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Pittsburgh-style learning classifier system for multiple environments: towards robust waterbus route for several situations

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2011年第6期3卷 370-383页

作者： Sato, Keiji Takadama, Keiki Univ Electrocommun Dept Informat Chofu Tokyo 1828585 Japan Japan Sci & Technol Agcy JST PRESTO Kawaguchi Saitama 3320012 Japan

This paper proposes an accuracy-based Pittsburgh-style learning classifier system (LCS) that can find effective and robust solutions against several different situations, and aims at investigating its effectiveness in the waterbus route optimisation problem. For this purpose, our accuracy-based Pittsburgh-style LCS: 1 introduces a new fitness calculation to remain robust classifiers (i.e., solutions) in different situations 2 employs NSGA-II to find the most effective and robust solutions among a lot of Pareto front solutions found in the multi-objective optimisation. Through intensive simulations on the waterbus route optimisation problem, we have revealed that our proposed LCS can find the waterbus routes that can cope with two different situations. In detail: 1 the relative fitness calculation can find the robust routes in comparison with the ordinary fitness calculation 2 the accuracy-based selection of the parents succeeds to find more effective and robust route in the different environments in comparison with the NSGA-II-based selection in the multi-objective optimisation.

关键词： learning classifier system LCS accuracy multi-objective optimisation non-dominated sorting genetic algorithm NSGA-II waterbus route optimisation

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THERMAL-ECONOMIC MULTI-OBJECTIVE OPTIMIZATION OF HEAT PIPE HEAT EXCHANGER FOR ENERGY RECOVERY IN HVAC APPLICATION USING genetic algorithm

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THERMAL SCIENCE 2014年第Sup2期18卷 S375-S391页

作者： Sanaye, Sepehr Modarrespoor, Davood Iran Univ Sci & Technol Dept Mech Engn Energy Syst Improvement Lab Tehran Iran

Cost and effectiveness are two important factors of heat pipe heat exchanger design. The total cost includes the investment cost for buying equipment (heat exchanger surface area) and operating cost for energy expenditures (related to fan power). The heat pipe heat exchanger was thermally modeled using epsilon-NTU method to estimate the overall heat transfer coefficient for the bank of finned tubes as well as estimating pressure drop. Fast and elitist non-dominated sorting genetic algorithm II with continuous and discrete variables was applied to obtain the maximum effectiveness and the minimum total cost as two objective *** diameter, pipe length, numbers of pipes per row, number of rows, fin pitch and fin lengthratio were considered as six design parameters. The results of optimal designs were a set of multiple optimum solutions, called "Pareto optimal solutions". The comparison of the optimum values of total cost and effectiveness, variation of optimum values of design parameters as well as estimating the payback period were also reported for various inlet fresh air volume flow rates.

关键词： heat pipe heat exchanger heat recovery effectiveness total cost multi-objective optimization non-dominated sorting genetic algorithm

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Technological analysis and fuel consumption saving potential of different gas turbine thermodynamic configurations for series hybrid electric vehicles

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2020年第6期234卷 1544-1562页

作者： Nader, Wissam Bou Cheng, Yuan Nault, Emmanuel Reine, Alexandre Wakim, Samer Kabalan, Bilal Nemer, Maroun Ctr Tech Velizy PSA Grp Route Gizy F-78943 Velizy Villacoublay Velizy France ENSMP Ctr Efficacite Energet Syst CES Palaiseau France Univ Paris Saclay ENS Paris Saclay Paris France Univ Paris Saclay IFP Sch Paris France ENSPM Rueil Malmaison France IFSTTAR Bron France

