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A multi-objective genetic Algorithm for detecting dynamic communities using a local search driven immigrant's scheme

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 110卷 960-975页

作者： Panizo-LLedot, Angel Bello-Orgaz, Gema Camacho, David Univ Rey Juan Carlos Comp Sci Dept Madrid Spain Univ Politecn Madrid Dept Sistemas Informat Madrid Spain

The interest in Community Detection Problems on networks that evolves over time has experienced an increasing attention over the last years. multi-objective genetic algorithms and other bio-inspired methods have been successfully applied to tackle the community finding problem in static networks. Although, there are a large number of evolutionary and bio-inspired approaches that combine Local Search Strategies and other techniques from graph theory to handle the community detection problems in static networks, few research has been done related to the application of these algorithms over temporal, or dynamic, networks. This work is focused on the design, implementation, and the empirical analysis of a new multi-objective genetic Algorithm that combines an Immigrant's scheme with local search strategies for dynamic community detection. The main contribution of this new algorithm is to address the adaptation of these strategies to dynamic networks. On the one hand, the Immigrant's scheme motif is to reuse previously acquired information to reduce computational time. On the other hand, in a dynamic environment is possible that a valid solution became invalid due to some changes in the environment, for example, if some nodes or edges have been removed or added to the network. Therefore, the aim of the local search operator used in the new algorithm is to transform an invalid solution, due to a change happened on the network, into a valid one maintaining the highest possible quality. Finally, the proposed algorithm has been tested using several synthetic and real-world networks, and compared against several algorithms (DYNMOGA, ALPA, Infomap) from the state of the art. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Dynamic community finding multi-objective genetic algorithms Graph computing Network analysis

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A novel approach to incorporate investor's preference in fuzzy multi-objective portfolio selection problem using credibility measure

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 212卷

作者： Jalota, Hemant Mandal, Pawan Kumar Thakur, Manoj Mittal, Garima Indian Inst Technol Mandi Mandi 175075 Himachal Prades India Indian Inst Management Lucknow Lucknow 226013 Uttar Pradesh India Indian Inst Technol Mandi A13-F4North Campus Mandi 175075 Himachal Prades India

Investment decision making is usually a multi-objective optimization problem in an uncertain environment. In a real-life scenario, an investor aims to choose a portfolio based on his/her preferences for each objective. Credibility theory has been attributed as one of the most effective ways to model uncertain portfolio attributes. LR fuzzy numbers based credibilistic moments have been used successfully in the recent past to model portfolio selection problems in uncertain environments. Traditionally, the preferences on portfolio attributes are reflected as user defined threshold values. This work proposes a novel way to integrate the investor's preferences in the portfolio selection model. Investor's preferences are specified as LR fuzzy numbers corresponding to the portfolio's expected return and illiquidity distributions. The portfolio models formulated in this study attempt to attain them to the best possible extent. Four new models are presented considering uncertain portfolio attributes, namely, fuzzy return and fuzzy illiquidity. The models are solved using the MIBEX-SM genetic Algorithm (GA) and ECE methodology. In order to demonstrate the efficient working of the proposed models, practical applications of portfolio selection problem using historical time series data of National Stock Exchange (NSE) is presented. Credibilistic Sharpe Ratio (CSR) has been used to compare the efficiency of models among themselves as well as with the Nifty-50 index.

关键词： Portfolio optimization Investor preference Credibility measure LR fuzzy numbers multi-objective genetic algorithms

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Optimal Design and Performance Analysis of Thermoelectric Power Generation Device Based on multi-objective genetic Algorithm

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ADVANCED THEORY AND SIMULATIONS 2021年第6期4卷

作者： Wu, Jinmeng Chen, Yan Dou, Yinke Ma, Chunyan Du, Qian Liu, Qiang Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan 030024 Peoples R China

The combined use of flue gas waste heat resources and thermoelectric generators (TEGs) is considered to be a relatively reliable method to generate electricity. The focus of this study is on the optimization and improvement of the hot-end heat collection pipe. This paper aims to increase the temperature difference between hot and cold ends of TEG and enhance uniformity of temperature distribution, thereby improving the output power of the TEG system. To balance the temperature difference between the cold and hot ends of the TEG, the pressure drop from the inlet to the outlet and the mass of TEG hot end pipes, a finite element simulation model is constructed. Meanwhile, a multi-objective genetic algorithm is applied to optimize the structure of four dimensions: fin bottom length, fin height, fin thickness, and outlet diameter. Three optimization objectives, namely, average temperature difference, total pressure drop from inlet to outlet, and pipeline mass are globally optimized to determine the best size, based on which the accuracy of the simulation model is verified by conducting experiments.

