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Learning-Based Problem Reduction for Large-Scale Uncapacitated Facility Location Problems 13

Learning-Based Problem Reduction for Large-Scale Uncapacitat...

引用

13th IEEE Congress on Evolutionary computation, CEC 2024

作者： Zhang, Shuaixiang Yang, Yixuan Tong, Hao Yao, Xin Lingnan University Department of Computing and Decision Sciences Hong Kong Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350308365

The uncapacitated facility location problem (UFLP) is a well-known combinational optimization problem, attracting numerous heuristic and meta-heuristic methods. However, these effective algorithms still encounter challenges when solving large-scale UFLP instances. To deal with the thousands of decision variables, one possible approach is to predict and remove unimportant facilities and thus decreases the dimensionality of problem instances. This paper explores the problem reduction of large-scale instances of UFLP. We propose suitable features of facilities to construct machine learning models for UFLP by learning from small instances. Based on this model, we introduce a novel problem reduction strategy-based optimization framework, and apply it to large instances. Through comprehen-sive experiments, we show that the proposed problem reduction strategy can effectively transform the UFLP instances to smaller ones. The performance of existing solution methods can be significantly enhanced, especially on large-scale instances. Under the proposed framework, we also show the generalization abilities of our models, which can be improved further. © 2024 IEEE.

关键词： Adversarial machine learning

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Two-Stage Lazy Greedy Inclusion Hypervolume Subset Selection for Large-Scale Problem

Two-Stage Lazy Greedy Inclusion Hypervolume Subset Selection...

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Nan, Yang Shu, Tianye Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350337020

Hypervolume subset selection (HSS) is a hot topic in the evolutionary multi-objective optimization (EMO) community since hypervolume is the most widely-used performance indicator. In the literature, most HSS algorithms were designed for small-scale HSS (e.g., environmental selection: select N solutions from 2N solutions where N is the population size). Few researchers focus on large-scale HSS as a post-processing procedure in an unbounded external archive framework (i.e., subset selection from all examined solutions). In this paper, we propose a two-stage lazy greedy inclusion HSS (TGI-HSS) algorithm for large-scale HSS. In the first stage of TGI- HSS, a small solution set is selected from a large-scale candidate set using an efficient subset selection method (which is not based on exact hypervolume calculation). In the second stage, the final subset is selected from the small solution set using an existing efficient HSS algorithm. Experimental results show that the computational time can be significantly reduced by the proposed algorithm in comparison with other state-of-the-art HSS algorithms at the cost of only a small deterioration of the selected subset quality. © 2023 IEEE.

关键词： Evolutionary multi-objective optimization (EMO) External archive Real-world problems Subset selection

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An Improved Fuzzy Classifier-Based Evolutionary Algorithm for Expensive Multiobjective Optimization Problems with Complicated Pareto Sets 12th

An Improved Fuzzy Classifier-Based Evolutionary Algorithm fo...

引用

12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023

作者： Zhang, Jinyuan He, Linjun Ishibuchi, Hisao Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031272493

Various surrogate-based multiobjective evolutionary algori-thms (MOEAs) have been proposed to solve expensive multiobjective optimization problems (MOPs). However, these algorithms are usually examined on test suites with unrealistically simple Pareto sets (e.g., ZDT and DTLZ test suites). Real-world MOPs usually have complicated Pareto sets, such as a vehicle dynamic design problem and a power plant design optimization problem. Such MOPs are challenging to construct reliable surrogates for surrogate-based MOEAs. Constructed surrogates with low accuracy are likely to make incorrect predictions and even mislead the search direction. In this paper, we propose an improved fuzzy classifier-based MOEA by leveraging the accuracy information of the classifier. The proposed algorithm is compared with five state-of-the-art algorithms on two well-known test suites with complicated Pareto sets and four real-world problems. Experimental results demonstrate the effectiveness of the proposed algorithm in solving realistic MOPs with complicated Pareto sets when only a limited number of function evaluations are available. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Two-Stage Greedy Approximated Hypervolume Subset Selection for Large-Scale Problems 12th

Two-Stage Greedy Approximated Hypervolume Subset Selection f...