Gas turbine systems are among potential energy converters to substitute the internal combustion engine as auxiliary power unit in future series hybrid electric vehicle powertrains. Fuel consumption of these auxiliary power units in the series hybrid electric vehicle strongly relies on the energy converter efficiency and power-to-weight ratio as well as on the energy management strategy deployed on-board. This paper presents a technological analysis and investigates the potential of fuel consumption savings of a series hybrid electric vehicle using different gas turbine-system thermodynamic configurations. These include a simple gas turbine, a regenerative gas turbine, an intercooler regenerative gas turbine, and an intercooler regenerative reheat gas turbine. An energetic and technological analysis is conducted to identify the systems' efficiency and power-to-weight ratio for different operating temperatures. A series hybrid electric vehicle model is developed and the different gas turbine-system configurations are integrated as auxiliary power units. A bi-level optimization method is proposed to optimize the powertrain. It consists of coupling the non-dominated sorting genetic algorithm to the dynamic programming to minimize the fuel consumption and the number of switching ON/OFF of the auxiliary power unit, which impacts its durability. Fuel consumption simulations are performed on the worldwide-harmonized light vehicles test cycle while considering the electric and thermal comfort vehicle energetic needs. Results show that the intercooler regenerative reheat gas turbine-auxiliary power unit presents an improved fuel consumption compared with the other investigated gas turbine systems and a good potential for implementation in series hybrid electric vehicles.

关键词： Gas turbine systems series hybrid electric vehicle technological analysis energy management strategy non-dominated sorting genetic algorithm dynamic programming

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A dynamic dispatching problem to allocate relief vehicles after a disaster

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ENGINEERING OPTIMIZATION 2021年第11期53卷 1999-2016页

作者： Gharib, Mehrdad Fatemi Ghomi, Seyyed Mohammad Taghi Jolai, Fariborz Amirkabir Univ Technol Dept Ind Engn Tehran Iran Univ Tehran Coll Engn Dept Ind Engn Tehran Iran

This article proposes a deterministic model for a post-disaster scenario in an urban emergency medical services system to allocate the emergency vehicles to the patients and transfer them to the hospital. To solve the model, an exact approach called the -constraint method is applied to the problem. Since this problem belongs to the class of NP-hard problems, two metaheuristic algorithms, namely the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective imperialist competitive algorithm (MOICA), are applied to large-scale problems. The performance of the algorithms is evaluated using computational experiments. Finally, the model is applied in a real-life case study for an expected earthquake in Iran and several managerial insights are extracted.

关键词： Disaster emergency medical services epsilon-constraint method non-dominated sorting genetic algorithm multi-objective imperialist competitive algorithm

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A Stack-based Ensemble Framework for Detecting Cancer MicroRNA Biomarkers

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Genomics, Proteomics & Bioinformatics 2017年第6期15卷 381-388页

作者： Sriparna Saha Sayantan Mitra Ravi Kant Yadav Department of Computer Science and Engineering Indian Institute of TechnologyPatna 801103India

MicroRNA （miRNA） plays vital roles in biological processes like RNA splicing and regulation of gene expression. Studies have revealed that there might be possible links between onco- genesis and expression profiles of some miRNAs, due to their differential expression between normal and tumor tissues. However, the automatic classification of miRNAs into different cate- gories by considering the similarity of their expression values has rarely been addressed. This article proposes a solution framework for solving some real-life classification problems related to cancer, miRNA, and mRNA expression datasets. In the first stage, a mulfiobjective optimization based framework, non-dominated sorting genetic algorithm II, is proposed to automatically determine the appropriate classifier type, along with its suitable parameter and feature combinations, pertinent for classifying a given dataset. In the second page, a stack-based ensemble technique is employed to get a single combinatorial solution from the set of solutions obtained in tke first stage. The performance of the proposed two-stage approach is evaluated on several cancer and RNA expression pro- file datasets. Compared to several state-of-the-art approaches for classifying different datasets, our method shows supremacy in the accuracy of classification.

关键词： Sequential minimal optimizer non-dominated sorting genetic algorithm Multiobjective optimization MicroRNA

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Fuel consumption potential of different external combustion gas-turbine thermodynamic configurations for extended range electric vehicles

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ENERGY 2019年 175卷 900-913页

作者： Reine, Alexandre Nader, Wissam Bou ENSPM Rueil Malmaison France PSA Grp Ctr Tech Velizy Velizy Villacoublay France

