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Applying Social Choice Theory to Solve Engineering multi-objective Optimization Problems

JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2020年第1期31卷 119-128页

作者： de Carvalho, Vinicius Renan Larson, Kate Brandao, Anarosa Alves Franco Sichman, Jaime Simao Univ Sao Paulo EP LTI Sao Paulo Brazil Univ Waterloo Cheriton Sch Comp Sci Waterloo ON Canada

multi-objective optimization problems usually do not have a single unique optimal solution, for either discrete or continuous domains. Furthermore, there are usually many possible available algorithms for solving these problems, and one typically does not know in advance which of these will be the most effective for solving a particular problem instance. Hyper-heuristics (HHs) are often used as a means to make this choice. In particular, the underlying idea of HHs is to run several algorithms or heuristics and dynamically decide, based on different criteria, which problem or part of the problem should be solved by which algorithm or heuristic. On the other hand, the domain of social choice theory studies how to design collective decision processes by aggregating individual inputs into collective ones. In this paper, we explore the use of social choice theory in creating HHs. By using HHs based on different voting methods, like Borda, Copeland and Kemeny-Young, we show how we can solve both continuous and discrete engineering multi-objective optimization problems and discuss the results obtained by each of these methods. Our obtained results show that our strategy has found solutions that are at least equals to the ones generated by the best algorithm among the studied ones, and sometimes even overcomes these results.

关键词： Hyper-heuristics multi-objective evolutionary algorithms Voting methods Crashworthiness Car side impact Machining Water resource planning multi-objective travel salesperson problem

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A hybrid promoter analysis methodology for prokaryotic genomes

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FUZZY SETS AND SYSTEMS 2005年第1期152卷 83-102页

作者： Cotik, V Zaliz, RR Zwir, I Washington Univ Sch Med Howard Hughes Med Inst Dept Mol Microbiol St Louis MO 63130 USA Univ Buenos Aires Fac Ciencias Exactas & Nat Dept Computac Buenos Aires DF Argentina Univ Granada Dept Ciencias Computac & Intelegencia Artificial ETS Ingn Informat Granada Spain

One of the big challenges of the post-genomic era is identifying regulatory systems and integrating them into genetic networks. Gene expression is determined by protein-protein interactions among regulatory proteins and with RNA polymerase(s), and protein-DNA interactions of these trans-acting factors with cis-acting DNA sequences in the promoter regions of those regulated genes. Therefore, identifying these protein-DNA interactions, by means of the DNA motifs that characterize the regulatory factors operating in the transcription of a gene, becomes crucial for determining, which genes participate in a regulation process, how they behave and how they are connected to build genetic networks. In this paper. we propose a hybrid promoter analysis methodology (HPAM) to discover complex promoter motifs that combines: the neural network efficiency and ability of representing imprecise and incomplete patterns;the flexibility and interpretability of fuzzy models;and the multi-objective evolutionary algorithms capability to identify optimal instances of a model by searching according to multiple criteria. We test our methodology by learning and predicting the RNA polymerase motif in prokaryotic genomes. This constitutes a special challenge due to the multiplicity of the RNA polymerase targets and its connectivity with other transcription factors, which sometimes require multiple functional binding sites even in close located regulatory regions;and the uncertainty of its motif, which allows sites with low specificity (i.e., differing from the best alignment or consensus) to still be functional. HPAM is available for public use in http://***. (c) 2004 Elsevier B.V All rights reserved.

关键词： gene regulation prokaryotic promoters RNA polymerase pattern recognition time delay neural networks multi-objective evolutionary algorithms fuzzy sets

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A new evolutionism based self-adaptive multi-objective optimization method to predict software cost estimation

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SOFTWARE-PRACTICE & EXPERIENCE 2022年第8期52卷 1826-1848页

作者： Gouda, Sunil Kumar Mehta, Ashok Kumar Natl Inst Technol Dept Comp Sci & Engn Jamshedpur Bihar India

One of the major challenge that organizations face in the present environment is having an efficient model for software cost estimation (SCE). In this article, the significance of the meta-heuristic algorithm in addressing various optimization challenges faced in mathematical models and software applications is discussed. The proposed method uses the new evolutionism-based self-adaptive mutation operator to solve the multi-objective optimization problems. This approach addresses the issues that exist in multi-objective differential evolution algorithms. To improve diversity among candidate solutions, the Pareto optimality principle is integrated with the evolutionism-based self-adaptive mutation operator in a multi-objective DE algorithm. To reduce the time complexity of Pareto dominance, we have adopted the non-dominated sorting algorithm. We used eight benchmark test functions to evaluate the effectiveness of the proposed method, and it outperformed the most recent multi-objective evolutionary algorithms (MOEAs). Furthermore, this article explores software engineering problems like SCE by using the proposed approach, where SCEs are accurately predicted by optimizing the tuning parameters of the multi-objective constructive cost model. The proposed algorithm achieves better cost prediction as compared to the other standard benchmark algorithms for all objective problems in terms of prediction, mean absolute error, and root mean square error.

