Material selection problem can be interpreted as an intricate MCDM problem. The aim of this work is to provide a simple and comprehensive MCDM-based framework for solving this problem. First, we ...review basic characteristics of general material selection problem under MCDM paradigm. For doing so, we have studied over 60 papers11This study demonstrates that a very important number of articles in this field have been published in “Materials and Design” (published by Elsevier). published between 2010 and 2016 as a brief continuation of previous review 42 in this field. On the other hand, many researchers have emphasized in complicated decision problems more than one MCDM methods should be applied to obtain a more trustworthy and safer decision. Under the scrutiny of over 100 scientific articles, COPRAS and TOPSIS are chosen for tackling material selection problem in general. It is observed that the suggested approach by integrating these MCDM techniques is simple and effective. Also we examine the use of DEA as an MCDM tool in material selection problem. It is found that DEA can be employed to handle this problem by considering a classical remark, but MCDM cannot be generally replaced by DEA in this area.
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•TOPSIS and COPRAS are chosen as the best multi-criteria decision making (MCDM) techniques for ranking the alternative materials in general practice.•An excellent agreement between COPRAS and TOPSIS is observed.•DEA can be used in material selection problem as an auxiliary tool by considering a classic rough rule of thumb, which represents the relation between the number of alternatives and the number variables.
•The techniques to build FAHP model are reviewed in terms of four important aspects.•Four types of fuzzy sets are discussed regarding how to establish comparison matrix.•Aggregation and ...defuzzification methods are examined for their pros and cons.•Measurement methods of crisp and fuzzy consistency are compared.•Guidance is provided on choosing suitable techniques; open questions are suggested.
Analytic Hierarchy Process (AHP) is a broadly applied multi-criteria decision-making method to determine the weights of criteria and priorities of alternatives in a structured manner based on pairwise comparison. As subjective judgments during comparison might be imprecise, fuzzy sets have been combined with AHP. This is referred to as fuzzy AHP or FAHP. An increasing amount of papers are published which describe different ways to derive the weights/priorities from a fuzzy comparison matrix, but seldomly set out the relative benefits of each approach so that the choice of the approach seems arbitrary. A review of various fuzzy AHP techniques is required to guide both academic and industrial experts to choose suitable techniques for a specific practical context. This paper reviews the literature published since 2008 where fuzzy AHP is applied to decision-making problems in industry, particularly the various selection problems. The techniques are categorised by the four aspects of developing a fuzzy AHP model: (i) representation of the relative importance for pairwise comparison, (ii) aggregation of fuzzy sets for group decisions and weights/priorities, (iii) defuzzification of a fuzzy set to a crisp value for final comparison, and (iv) consistency measurement of the judgements. These techniques are discussed in terms of their underlying principles, origins, strengths and weakness. Summary tables and specification charts are provided to guide the selection of suitable techniques. Tips for building a fuzzy AHP model are also included and six open questions are posed for future work.
The empirical testing of metaheuristic solution methods for optimization applications should consider the effect of the underlying structure of the optimization problem test instances employed. This ...paper presents a methodology for analyzing the performance of metaheuristics applied to the 0–1 multidemand multidimensional knapsack problem (MDMKP) specifically considering problem structure. This research leverages instance space analysis (ISA) to graphically depict both the multidimensional problem structure and metaheuristic performance. A new instance generation method augments the existing set of test instances; in doing so, it introduces correlation structure into the problem and helps ensure MDMKP instance feasibility. Testing compares four metaheuristics from the literature and trains an interpretable machine learning model to select a metaheuristic for a given instance based on that problem’s meta-features. The results show that the correlation structure meta-features are significant factors affecting metaheuristic performance and that a decision tree model can provide interpretable insights into the algorithm selection problem. This work demonstrates the usefulness of ISA for rigorous empirical testing to enhance understanding the performance of metaheuristics applied to the MDMKP.