External combustion gas-turbine (ECGT) systems are among potential multi-fuel energy converters to substitute the internal combustion engine (ICE) as auxiliary power unit (APU) in future extended range hybrid electric vehicle (EREV) powertrains. Fuel consumption of these APUs in EREV strongly relies on the energy converter efficiency and power to weight ratio as well as on the energy management strategy deployed on-board. This paper presents a technological analysis and investigates the potential of fuel consumption savings of an EREV using different ECGT-system thermodynamic configurations. These include a simple ECGT (S-ECGT), a downstream simple ECGT (DS-ECGT), a downstream intercooled ECGT (DS-ECGT) and a downstream intercooled reheat ECGT (DIRe-ECGT). An energetic and technological analysis is conducted to identify the systems' efficiency and power to weight ratio for different operating temperatures. An EREV model is developed and the different ECGT-system configurations are integrated as APUs. A bi-level optimization method is proposed to optimize the powertrain. It consists of coupling the non-dominated sorting genetic algorithm (NSGA) to the dynamic programing (DP) in order to minimize the fuel consumption and the number of switching On/Off of the APU, which impacts its durability. Fuel consumption simulations are performed on the worldwide-harmonized light vehicles test cycle (WLTC). Results show that the DIRe-ECGT-APU presents an improved fuel consumption compared to the other investigated ECGT-systems and a good potential for implementation in EREVs. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： External combustion gas-turbine Extended-range-electric-vehicle Technological analysis non-dominated sorting genetic algorithm Dynamic programming

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Optimization of chemotherapy and immunotherapy: In silico analysis using pharmacokinetic-pharmacodynamic and tumor growth models

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JOURNAL OF PROCESS CONTROL 2013年第3期23卷 396-403页

作者： Kiran, Kanchi Lakshmi Lakshminarayanan, S. Natl Univ Singapore Dept Chem & Biomol Engn Singapore 117576 Singapore

Chemotherapy is one of the most prominent cancer treatment modalities. However, it is not always a comprehensive solution for tumor regression. This led to the advent of novel strategies to combine chemotherapy with other emerging therapies to treat cancer patients keeping side effects to a minimum. In this work, the focus is on the optimization of chemotherapy using doxorubicin and its combination with adoptive-cell-transfer therapy which is one of the schemes of immunotherapy. The key challenge in the combination therapy is to find the sequence, timing and the dosage of therapies for a given patient. In this regard, an in silica pharmacokinetic/pharmacodynamic model describing the interaction between tumor cells, immune cells and doxorubicin is used to formulate a multi-objective optimization problem by considering clinically relevant objectives and constraints. Then, the multi-objective optimization problem is solved using genetic algorithm and the results obtained for the different cases are compared to discover a therapeutically efficacious treatment regimen. And the metrics used to compare different cases are final tumor size and tumor relapse time. The comparison between chemotherapy alone and its combination with immunotherapy shows that combination therapy is effective in controlling the tumor growth. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Cancer Chemotherapy Immunotherapy Mathematical model Multi-objective optimization non-dominated sorting genetic algorithm

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A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2016年第9期67卷 1184-1199页

作者： Long, Jianyu Zheng, Zhong Gao, Xiaoqiang Pardalos, Panos M. Chongqing Univ Chongqing Peoples R China Univ Florida Gainesville FL USA

The hot metal is produced from the blast furnaces in the iron plant and should be processed as soon as possible in the subsequent steel plant for energy saving. Therefore, the release times of hot metal have an influence on the scheduling of a steel plant. In this paper, the scheduling problem with release times for steel plants is studied. The production objectives and constraints related to the release times are clarified, and a new multi-objective scheduling model is built. For the solving of the multi-objective optimization, a hybrid multi-objective evolutionary algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. In the hybrid multi-objective algorithm, an efficient decoding heuristic (DH) and a non-dominated solution construction method (NSCM) are proposed based on the problem-specific characteristics. During the evolutionary process, individuals with different solutions may have a same chromosome because the NSCM constructs non-dominated solutions just based on the solution found by DH. Therefore, three operations in the original NSGA-II process are modified to avoid identical chromosomes in the evolutionary operations. Computational tests show that the proposed hybrid algorithm based on NSGA-II is feasible and effective for the multi-objective scheduling with release times.