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12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023

作者： Nan, Yang Ishibuchi, Hisao Shu, Tianye Shang, Ke Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031272493

Recently, it has been demonstrated that a solution set that is better than the final population can be obtained by subset selection in some studies on evolutionary multi-objective optimization. The main challenge in this type of subset selection is how to efficiently handle a huge candidate solution set, especially when the hypervolume-based subset selection is used for many-objective optimization. In this paper, we propose an efficient two-stage greedy algorithm for hypervolume-based subset selection. In each iteration of the proposed greedy algorithm, a small number of promising candidate solutions are selected in the first stage using the rough hypervolume contribution approximation. In the second stage, a single solution among them is selected using the more precise approximation. Experimental results show that the proposed algorithm is much faster than state-of-the-art hypervolume-based greedy subset selection algorithms at the cost of a slight deterioration of the selected subset quality. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deterioration

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Effects of Initialization Methods on the Performance of Surrogate-Based Multiobjective Evolutionary Algorithms

Effects of Initialization Methods on the Performance of Surr...

引用

2023 IEEE Symposium Series on computational Intelligence, SSCI 2023

作者： Zhang, Jinyuan Ishibuchi, Hisao He, Linjun Nan, Yang Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9781665430654

Initialization plays a crucial role in surrogate-based multiobjective evolutionary algorithms (MOEAs) when tackling computationally expensive multiobjective optimization problems. During the initialization process, solutions are generated to train surrogate models. Consequently, the accuracy of these surrogate models depends on the quality of the initial solutions, which in turn directly impacts the performance of surrogate-based MOEAs. Despite the widespread use of Latin hypercube sampling as an initialization method in surrogate-based MOEAs, there is a lack of comprehensive research examining the effectiveness of different initialization methods. Additionally, the impact of the number of initial solutions on the performance of surrogate-based MOEAs remains largely unexplored. This paper aims to bridge these research gaps by comparing the usefulness of two commonly employed initialization methods (i.e., random sampling and Latin hypercube sampling) in surrogate-based MOEAs. Furthermore, it investigates how varying the number of initial solutions influences the performance of surrogate-based MOEAs. © 2023 IEEE.

关键词： initialization Multiobjective evolutionary algorithms surrogate models

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Performance Evaluation of Multi-objective Evolutionary Algorithms Using Artificial and Real-world Problems 12th

Performance Evaluation of Multi-objective Evolutionary Algor...

引用

12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023

作者： Ishibuchi, Hisao Nan, Yang Pang, Lie Meng Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031272493

Performance of evolutionary multi-objective optimization (EMO) algorithms is usually evaluated using artificial test problems such as DTLZ and WFG. Every year, new EMO algorithms with high performance on those test problems are proposed. One question is whether they also work well on real-world problems. In this paper, we try to find an answer to this question by examining the performance of ten EMO algorithms including both well-known representative algorithms and recently-proposed new algorithms. First, those algorithms are applied to five artificial test suites (DTLZ, WFG, Minus-DTLZ, Minus-WFG and MaF) and three real-world problem suites. The performance of each algorithm is evaluated by the hypervolume indicator. Next, the ranking of the ten EMO algorithms is created for each problem suite. That is, eight different rankings are obtained (each ranking is for each problem suite). Then, the eight different rankings are visually compared to answer our research question. The distance between two rankings is also calculated to support visual comparison results. Our experimental results show that similar rankings of the ten EMO algorithms are obtained for the three real-world problem suites and Minus-WFG. It is also shown that the ranking for each of the three real-world problem suites is clearly different from their ranking for DTLZ. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Effects of External Archives on the Performance of Multi-Objective Evolutionary Algorithms on Real-World Problems

Effects of External Archives on the Performance of Multi-Obj...

引用

2023 IEEE Congress on Evolutionary computation, CEC 2023

ISBN: (纸本)9798350314588

External archives have attracted more and more attention in the evolutionary multi-objective optimization (EMO) community. This is because a solution set selected from an external archive is usually better than the final population of an EMO algorithm. Whereas the effects of subset selection from external archives have already been investigated on artificial test problems, its effects on real-world problems have not been examined. In this paper, we examine the effects of subset selection from external archives for ten EMO algorithms on two real-world problem suites. Experimental results show that the performance improvement by subset selection is large for most algorithms and many problems but small for a few algorithms and a few problems (i.e., algorithm dependent and problem dependent). © 2023 IEEE.