•Metaheuristic Selection with Instance Space Analysis.•Propose interpretable Machine Learning method for Algorithm Selection Problem.•Apply Algorithm Selection Problem to Multidemand Multidimensional Knapsack Problem.
•A novel metaheuristic has been proposed that combines an Adaptive Parameter control with a Mutant tournament in a Multi-Objective Differential Evolution (APMT-MODE).•APMT-MODE is addressed for ...solving the Parameters Selection Problem (PSP) for SVM/SVRs.•APMT-MODE uses the Criticism of Lexicographic Ordering (CLO) to adapt the Differential Evolution (DE) with a multi-objective approach to properly obtain solutions yielding a better tradeoff between high precision and a low number of support vectors.•APMT-MODE yields hyperparameters, aiming to minimize the empirical risk and complexity, reducing the probability of underfitting and overfitting.
Parameters Selection Problem (PSP) is a relevant and complex optimization issue in Support Vector Machine (SVM) and Support Vector Regression (SVR), looking for obtaining an optimal set of hyperparameters. In our case, the optimization problem is addressed to obtain models that minimize the number of support vectors and maximize generalization capacity. However, to obtain accurate and low complexity solutions, defining an adequate kernel function and the SVM/SVR’s hyperparameters are necessary, which currently represents a relevant research topic. To tackle this problem, this work proposes a multi-objective metaheuristic named Adaptive Parameter control with Mutant Tournament Multi-Objective Differential Evolution (APMT-MODE). Its performance is first tested in a series of benchmarks for classification and regression problems using simple kernels such as Gaussian and polynomial kernels. In both cases, the APMT-MODE is able to yield more precise and more straightforward solutions using simple kernels. Then, the approach is used on a real case study to create a welding bead depth and width SVR models for a Gas Metal Arc Welding (GMAW) process. Additionally, a study on kernel functions was developed in terms of computational effort, aiming to assess its performance for embedded systems applications.
The Hypervolume Indicator Guerreiro, Andreia P.; Fonseca, Carlos M.; Paquete, Luís
ACM computing surveys,
07/2021, Letnik:
54, Številka:
6
Journal Article
Recenzirano
Odprti dostop
The hypervolume indicator is one of the most used set-quality indicators for the assessment of stochastic multiobjective optimizers, as well as for selection in evolutionary multiobjective ...optimization algorithms. Its theoretical properties justify its wide acceptance, particularly the strict monotonicity with respect to set dominance, which is still unique of hypervolume-based indicators. This article discusses the computation of hypervolume-related problems, highlighting the relations between them, providing an overview of the paradigms and techniques used, a description of the main algorithms for each problem, and a rundown of the fastest algorithms regarding asymptotic complexity and runtime. By providing a complete overview of the computational problems associated to the hypervolume indicator, this article serves as the starting point for the development of new algorithms and supports users in the identification of the most appropriate implementations available for each problem.
This work aims to evaluate and propose matheuristics for the Distinguishing String Selection Problem (DSSP) and the Distinguishing Substring Selection Problems (DSSSP). Heuristics based on ...mathematical programming have already been proposed for String Selection problems in the literature and we are interested in adopting and testing different approaches for those problems. We proposed two matheuristics for both the DSSP and DSSSP by combining the Variable Neighbourhood Search (VNS) metaheuristic and mathematical programming. We compare the linear relaxation, lower bounds found through the branch-and-bound technique, and the matheuristics in three different groups of instances. Computational experiments show that the Basic Core Problem Algorithm (BCPA) finds overall better results for the DSSP. However, it was unable to provide any solutions for some hard DSSSP instances in a reasonable time limit. The two matheuristics based on the VNS have their own niche related to the different groups of instances. They found good solutions for the DSSSP while the BCPA failed. All the obtained data are available in our repository.