关键词： multi-objective differential evolution multi-objective evolutionary algorithms optimization software cost estimation

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Efficient multi-objective evolutionary neural architecture search for U-Nets with diamond atrous convolution and Transformer for medical image segmentation

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APPLIED SOFT COMPUTING 2023年 148卷

作者： Ying, Weiqin Zheng, Qiaoqiao Wu, Yu Yang, Kaihao Zhou, Zekun Chen, Jiajun Ye, Zilin South China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Guangdong Peoples R China

Deep encoder-decoder neural networks like U-Nets have made significant contributions to the development of computer vision applications such as image segmentation. Neural architecture search (NAS) has the potential to further automatically adjust the architectures of U-Nets for various medical image segmentation tasks. Most of the NAS techniques focus on optimizing segmentation accuracies of network architectures. In real-world medical image segmentation scenarios, two main challenges are poor medical image quality and diverse deployment devices with different computing capabilities. A large architecture designed only for the high segmentation accuracy is difficult to run on various deployment devices. To address these challenges, this paper proposes a multi-objective evolutionary neural architecture search method (CTU-NAS) for U-Nets with diamond atrous convolution and Transformer for medical image segmentation. A hybrid U-Net architecture (CTU-Net) with diamond atrous convolution and Transformer modules is designed as the supernet of CTU-NAS. Then a channel search strategy based on sorting and selection is applied to speed up the search for subnets by precisely selecting and training the most important channels more times. In addition, CTU-NAS employs a dual acceleration mechanism based on weight sharing and surrogate model to lower the cost of evaluations of subnets. CTU-NAS applies a multi-objective evolutionary algorithm to balance between the segmentation accuracy and the number of parameters. Experimental results on two medical segmentation datasets show that CTU-NAS is capable of quickly generating a group of excellent network architectures with different sizes and their performances also outperform or come close to those of the manually designed networks.

关键词： Medical image segmentation Neural architecture search multi-objective evolutionary algorithms Diamond atrous convolution Surrogate model

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multi-objective evolutionary optimization for dimensionality reduction of texts represented by synsets

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1240页

作者： de Mendizabal, Inaki Velez Basto-Fernandes, Vitor Ezpeleta, Enaitz Mendez, Jose R. Gomez-Meire, Silvana Zurutuza, Urko Mondragon Unibertsitatea Elect & Comp Dept Gipuzkoa Spain Univ Inst Lisbon ISTAR Inst Univ Lisboa ISCTE IUL IUL Lisbon Portugal Hosp Alvaro Cunqueiro Galicia Hlth Res Inst IIS Galicia Sur Bloque tecn SING Res Grp Pontevedra Spain Biomed Res Ctr CINBIO Lagoas Marcosende Vigo Spain Dept Comp Sci Univ Vigo Orense Spain

Despite new developments in machine learning classification techniques, improving the accuracy of spam filtering is a difficult task due to linguistic phenomena that limit its effectiveness. In particular, we highlight polysemy, synonymy, the usage of hypernyms/hyponyms, and the presence of irrelevant/confusing words. These problems should be solved at the pre-processing stage to avoid using inconsistent information in the building of classification models. Previous studies have suggested that the use of synset-based representation strategies could be successfully used to solve synonymy and polysemy problems. Complementarily, it is possible to take advantage of hyponymy/hypernymy-based to implement dimensionality reduction strategies. These strategies could unify textual terms to model the intentions of the document without losing any information (e.g., bringing together the synsets "viagra", "ciallis", "levitra" and other representing similar drugs by using "virility drug" which is a hyponym for all of them). These feature reduction schemes are known as lossless strategies as the information is not removed but only generalised. However, in some types of text classification problems (such as spam filtering) it may not be worthwhile to keep all the information and let dimensionality reduction algorithms discard information that may be irrelevant or confusing. In this work, we are introducing the feature reduction as a multi-objective optimisation problem to be solved using a multi-objective evolutionary Algorithm (MOEA). Our algorithm allows, with minor modifications, to implement lossless (using only semantic-based synset grouping), low-loss (discarding irrelevant information and using semantic -based synset grouping) or lossy (discarding only irrelevant information) strategies. The contribution of this study is two-fold: (i) to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using MOE