关键词： multi-objective genetic algorithms power generation characteristics thermoelectric generators waste heat

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Automated Design and Optimization of Metallic Alloys

Automated Design and Optimization of Metallic Alloys

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作者： Hohl, Jacob University of Nevada Reno

学位级别：M.S., Master of Science/Master of Surgery

The design and optimization of metallic alloys poses a significant engineering challenge. The search space of all possible alloys is sufficiently large that it is impossible to fully explore by traditional methods. In order to address this challenge, physics based computational frameworks linked to advanced machine learning algorithms can serve to automate this process with computational efficiency such that the state of the industry may be rapidly advanced. The work herein presents a suite of computational frameworks leveraged to automate the design and optimization process of advanced alloys. An ab initio alloy thermodynamics system, Molecular Dynamics simulations, a Convolutional-Neural Network system, and a coupled Neural Network and multi-objective genetic Algorithm. These algorithms are validated over the set of binary nanocrystalline Al-X alloys, and multi-component High Entropy Alloys (HEA).

关键词： Metallic alloys Design optimization Neural networks multi-objective genetic algorithms High entropy alloys

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Design of Ensemble Forecasting Models for Home Energy Management Systems

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ENERGIES 2021年第22期14卷 7664页

作者： Bot, Karol Santos, Samira Laouali, Inoussa Ruano, Antonio Ruano, Maria da Graca Univ Algarve Fac Sci & Technol P-8005294 Faro Portugal Sidi Mohamed Ben Abdellah Univ Fac Sci & Technol SIGER Fes 1049001 Morocco Univ Lisbon Inst Super Tecn IDMEC P-1950044 Lisbon Portugal Univ Coimbra CISUC P-3030290 Coimbra Portugal

The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption;as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.

关键词： energy systems machine learning forecasting energy management systems multi-objective genetic algorithms ensemble models energy in buildings

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multi-objective parameter-less population pyramid for solving industrial process planning problems

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 60卷

作者： Przewozniczek, Michal Witold Dziurzanski, Piotr Zhao, Shuai Indrusiak, Leandro Soares Univ York Dept Comp Sci York N Yorkshire England

Evolutionary methods are effective tools for obtaining high-quality results when solving hard practical problems. Linkage learning may increase their effectiveness. One of the state-of-the-art methods that employ linkage learning is the Parameter-less Population Pyramid (P3). P3 is dedicated to solving single-objective problems in discrete domains. Recent research shows that P3 is highly competitive when addressing problems with so-called overlapping blocks, which are typical for practical problems. In this paper, we consider a multi-objective industrial process planning problem that arises from practice and is NP-hard. To handle it, we propose a multi-objective version of P3. The extensive research shows that our proposition outperforms the competing methods for the considered practical problem and typical multi-objective benchmarks.

关键词： multi-objective genetic algorithms Linkage learning Parameter-less population pyramid Process manufacturing optimisation

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An online variable-fidelity optimization approach for multi-objective design optimization

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STRUCTURAL AND multiDISCIPLINARY OPTIMIZATION 2019年第3期60卷 1059-1077页

作者： Shu, Leshi Jiang, Ping Zhou, Qi Xie, Tingli Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Hubei Peoples R China

multi-objective genetic algorithms (MOGAs) are effective ways for obtaining Pareto solutions of multi-objective design optimization problems. However, the high computational cost of MOGAs limits their applications to practical engineering optimization problems involving computational expensive simulations. To address this issue, a novel variable-fidelity (VF) optimization approach for multi-objective design optimization is proposed, in which a VF metamodel is embedded in the computation process of MOGA to replace the expensive simulation model. The VF metamodel is updated in the optimization process of MOGA, considering the cost of simulation models with different fidelity and the influence of the VF metamodel uncertainty. A normalized distance constraint is introduced to avoid selecting clustered sample points. Four numerical examples and two engineering cases are used to demonstrate the applicability and efficiency of the proposed approach. The results show that the proposed approach can obtain Pareto solutions with good quality and outperforms the other four approaches considered here as references in terms of computational efficiency.