Discrimination in selection problems such as hiring or college admission is often explained by implicit bias from the decision maker against disadvantaged demographic groups. In this paper, we ...consider a model where the decision maker receives a noisy estimate of each candidate's quality, whose variance depends on the candidate's group—we argue that such differential variance is a key feature of many selection problems. We analyze two notable settings: in the first, the noise variances are unknown to the decision maker who simply picks the candidates with the highest estimated quality independently of their group; in the second, the variances are known and the decision maker picks candidates having the highest expected quality given the noisy estimate. We show that both baseline decision makers yield discrimination, although in opposite directions: the first leads to underrepresentation of the low-variance group while the second leads to underrepresentation of the high-variance group. We study the effect on the selection utility of imposing a fairness mechanism that we term the γ-rule (it is an extension of the classical four-fifths rule and it also includes demographic parity). In the first setting (with unknown variances), we prove that under mild conditions, imposing the γ-rule increases the selection utility—here there is no trade-off between fairness and utility. In the second setting (with known variances), imposing the γ-rule decreases the utility but we prove a bound on the utility loss due to the fairness mechanism.
In this work, a discrete-time mean-field type stochastic optimal control problem is studied. The goal is to derive the stochastic maximum principle with convex control domains. L-derivative is ...applied to handle the mean-field term and a technique of adjoint operator is used to overcome the difficulties of obtaining adjoint equations and duality relation. Then, the stochastic maximum principle for discrete-time mean-field type stochastic optimal control problem is established. Finally, as an illustration of our stochastic maximum principle, a discrete-time mean–variance portfolio selection problem is solved with the decoupling technique which is different from the continuous-time case.
Hotel selection method based on online evaluations has become a hot research topic. The existing models based on online ratings or reviews from one website have a disadvantage of information being ...definite and information amount being small. Therefore, this paper proposes a hotel selection model based on Probabilistic linguistic Term Set (PLTS) which integrates online ratings and reviews from multiple websites: (1) Unifying the rating information’s evaluation attributes among different websites based on the PLTS similarity calculation method, putting forward the transformation method of linguistic scale to unify the rating information’s evaluation scale among different websites; (2) Analyzing the sentiment of review texts and putting forward the aggregation model of user reviews based on different groups' risk attitudes; (3) Improving the linguistic scale function to introduce the unbalanced effect of positive and negative evaluations; (4) According to preference differences among different groups, putting forward the attribute weight calculation method and providing recommendation results for different groups. Take four hotels on TripAdvisor, Ctrip and Hostelworld websites for case studies. The results show that information can be used to a greater extent by integrating online ratings and reviews from multiple websites, thus providing consumers with more objective and reliable decision-making results.
•A video summarization framework extracting key-frames that jointly reconstruct the original video and are salient.•Three specific algorithms (numerical, greedy, genetic) based on the Column Subset ...Selection Problem (CSSP) are derived.•The saliency terms are novel, video-oriented modifications of state-of-the-art image saliency algorithms.•An objective metric (Independence Ratio) is proposed for activity video key-frame extraction evaluation.
Recently, dictionary learning methods for unsupervised video summarization have surpassed traditional video frame clustering approaches. This paper addresses static summarization of videos depicting activities, which possess certain recurrent properties. In this context, a flexible definition of an activity video summary is proposed, as the set of key-frames that can both reconstruct the original, full-length video and simultaneously represent its most salient parts. Both objectives can be jointly optimized across several information modalities. The two criteria are merged into a “salient dictionary” learning task that is proposed as a strict definition of the video summarization problem, encapsulating many existing algorithms. Three specific, novel video summarization methods are derived from this definition: the Numerical, the Greedy and the Genetic Algorithm. In all formulations, the reconstruction term is modeled algebraically as a Column Subset Selection Problem (CSSP), while the saliency term is modeled as an outlier detection problem, a low-rank approximation problem, or a summary dispersion maximization problem. In quantitative evaluation, the Greedy Algorithm seems to provide the best balance between speed and overall performance, with the faster Numerical Algorithm a close second. All the proposed methods outperform a baseline clustering approach and two competing state-of-the-art static video summarization algorithms.
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