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Analyzing the Performance of a Two-Tail-Measures-Utility multi-objective Portfolio Optimization Model

Operations Research Forum 2021年第4期2卷 58页

作者： Mamanis, Georgios CGSoft Athens Greece

This research paper proposes and experimentally investigates the out-of-sample performance of a multi(three)-objective portfolio optimization model. The three objectives used to evaluate the return distribution of the portfolio are two tail performance measures and a utility function in order to evaluate the middle part of the return distribution. Five different utility functions are considered, thus forming five instances of the proposed multi-objective portfolio selection model. For solving the problem, a multi-objective evolutionary algorithm, namely Strength Pareto evolutionary Algorithm 2 (SPEA2), was employed. The results show that the majority of the portfolios generated by the solution technique produce better portfolios than S&P 500 Index considering the final wealth, Sharpe ratio, and Sortino ratio. The five portfolio models defined by different utility functions return, on average, approximately 10% more than the return on S&P 500 Index for an evaluation period of one and a half year. Furthermore, the computational results show that the proposed multi-objective portfolio optimization models are competitive to portfolios that have shown good out-of-sample performance in past studies, like the minimum variance portfolio with and without short sales and the second-order stochastic dominance portfolio. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： multi-objective evolutionary algorithms multi-objective optimization Portfolio optimization Portfolio selection Utility functions

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Energy and Spectral Efficiency Tradeoff in NOMA: multi-objective evolutionary Approaches

Energy and Spectral Efficiency Tradeoff in NOMA: Multi-Objec...

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IEEE International Conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Cetinkaya, Sinasi Arslan, Huseyin Univ S Florida Dept Elect Engn Tampa FL 33620 USA Istanbul Medipol Univ Dept Elect & Elect Engn TR-34810 Istanbul Turkey

ISBN: (纸本)9781728174402

Non-orthogonal multiple access (NOMA) deployment in future wireless networks has been recently considered a promising radio access technology to enhance spectral efficiency (SE). However, gain in SE comes always with the cost of energy efficiency (EE). In this paper, we investigate the SE and EE tradeoff in downlink NOMA with the consideration of quality of service (QoS) requirements based on three population-based multi-objective evolutionary algorithms (MOEAs): multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm-II (NSGA-II) and strength Pareto evolutionary algorithm-2 (SPEA2). The tradeoff is optimized and Pareto optimal solutions are obtained through MOEAs. The effectiveness of the algorithms is evaluated based on the hypervolume metric and the capability of solving multi-objective optimization problems. Simulation results reveal that SPEA2 outperforms NSGA-II and MOPSO. Furthermore, NSGA-II is the loser among all algorithms in terms of finding Pareto optimal results.

关键词： Energy efficiency multi-objective evolutionary algorithms non-orthogonal multiple access (NOMA) power allocation spectral efficiency

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Hybrid NSGA III/dual simplex approach to generation and transmission maintenance scheduling

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 135卷 107498-107498页

作者： San Martin, Luis Salinas Yang, Jin Liu, Ying Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Lanark Scotland

The generation and transmission maintenance scheduling (GTMS) problem presents generation (GENCOs) and transmission (TRANSCO) companies scheduling their facilities for maintenance to maximize their profits, while the independent system operator (ISO) pushes for maintenance schedules (MS) that guarantees system reliability and minimizes operation cost. Inherently, GTMS is a high-dimensional, non-linear, non-convex, multi-objective optimization problem that contains conflicting objectives related to different participants in the market. This paper develops a hybrid model to tackle the GTMS problem in a deregulated market environment by combining in a novel way the non-dominated sorting genetic algorithm III (NSGA III) and the Dual-Simplex (DS) techniques. The model manages to minimize the total system operational cost and keep high system adequacy, both aspects of interest for the independent system operator (ISO), while increasing the profits of GENCOs. The approach used matches accepted industry maintenance practices with cutting-edge optimization techniques developed in academia. The model, tested in the IEEE-RTS 24 bus test network, delivers a set of feasible MS solutions that address the conflicting relationships between the GENCOs and the ISO in the market, displays a degree of coordination among generation and transmission MS and their impact on electricity prices. Finally, it allows the ISO to use this set to identify the best using the technique for ordering preferences according to the similarity to an ideal solution (TOPSIS) decision-making tool.