关键词： Spam filtering Synset-based representation Semantic-based feature reduction multi-objective evolutionary algorithms

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A multi-objective evolutionary Algorithm Based on Uniformity and Diversity to Handle Regular and Irregular Pareto Front Shapes

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IEEE ACCESS 2024年 12卷 158878-158907页

作者： Marquez-Vega, Luis A. Falcon-Cardona, Jesus Guillermo Osuna, And Edgar Covantes Tecnol Monterrey Sch Engn & Sci Monterrey 64700 Nuevo Leon Mexico Heineken Mexico Data & Analyt Dept Monterrey 64410 Nuevo Leon Mexico

Achieving uniform Pareto front (PF) approximations across various PF geometries and dimensions is a significant challenge. Most multi-objective evolutionary algorithms (MOEAs) that adapt a reference set to guide the evolutionary process, do not prioritize uniformity but the degree of resemblance to the PF shape. Consequently, these MOEAs often require extensive function evaluations to balance uniformity and diversity while representing the PF effectively. To address this issue, we introduce MOEA-UD, a MOEA designed to construct uniformly distributed PF approximations independent of the PF's geometrical properties. MOEA-UD features a two-stage niching selection process. Initially, it classifies the population into niches using a reference set for uniformity, where the best solution per niche is selected. If additional solutions are needed to reach the population size, it then employs a reference set for diversity to populate any sparse regions. This approach ensures that uniformity is prioritized, while the diversity is used strategically to fill gaps and avoid empty regions. Also, an external archive using an improved selection mechanism based on niching and pair-potential energy function is employed to adapt both reference sets iteratively. We compared MOEA-UD with twelve state-of-the-art MOEAs on a wide range of artificial and real-world multi-objective optimization problems with different PF geometries. Our experimental results show that MOEA-UD consistently exhibits a PF shape invariant performance according to quality indicators, making it a promising option for generating uniform PF approximations.

关键词： Vectors Shape Optimization Linear programming evolutionary computation Superluminescent diodes Geometry Sorting Object recognition Genetic algorithms multi-objective evolutionary algorithms niching selection pair-potential energy function Pareto front shape invariant performance reference sets

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Three-objective constrained evolutionary instance selection for classification: Wrapper and filter approaches

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期107卷 104531-104531页

作者： Jimenez, Fernando Sanchez, Gracia Palma, Jose Sciavicco, Guido Univ Murcia Dept Informat & Commun Engn Murcia Spain Univ Ferrara Dept Math & Comp Sci Ferrara Italy

The large amount of data that is produced today with new technologies is an impediment for machine learning algorithms to work correctly, both due to the memory requirements and the necessary execution times. That is why the processes of reducing both the quantity and the size of the data are increasingly important. One of these processes is the so-called instance selection. In this paper we propose three-objective constrained optimization models to formulate instance selection wrapper and filter methods (separately) for classification problems, which are solved with multi-objective evolutionary algorithms and multi-objective differential evolution. In the proposed instance selection wrapper method, an objective is added to the usual ones to minimize the generalization error of the classifier. The proposed instance selection filter method simultaneously optimizes the correlation, redundancy and consistency of the datasets. Instance retention constraints are imposed on optimization models to retain a maximum percentage of samples, established by the decision maker, in big data scenarios. The experiments have been designed to compare (1) the NSGA-II and MODE algorithms, (2) two- and three-objective optimization models, (3) two different constraint handling techniques, and (4) the proposed evolutionary approaches and other 12 non-evolutionary approaches used in literature. The proposed wrapper and filter instance selection methods have been used in a real-world business engineering application, and have also been validated using three public datasets to facilitate the replicability of the research results. The results of the experiments show the superiority of the three-objective constrained evolutionary techniques proposed in this paper over the non-evolutionary techniques and over the two-objective evolutionary approaches used in the literature.