关键词： scheduling steel plant multi-objective optimization non-dominated sorting genetic algorithm release time heuristic

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Shunt capacitor placement in radial distribution networks considering switching transients decision making approach

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2017年 92卷 167-180页

作者： Javadi, Mohammad Sadegh Nezhad, Ali Esmaeel Siano, Pierluigi Shafie-khah, Miadreza Catalao, Joao P. S. Islamic Azad Univ Shiraz Branch Dept Elect Engn Shiraz Iran Islamic Azad Univ Shiraz Branch Young Researchers & Elite Club Shiraz Iran Univ Salerno Dept Ind Engn Fisciano Italy Univ Beira Interior C MAST R Fonte Lameiro P-6201001 Covilha Portugal Univ Porto INESC TEC R Dr Roberto Frias P-4200465 Oporto Portugal Univ Porto Fac Engn R Dr Roberto Frias P-4200465 Oporto Portugal Univ Lisbon Inst Super Tecn INESC ID Av Rovisco Pais 1 P-1049001 Lisbon Portugal

This paper provides a new approach in decision making process for shunt capacitor placement in distribution networks. The main core of the evaluation process is a multi-objective framework to allocate the capacitor banks. The power loss and the total harmonic distortion (THD) are the objective functions of the system under study in a long-term planning horizon. In order to select the executive plan introduced by using a multi-objective model, transient switching overvoltages have been considered. As the size and location of shunt capacitors may result in unacceptable overvoltages, the proposed technical decision making framework can be applied to avoid corresponding damages. In this paper, an iterative conventional power flow technique is introduced. This technique can be applied to evaluate THD for distribution networks as well as other power flow based objectives, such as power losses calculation and voltage stability assessment. The presented framework is a two stage one where at the first stage, a non-dominated sorting genetic algorithm (NSGA-II) augmented with a local search technique is used in order to solve the addressed multi-objective optimization problem. Then, at the second stage, a decision making support technique is applied to determine the best solution from the obtained Pareto front. In order to evaluate the effectiveness of the proposed method, two benchmarks are addressed in this paper. The first test system is a 9-bus distribution network and the second one is an 85-bus large scale distribution network. The simulation results show that the presented method is satisfactory and consistent with the expectation. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Shunt capacitors Switching transients Multi-objective optimization Iterative power flow Local search technique non-dominated sorting genetic algorithm

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Multi-Objective Optimization-Based GA in PLS of IRS-Assisted PDNOMA Communication

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IEEE ACCESS 2024年 12卷 87361-87383页

作者： Thuc, Kieu-Xuan Hong, Nguyen-Thi Anh, Le-Thi Hanoi Univ Ind Fac Informat Technol Hanoi Vietnam Posts & Telecommun Inst Technol Hanoi Vietnam

The intelligent reflecting surface (IRS) supports communication systems well, especially in physical layer security for the cooperative power domain non-orthogonal multiple access (PDNOMA). In this work, we investigate the secrecy performance of PDNOMA with the assistance of the IRS and a multiple-relaying network in the presence of an eavesdropper. Three selection strategies are considered at the relaying network to boost the system's performance: the first method is based on the best relay selection, the second on the max-min concept, and the third on harmonious characteristics. Moreover, the phase shift of IRS element and power allocation for each NOMA user can be controlled to improve the secrecy quality and reduce the influence of an eavesdropper (E). Besides, applying the technique of transmitting artificial noise (AN) from the source is also considered in this paper to interfere with the signal at E. Furthermore, in this paper, we determine two primary metrics to evaluate the secrecy performance of our proposed system: the worst secrecy capacity and secrecy energy efficiency. The balance of these two metrics needs to be assured to improve the secrecy performance. Thus, in this paper, we consider the multi-object problem and propose the genetic algorithm-based approach, a non-dominated sorting genetic algorithm with three procedures (NSGA-II), to solve this problem. Then, to highlight the proposed algorithm's outstanding performance, we compare it with other algorithms, Reference point based NSGA-II (R-NSGA-II) and the exhaustive search (ES). Additionally, the impacts of critical system parameters are investigated for both cases as IRS and none-IRS assistance comprises three relaying selection techniques, the number of IRS elements, the strength of AN signal, the distances of source-relay link, relay-IRS link, and IRS-Eavesdropper link. Finally, the summaries of these archived results show the benefits of our proposed model in different cases of the deployment

关键词： NOMA genetic algorithms Wireless communication 6G mobile communication Security Relays Optimization Energy efficiency IRS-assisted PDNOMA non-dominated sorting genetic algorithm multi-objective optimization worst secrecy capacity secrecy energy efficiency

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