关键词： Generation and transmission maintenance scheduling Deregulated electricity market multi-objective optimization multi-objective evolutionary algorithms Non-dominated Sorting Genetic Algorithm Dual-Simplex optimization technique

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EEEA-Net: An Early Exit evolutionary Neural Architecture Search

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 104卷 104397-104397页

作者： Termritthikun, Chakkrit Jamtsho, Yeshi Ieamsaard, Jirarat Muneesawang, Paisarn Lee, Ivan Univ South Australia STEM Adelaide SA 5095 Australia Naresuan Univ Fac Engn Dept Elect & Comp Engn Phitsanulok 65000 Thailand Royal Univ Bhutan Coll Sci & Technol Phuentsholing 21101 Bhutan

The goals of this research were to search for Convolutional Neural Network (CNN) architectures, suitable for an on-device processor with limited computing resources, performing at substantially lower Network Architecture Search (NAS) costs. A new algorithm entitled an Early Exit Population Initialisation (EE-PI) for evolutionary Algorithm (EA) was developed to achieve both goals. The EE-PI reduces the total number of parameters in the search process by filtering the models with fewer parameters than the maximum threshold. It will look for a new model to replace those models with parameters more than the threshold. Thereby, reducing the number of parameters, memory usage for model storage and processing time while maintaining the same performance or accuracy. The search time was reduced to 0.52 GPU day. This is a huge and significant achievement compared to the NAS of 4 GPU days achieved using NSGA-Net, 3,150 GPU days by the AmoebaNet model, and the 2,000 GPU days by the NASNet model. As well, Early Exit evolutionary Algorithm networks (EEEA-Nets) yield network architectures with minimal error and computational cost suitable for a given dataset as a class of network algorithms. Using EEEA-Net on CIFAR-10, CIFAR-100, and ImageNet datasets, our experiments showed that EEEA-Net achieved the lowest error rate among state-of-the-art NAS models, with 2.46% for CIFAR-10, 15.02% for CIFAR-100, and 23.8% for ImageNet dataset. Further, we implemented this image recognition architecture for other tasks, such as object detection, semantic segmentation, and keypoint detection tasks, and, in our experiments, EEEA-Net-C2 outperformed MobileNet-V3 on all of these various tasks. (The algorithm code is available at https://***/chakkritte/EEEA-Net).

关键词： Deep learning Neural Architecture Search multi-objective evolutionary algorithms Image classification

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Spatial and sectoral benefit distribution in water-energy system design

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APPLIED ENERGY 2020年 269卷 114794-114794页

作者： Gonzalez, Jose M. Tomlinson, James E. Harou, Julien J. Cesena, Eduardo A. Martinez Panteli, Mathaios Bottacin-Busolin, Andrea Hurford, Anthony Olivares, Marcelo A. Siddiqui, Afzal Erfani, Tohid Strzepek, Kenneth M. Mancarella, Pierluigi Mutale, Joseph Obuobie, Emmanuel Seid, Abdulkarim H. Ya, Aung Ze Univ Manchester Dept Mech Aerosp & Civil Engn Manchester M13 9PL Lancs England Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL Lancs England Univ Chile Dept Civil Engn Santiago 2002 Chile UCL Dept Stat Sci London WC1E 6BT England Stockholm Univ Dept Comp & Syst Sci Stockholm Sweden UCL Dept Civil Environm & Geomat Engn London WC1E 6BT England MIT Joint Program Sci & Policy Global Change 77 Massachusetts AveE19-429F Cambridge MA 02139 USA Univ Melbourne Sch Elect & Elect Engn Melbourne Vic Australia CSIR Water Res Inst POB AH 38 Achimota Accra Ghana Nile Basin Initiat Secretariat Water Resources Management Entebbe Uganda Yangon Technol Univ Dept Elect Power Engn Insein Township Yangon Myanmar

The design of water and energy systems has traditionally been done independently or considering simplified interdependencies between the two systems. This potentially misses valuable synergies between them and does not consider in detail the distribution of benefits between different sectors or regions. This paper presents a framework to couple integrated water-power network simulators with multi-objective optimisation under uncertainty to explore the implications of explicitly including spatial topology and interdependencies in the design of multi-sector integrated systems. A synthetic case study that incorporates sectoral dependencies in resource allocation, operation of multi-purpose reservoirs and spatially distributed infrastructure selection in both systems is used. The importance of explicitly modelling the distribution of benefits across different sectors and regions is explored by comparing different spatially aggregated and disaggregated multi-objective optimisation formulations. The results show the disaggregated formulation identifies a diverse set of non-dominated portfolios that enables addressing the spatial and sectoral distribution of benefits, whilst the aggregated formulations arbitrarily induce unintended biases. The proposed disaggregated approach allows for detailed spatial design of interlinked water and energy systems considering their complex regional and sectoral trade-offs. The framework is intended to assist planners in real resource systems where diverse stakeholder groups are mindful of receiving their fair share of development benefits.

