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A numerical framework for the multi-objective optimal design of check dam systems in erosion-prone areas

ENVIRONMENTAL MODELLING & SOFTWARE 2019年 119卷 21-31页

作者： Pal, Debasish Galelli, Stefano Singapore Univ Technol & Design Pillar Engn Syst & Design Singapore 487372 Singapore

Check dams are one of the most popular measures for soil erosion control. Despite the availability of various process-based modelling tools, their design is often carried out in an uncoordinated manner, potentially leading to check dam systems with limited life expectancy or unreliable sediment retention capacity. In this study, we consider the problem of determining the optimal location and size (i.e., initial storage capacity) of a given number of check dams, and tackle this problem by contributing a numerical framework that builds on geo-processing, two process-based models (WaTEM/SEDEM and StoDyM), and multi-objective evolutionary computation. The application of our framework to two catchments in the Chinese Loess Plateau, characterized by similar erosion processes but different extensions (4.26 and 13.97 km(2)), reveals a strong trade-off between three criteria of system's performance, namely life expectancy, sediment retention capacity, and storage dynamics. Results also show that there are opportunities for improving the performance of existing check dam systems through a coordinated, optimization-based, planning exercise.

关键词： Soil erosion Check dams Optimization multi-objective evolutionary algorithms WaTEM/SEDEM Loess plateau

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AI based realtime task schedulers for multicore processor based low power biomedical devices for health care application

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multiMEDIA TOOLS AND APPLICATIONS 2022年第29期81卷 42079-42095页

作者： Prabhaker, M. Lordwin Cecil Ponnan, Suresh Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept ECE Chennai Tamil Nadu India

The bioinformatics data processing plays a vital role in low power biomedical devices. The functional domain of processing biological data is collection, execution, conversion, storing and distribution. So, there is an effective multiple objective real time task scheduling technique are required to provide better solution in this domain. This paper describes novel AI based multi-objective evolutionary algorithmic techniques such as multi-objective genetic algorithm (MOGA), non-dominated sorting genetic algorithm (NSGA) and multi-objective messy genetic algorithm (MOMGA) for scheduling real time tasks to a multicore processor-based low power biomedical device used for health care application. These techniques improve the performance upon earlier reported system by considering multiple objectives such as, low power consumption (P), maximizing core utilization (U) and minimizing deadline miss-rate (delta). The novelty of this work is to achieve the schedulability of realtime tasks by computing the converging value of a series of task parameters such as execution time, release time, workload and arrival time. Finally, we investigated the performance parameters such as power consumption (P), deadline miss-rate (delta), and core utilization for the given architecture. The evaluation results show that the power consumption is reduced to about 5-8%, utilization of the core is increased about 10% to 40% and deadline miss-rate is comparatively minimized with conventional realtime scheduling approaches.

关键词： Bioinformatics Low power biomedical devices multicore architecture multi-objective evolutionary algorithms AI realtime task schedulers

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multi-objective design and control of hybrid systems minimizing costs and unmet load

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ELECTRIC POWER SYSTEMS RESEARCH 2009年第1期79卷 170-180页

作者： Bernal-Agustin, Jose L. Dufo-Lopez, Rodolfo Univ Zaragoza Dept Elect Engn Zaragoza 50018 Spain

This paper presents, for the first time, the application of the strength Pareto evolutionary algorithm to the multi-objective design of isolated hybrid systems, minimising both the total cost throughout the useful life of the installation and the unmet load. For this task. a multi-objective evolutionary algorithm (MOEA) and a genetic algorithm (GA) have been used in order to find the best combinations of components for the hybrid system and control strategy. Also, a novel control strategy has been developed and it will be expounded in this article. As an example of application, a PV-wind-diesel system has been designed, obtaining a Set of possible Solutions (Pareto set) from which the designer can choose those which he/she prefers considering the costs and unmet load of each. The results obtained demonstrate the practical utility of the design method used. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Hybrid systems multi-objective design multi-objective evolutionary algorithms Genetic algorithms

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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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Optimisation of photovoltaic-diesel-battery stand-alone systems minimising system weight

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ENERGY CONVERSION AND MANAGEMENT 2016年第Jul.期119卷 279-288页

