allocation was established with the objective to maximize the system reliability, minimize the cost, volume and mass. Secondly, aiming at the characteristic of the model, a non dominated sorting genetic algorithm with elitist strategy (NSGA II) was presented and designed. In the algorithm, an object-oriented technique was introduced to map each individual to each corresponding object and a population to an array of objects, each individual was encoded by each corresponding vector made up of the selection indices of every function unit, the tournament selection strategy was used to implement the selection operation based on the ranks and congestion degrees of the individuals, a swap way of two-point gene segment was used to implement the crossover operation, a one point mutation way was used to implement the mutation operation and the Pareto operation was implemented based on the ranks and congestion degrees of the individuals. Finally, the effectiveness of the proposed research was validated by case study.

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A multi-objective hyper-heuristic algorithm based on adaptive epsilon-greedy selection

Complex & Intelligent Systems ◽

10.1007/s40747-020-00230-8 ◽

2021 ◽

Author(s):

Tailong Yang ◽

Shuyan Zhang ◽

Cuixia Li

Keyword(s):

Heuristic Algorithm ◽

Performance Indicators ◽

Optimization Problems ◽

Test Suite ◽

Selection Strategy ◽

Multi Objective Optimization ◽

Multi Objective ◽

Cross Domain ◽

The Many ◽

High Level

AbstractA variety of meta-heuristics have shown promising performance for solving multi-objective optimization problems (MOPs). However, existing meta-heuristics may have the best performance on particular MOPs, but may not perform well on the other MOPs. To improve the cross-domain ability, this paper presents a multi-objective hyper-heuristic algorithm based on adaptive epsilon-greedy selection (HH_EG) for solving MOPs. To select and combine low-level heuristics (LLHs) during the evolutionary procedure, this paper also proposes an adaptive epsilon-greedy selection strategy. The proposed hyper-heuristic can solve problems from varied domains by simply changing LLHs without redesigning the high-level strategy. Meanwhile, HH_EG does not need to tune parameters, and is easy to be integrated with various performance indicators. We test HH_EG on the classical DTLZ test suite, the IMOP test suite, the many-objective MaF test suite, and a test suite of a real-world multi-objective problem. Experimental results show the effectiveness of HH_EG in combining the advantages of each LLH and solving cross-domain problems.

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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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