关键词： Water-energy system design multi-sector benefit distribution multi-objective robust optimisation under uncertainty multi-objective evolutionary algorithms

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A Many-objective Analysis Framework for Large Real-World Water Distribution System Design Problems

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WATER 2022年第4期14卷 557-557页

作者： Bi, Weiwei Chen, Minjie Shen, Shuwen Huang, Zhiyuan Chen, Jie Zhejiang Univ Technol Coll Civil Engn Hangzhou 310023 Peoples R China

This paper presents a many-objective analysis framework to handle large real-world water distribution system design problems (WDSDP), which is a typically difficult infrastructure engineering optimization problem type. Six objectives are formulated, focusing on economic, structural and functional aspects in the operation and management of the water distribution system (WDS), and solved by Borg, which is one state-of-the-art multi-objective evolutionary algorithm (MOEA) in water resources. The framework comprehensively analyzes and reveals the underlying trade-offs among many objectives, thereby facilitating the selection of the most appropriate design solutions for real-world WDSs. A real-world WDSDP with 1278 decision variables is used to demonstrate the effectiveness of the proposed framework, and results show that it can clearly reveal the complex trade-offs among these six different objectives, and it greatly enhances the understanding of the underlying characteristics of Pareto-front solutions. The insights have great practical implications for optimally designing large real-world WDS problems.

关键词： many-objective optimization water distribution system multi-objective evolutionary algorithms MOEAs trade-offs

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A Classification Surrogate Model based evolutionary Algorithm for Neural Network Structure Learning

A Classification Surrogate Model based Evolutionary Algorith...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Hu, Wenyue Zhou, Aimin Zhang, Guixu East China Normal Univ Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China

ISBN: (纸本)9781728169262

Designing neural networks often requires a large number of artificial intelligence experts. However, such manual processes are time-consuming and require numerous resources. In this paper, we try to search neural network structures automatically for the image classification task. Moreover, considering the huge computational cost of neural architecture search (NAS), we attempt to apply a classification surrogate model based multi-objective evolutionary algorithm to search neural network architectures (CSMEA-Net). The algorithm combines two objectives, i.e., minimizing the validation error and the computational complexity measured by the number of floating-point operations (FLOPs) to achieve Pareto Optimality. In addition, we improve the components of the cell-based search space. The performance of network architectures discovered by our method is evaluated on CIFAR-10 and CIFAR-100 datasets. The experimental results show that the proposed approach can find a higher-performance neural network architecture compared with both hand-crafted as well as automatically-designed networks.

关键词： convolutional neural network neural architecture search multi-objective evolutionary algorithms image classification

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Two-stage storage assignment to minimize travel time and congestion for warehouse order picking operations

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 139卷 106129-000页

作者： Lee, In Gyu Chung, Sung Hoon Yoon, Sang Won SUNY Binghamton Dept Syst Sci & Ind Engn Binghamton NY 13902 USA

This research presents a systematic and integrated approach that extends the correlated storage assignment strategy to improve the efficiency of warehouse order picking operations. The correlated storage assignment can reduce a significant amount of travel costs, but could lead to traffic congestion due to the imbalanced traffic flow. Hence, this research proposes the correlated and traffic balanced storage assignment (C&TBSA) to minimize the travel time and picking delays, which is modeled in two stages: clustering and assignment. In the clustering stage, a bi-objective optimization model is formulated to group items with the consideration of both travel efficiency and traffic flow balance, which is solved using multi-objective evolutionary algorithms (MOEAs). In the assignment stage, items in each cluster are distributed to the available storage locations. C& TBSA is evaluated with an actual warehouse case study and the results show that C&TBSA outperforms random, class-based, and correlated storage assignment methods by 48.74%, 23.82%, and 7.58% respectively, regarding the total time consisting of travel time and picking delays.