关键词： Multiobjective optimization

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Partially Degenerate Multi-objective Test Problems 12th

Partially Degenerate Multi-objective Test Problems

引用

12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023

作者： Pang, Lie Meng Nan, Yang Ishibuchi, Hisao Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031272493

Degenerate multi-objective test problems are included in test suites to evaluate EMO algorithms on a wide variety of test problems. However, it was pointed out in some studies that the frequently-used degenerate DTLZ5, DTLZ6 and WFG3 test problems do not have degenerate Pareto fronts. Their Pareto fronts are different from the originally intended degenerate shapes. Actually, they are partially degenerate test problems. Modified formulations of DTLZ5 and DTLZ6 were proposed to remove the non-degenerate parts of their Pareto fronts. However, the original formulations of DTLZ5, DTLZ6 and WFG3 continue to be used as degenerate test problems in many studies whereas they are not degenerate test problems. One issue in their use as degenerate test problems is that reference point sets for IGD calculation are sampled from the originally intended degenerate Pareto fronts whereas they are not the true Pareto fronts. Nevertheless, the original DTLZ5, DTLZ6 and WFG3 formulations are useful for performance evaluation of EMO algorithms since their Pareto front shapes are similar to some real-world problems and much more complicated than other test problems. That is, their use helps us to evaluate the performance of EMO algorithms on a wide variety of test problems including realistic and challenging test problems. In this paper, we clearly demonstrate the usefulness of the original DTLZ5, DTLZ6 and WFG3 formulations. Then, after pointing out the difficulty in their use in computational experiments, we explain how we can obtain reliable experimental results on those test problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Adversarial Torn-Paper Codes

Adversarial Torn-Paper Codes

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作者： Bar-Lev, Daniella Marcovich, Sagi Yaakobi, Eitan Yehezkeally, Yonatan Technion - Israel Institute of Technology Department of Computer Science Haifa3200003 Israel Institute for Communications Engineering School of Computation Information and Technology Technical University of Munich Munich80333 Germany

We study the adversarial torn-paper channel. This problem is motivated by applications in DNA data storage where the DNA strands that carry information may break into smaller pieces which are received out of order. Our model extends the previously researched probabilistic setting to the worst-case. We develop code constructions for any parameters of the channel for which non-vanishing asymptotic rate is possible and show our constructions achieve asymptotically optimal rate while allowing for efficient encoding and decoding. Finally, we extend our results to related settings included multi-strand storage, presence of substitution errors, or incomplete coverage. © 1963-2012 IEEE.

关键词： DNA sequences

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A Minimal Model for Compositional Generalization on gSCAN 5

A Minimal Model for Compositional Generalization on gSCAN

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5th Workshop on Analyzing and Interpreting Neural Networks for NLP, BlackboxNLP 2022 hosted by the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Hein, Alice Diepold, Klaus TUM School of Computation Information and Technology Department of Computer Engineering Technical University of Munich Munich Germany

ISBN: (纸本)9781959429050

Whether neural networks are capable of compositional generalization has been a topic of much debate. Most previous studies on this subject investigate the generalization capabilities of state-of-the-art deep learning architectures. We here take a more bottom-up approach and design a minimal model that displays generalization on a compositional benchmark, namely, the gSCAN dataset. The model is a hybrid architecture that combines layers trained with gradient descent and a selective attention mechanism optimized with an evolutionary strategy. The architecture has around 60 times fewer trainable parameters than models previously tested on gSCAN, and achieves comparable accuracies on most test splits, even when trained only on a fraction of the dataset. On adverb to verb generalization accuracy, it outperforms previous approaches by 65 to 86%. Through ablation studies, neuron pruning, and error analyses, we show that weight decay and attention mechanisms facilitate compositional generalization by encouraging sparse representations divorced from irrelevant context. We find that the model’s sample efficiency can mainly be attributed to its selective attention mechanism. © 2022 Association for computational Linguistics.

关键词： Gradient methods

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