关键词： multi-objective optimization Variable-fidelity optimization multi-objective genetic algorithms Metamodel uncertainty

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Design of a post-disaster shelter through soft computing

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INTERNATIONAL JOURNAL OF ARCHITECTURAL COMPUTING 2019年第2期17卷 185-205页

作者： Karaoglan, Fuesun Cemre Alacam, Sema Istanbul Tech Univ Fac Architecture Istanbul Turkey

Temporary shelters become a more critical subject of architectural design as the increasing number of natural disasters taking place each year result in a larger number of people in need of urgent sheltering. Therefore, this project focuses on designing a temporary living space that can respond to the needs of different post-disaster scenarios and form a modular system through differentiation of units. When designing temporary shelters, it is a necessity to deal with the provision of materials, low-cost production and the time limit in the emergency as well as the needs of the users and the experiential quality of the space. Although computational approaches might lead to much more efficient and resilient design solutions, they have been utilized in very few examples. For that reason and due to their suitability to work with architectural design problems, soft computing methods shape the core of the methodology of the study. Initially, a digital model is generated through a set of rules that define a growth algorithm. Then, multi-objective genetic algorithms alter this growth algorithm while evaluating different configurations through the objective functions constructed within a Fuzzy Neural Tree. The struggle to represent design goals in the form of Fuzzy Neural Tree holds potential for the further use of it for architectural design problems centred on resilience. Resilience in this context is defined as a measure of how agile a design is when dealing with a major sheltering need in a post-disaster environment. Different from the previous studies, this article aims to focus on the design of a temporary shelter that can respond to different user types and disaster scenarios through mass customization, using Fuzzy Neural Tree as a novel approach. While serving as a temporary space, the design outcomes are expected to create a more neighbourhood-like pattern with a stronger sense of community for the users compared to the previous examples.

关键词： Humanitarian design emergency architecture computational design Fuzzy Neural Tree multi-objective genetic algorithms

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A new bi-objective fuzzy portfolio selection model and its solution through evolutionary algorithms

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SOFT COMPUTING 2019年第12期23卷 4367-4381页

作者： Kar, Mohuya B. Kar, Samarjit Guo, Sini Li, Xiang Majumder, Saibal Heritage Inst Technol Dept Comp Sci & Engn Kolkata 700107 India Natl Inst Technol Dept Math Durgapur 713209 India Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China Natl Inst Technol Dept Comp Sci & Engn Durgapur 713209 India

In this paper, a new bi-objective fuzzy portfolio selection model is proposed, for which Sharp ratio (SR) and Value at Risk ratio (VR) of a portfolio are chosen as objectives. SR is an important nonsystematic risk measurement that examines the investment risk by aspiring the diversification of the capital allocation. On the other hand, VR measures the systematic risk, which reduces the largest loss of an investment at a given confidence level. The proposed fuzzy portfolio model assumes both SR and VR as maximization objectives for which the associated fuzzy parameters are considered as triangular fuzzy numbers. The proposed model is solved using multi-objective genetic algorithms, namely multi-objective cellular genetic algorithm (MOCell), archive-based hybrid scatter search (AbYSS), and nondominated sorting genetic algorithm II (NSGA-II). We have used a data set from the Shenzhen Stock Exchange to illustrate the performance of the proposed model and algorithms. Finally, a comparative study in terms of five standard performance metrics is presented, among the MOCell, AbYSS, and NSGA-II algorithms that are mentioned extensively in various research articles to exhibit the best suitable algorithm.

关键词： Fuzzy portfolio selection Sharp ratio Value at Risk ratio Credibility measure multi-objective genetic algorithms Pareto solutions

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Solving the multi-objective Flexible Job-Shop Scheduling Problem with Alternative Recipes for a Chemical Production Process 1

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22nd International Conference on Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Dziurzanski, Piotr Zhao, Shuai Swan, Jerry Indrusiak, Leandro Soares Scholze, Sebastian Krone, Karl Univ York Dept Comp Sci Deramore Lane York YO10 5GH N Yorkshire England Inst Angew Systemtech Bremen GmbH Wiener Str 1 D-28359 Bremen Germany OAS AG Caroline Herschel Str 1 D-28359 Bremen Germany

ISBN: (数字)9783030166922

ISBN: (纸本)9783030166915;9783030166922

This paper considers a new variant of a multi-objective flexible job-shop scheduling problem, featuring multisubset selection of manufactured recipes. We propose a novel associated chromosome encoding and customise the classic MOEA/D multi-objective genetic algorithm with new genetic operators. The applicability of the proposed approach is evaluated experimentally and showed to outperform typical multi-objective genetic algorithms. The problem variant is motivated by real-world manufacturing in a chemical plant and is applicable to other plants that manufacture goods using alternative recipes.

关键词： multi-objective job-shop scheduling Process manufacturing optimisation multi-objective genetic algorithms

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