关键词： Order picking operation Correlated and traffic balanced storage assignment multi-objective evolutionary algorithms Warehouse operations Supply chain management

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SDRS: A new lossless dimensionality reduction for text corpora

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INFORMATION PROCESSING & MANAGEMENT 2020年第4期57卷 102249-102249页

作者： Velez de Mendizabal, Inaki Basto-Fernandes, Vitor Ezpeleta, Enaitz Mendez, Jose R. Zurutuza, Urko Mondragon Unibersitatea Elect & Comp Dept Arrasate Mondragon Spain Univ Inst Lisbon Inst Univ Lisboa ISCTE IUL ISTAR IUL Av Forcas Armadas Lisbon 1649026 Portugal Univ Vigo Dept Comp Sci Edificio Politecn ESEI Escuela Super Ingn Informat Campus Univ As Lagoas S-N Orense 32004 Spain Univ Vigo CINBIO Biomed Res Ctr Campus Univ Lagoas Marcosende Vigo 36310 Spain SERGAS UVIGO Galicia Sur Hlth Res Inst IIS Galicia Sur SING Res Grp Hosp Alvaro Cunqueiro Bloque TecnEstr Clara Vigo 36312 Pontevedra Spain

In recent years, most content-based spam filters have been implemented using Machine Learning (ML) approaches by means of token-based representations of textual contents. After introducing multiple performance enhancements, the impact has been virtually irrelevant. Recent studies have introduced synset-based content representations as a reliable way to improve classification, as well as different forms to take advantage of semantic information to address problems, such as dimensionality reduction. These preliminary solutions present some limitations and enforce simplifications that must be gradually redefined in order to obtain significant improvements in spam content filtering. This study addresses the problem of feature reduction by introducing a new semantic-based proposal (SDRS) that avoids losing knowledge (lossless). Synset-features can be semantically grouped by taking advantage of taxonomic relations (mainly hypernyms) provided by BabelNet ontological dictionary (e.g. "Viagra" and "Cialis" can be summarized into the single features "anti-impotence drug", "drug" or "chemical substance" depending on the generalization of 1, 2 or 3 levels). In order to decide how many levels should be used to generalize each synset of a dataset, our proposal takes advantage of multi-objective evolutionary algorithms (MOEA) and particularly, of the Non-dominated Sorting Genetic Algorithm (NSGA-II). We have compared the performance achieved by a Naive Bayes classifier, using both token-based and synset-based dataset representations, with and without executing dimensional reductions. As a result, our lossless semantic reduction strategy was able to find optimal semantic-based feature grouping strategies for the input texts, leading to a better performance of Naive Bayes classifiers.

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

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New hybrid between SPEA/R with deep neural network: Application to predicting the multi-objective optimization of the stiffness parameter for powertrain mount systems

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2020年第4期39卷 850-865页

作者： Dinh-Nam Dao Guo, Li-Xin Northeastern Univ Sch Mech Engn & Automat Shenyang Liaoning Peoples R China Le Quy Don Tech Univ Control Technol Coll Hanoi Vietnam

In this study, a new methodology, hybrid Strength Pareto evolutionary Algorithm Reference Direction (SPEA/R) with Deep Neural Network (HDNN&SPEA/R), has been developed to achieve cost optimization of stiffness parameter for powertrain mount systems. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration of a rear engine mount, mean square displacement of a rear engine mount, mean square acceleration of a front left engine mount, mean square displacement of a front left engine mount, mean square acceleration of a front right engine mount, and mean square displacement of a front right engine mount. A hybrid HDNN&SPEA/R is proposed with the integration of genetic algorithm, deep neural network, and a Strength Pareto evolutionary algorithm based on reference direction for multi-objective SPEA/R. Several benchmark functions are tested, and results reveal that the HDNN&SPEA/R is more efficient than the typical deep neural network. stiffness parameter for powertrain mount systems optimization with HDNN&SPEA/R is simulated, respectively. It proved the potential of the HDNN&SPEA/R for stiffness parameter for powertrain mount systems optimization problem.

关键词： SPEA R algorithm feed-forward artificial neural network extreme learning machine multi-objective evolutionary algorithms powertrain mount system stiffness mounting system

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