关键词： multi-objective evolutionary algorithms multi-objective constrained optimization Instance selection Classification

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Scheduling a Bi-objective Hybrid Flow Shop with Sequence-Dependent Family Setup Times Using Metaheuristics

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2013年第8期38卷 2233-2244页

作者： Fadaei, M. Zandieh, M. Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Shahid Beheshti Univ Management & Accounting Fac Dept Ind Management GC Tehran Iran

This paper deals with the problem of hybrid flow shop scheduling. In this investigation, we considered group scheduling within the area of sequence-dependent family setup times and two objectives of minimizing makespan and total tardiness are taken into consideration simultaneously. Due to the computational complexity in solving these set of problems with multiple objectives, metaheuristics has a high priority, because these algorithms are capable of solving combinatorial problems in a reasonable time. This study focuses on three multi-objective algorithms, multi-objective genetic algorithm, sub-population genetic algorithm-II and non-dominated sorting genetic algorithm-II, to solve the mentioned problem. In order to investigate the effectiveness and efficiency of applying the noted metaheuristics for such an NP-hard problem, we evaluate non-dominated solution sets obtained via each algorithm through some evaluation metrics.

关键词： multi-objective optimization Hybrid flow shop multi-objective evolutionary algorithms Sequence-dependent family setup times Group scheduling Makespan Total tardiness

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Solution methods for the bi-objective (cost-coverage) unconstrained facility location problem with an illustrative example

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ANNALS OF OPERATIONS RESEARCH 2006年第1期147卷 109-141页

作者： Villegas, Juan G. Palacios, Fernando Medaglia, Andres L. Univ Los Andes Fac Ingn Dept Ingn Ind Bogota 4976 DC Colombia Univ Antioquia Fac Ingn Dept Ingn Ind Medellin Colombia

The Colombian coffee supply network, managed by the Federacion Nacional de Cafeteros de Colombia (Colombian National Coffee-Growers Federation), requires slimming down operational costs while continuing to provide a high level of service in terms of coverage to its affiliated coffee growers. We model this problem as a biobjective (cost-coverage) uncapacitated facility location problem (BOUFLP). We designed and implemented three different algorithms for the BOUFLP that are able to obtain a good approximation of the Pareto frontier. We designed an algorithm based on the Nondominated Sorting Genetic Algorithm;an algorithm based on the Pareto Archive Evolution Strategy;and an algorithm based on mathematical programming. We developed a random problem generator for testing and comparison using as reference the Colombian coffee supply network with 29 depots and 47 purchasing centers. We compared the algorithms based on the quality of the approximation to the Pareto frontier using a nondominated space metric inspired on Zitzler and Thiele's. We used the mathematical programming-based algorithm to identify unique tradeoff opportunities for the reconfiguration of the Colombian coffee supply network. Finally, we illustrate an extension of the mathematical programming-based algorithm to perform scenario analysis for a set of uncapacitated location problems found in the literature.

关键词： uncapacitated facility location multi-objective optimization multi-objective evolutionary algorithms combinatorial optimization integer programming scenario analysis

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multi-objective evolutionary Neural Architecture Search with Weight-Sharing Supernet

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APPLIED SCIENCES-BASEL 2024年第14期14卷 6143页

作者： Liang, Junchao Zhu, Ke Li, Yuan Li, Yun Gong, Yuejiao South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Henan Normal Univ Sch Comp & Informat Engn Xinxiang 453007 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

Deep neural networks have played a crucial role in the field of deep learning, achieving significant success in practical applications. The architecture of neural networks is key to their performance. In the past few years, these architectures have been manually designed by experts with rich domain knowledge. Additionally, the optimal neural network architecture can vary depending on specific tasks and data distributions. Neural Architecture Search (NAS) is a class of techniques aimed at automatically searching for and designing neural network architectures according to the given tasks and data. Specifically, evolutionary-computation-based NAS methods are known for their strong global search capability and have aroused widespread interest in recent years. Although evolutionary-computation-based NAS has achieved success in a wide range of research and applications, it still faces bottlenecks in training and evaluating a large number of individuals during optimization. In this study, we first devise a multi-objective evolutionary NAS framework based on a weight-sharing supernet to improve the search efficiency of traditional evolutionary-computation-based NAS. This framework combines the population optimization characteristic of evolutionary algorithms with the weight-sharing ideas in one-shot models. We then design a bi-population MOEA/D algorithm based on the proposed framework to effectively solve the NAS problem. By constructing two sub-populations with different optimization objectives, the algorithm can effectively explore network architectures of various sizes in complex search spaces. An inter-population communication mechanism further enhances the algorithm's exploratory capability, enabling it to find network architectures with uniform distribution and high diversity. Finally, we conduct performance comparison experiments on image classification datasets of different scales and complexities. Experimental results demonstrate the effectiveness of the proposed

关键词： deep learning neural architecture search multi-objective evolutionary algorithms image classification

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