作者： Cristobal-Monreal, Ivan R. Dufo-Lopez, Rodolfo Ctr Univ Def Acad Gen Militar Ctra Huesca S-N Zaragoza 50090 Spain Univ Zaragoza Dept Elect Engn Calle Maria de Luna 3 Zaragoza 50018 Spain

This article shows a new method for the optimisation of stand-alone (off-grid) hybrid systems (photovoltaic-diesel-battery) to supply the electricity of mobile systems such as non-governmental organization hospitals, temporary camps or other mobile facilities to be placed temporally in remote or conflictive areas. If there is difficult or dangerous access, the most important objective to be minimised is the total weight of the system. Also, the cost is an important variable to minimise. Nowadays, the majority of these systems are diesel-only or diesel-battery systems. However, depending on the duration of the temporary system, a photovoltaic-diesel-battery system can have a lower weight and/or cost. Three types of optimisation are considered: (i) minimisation of the weight of the system;(ii) minimisation of the cost;and (iii) minimisation of both weight and cost. The two first are conducted by genetic algorithms, and the last one is performed using multi-objective evolutionary algorithms. An example of application of this method to a temporary hospital in Central African Republic is shown, concluding that in the cases of more than 90 days photovoltaic (flexible crystalline silicon panels) + diesel + battery is the solution which minimises weight. When minimising cost, all the cases include photovoltaic with high penetration. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Renewable stand-alone systems Weight minimisation Genetic algorithms multi-objective evolutionary algorithms

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Design of isolated hybrid systems minimizing costs and pollutant emissions

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RENEWABLE ENERGY 2006年第14期31卷 2227-2244页

作者： Bernal-Agustin, Jose L. Dufo-Lopez, Rodolfo Rivas-Ascaso, David M. Univ Zaragoza Dept Elect Engn Zaragoza 50018 Spain

In this paper we expound, for the first time, the application of the Strength Pareto evolutionary Algorithm to the multi-objective design of isolated hybrid systems. The design is posed as an optimisation problem whose solution allows obtaining the configuration of the system as well as the control strategy that simultaneously minimises both the total cost through the useful life of the installation and the pollutant emissions. As an example, we have designed a PV-wind-diesel system for two different load profiles, obtaining a set of possible solutions from which the designer can choose those which he prefers considering the costs and pollutant emissions of each one of them. The reached results demonstrate the practical utility of the design method used. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： hybrid systems multi-objective design multi-objective evolutionary algorithms

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Response surface approximation of Pareto optimal front in multi-objective optimization

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2007年第4-6期196卷 879-893页

作者： Goel, Tushar Vaidyanathan, Rajkumar Haftka, Raphael T. Shyy, Wei Queipo, Nestor V. Tucker, Kevin Univ Florida Dept Mech & Aerosp Engn Gainesville FL 32611 USA Univ Zulia Fac Engn Appl Comp Inst Maracaibo 4011 Venezuela NASA George C Marshall Space Flight Ctr Huntsville AL 35812 USA

A systematic approach is presented to approximate the Pareto optimal front (POF) by a response surface approximation. The data for the POF is obtained by multi-objective evolutionary algorithm. Improvements to address drift in the POF are also presented. The approximated POF can help visualize and quantify trade-offs among objectives to select compromise designs. The bounds of this approximate POF are obtained using multiple convex-hulls. The proposed approach is applied to study trade-offs among objectives of a rocket injector design problem where performance and life objectives compete. The POF is approximated using a quintic polynomial. The compromise region quantifies trade-offs among objectives. (c) 2006 Elsevier B.V. All rights reserved.

关键词： Pareto optimal front response surface approximation multi-objective evolutionary algorithms Rocket injector design Pareto drift

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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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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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Latency-Aware multi-objective Fog Scheduling: Addressing Real-Time Constraints in Distributed Environments

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IEEE ACCESS 2024年 12卷 62543-62557页

作者： Altin, Lokman Topcuoglu, Haluk Rahmi Gurgen, Fikret Sadik Bogazici Univ Dept Comp Engn TR-34450 Istanbul Turkiye Siemens Advanta Turkey TR-34870 Istanbul Turkiye Marmara Univ Fac Engn Comp Engn Dept TR-34854 Istanbul Turkiye

The fog computing paradigm was introduced to overcome challenges that cannot be addressed by conventional cloud computing, such as the lower response latency for real-time applications. Task scheduling in fog environments sets forth more complexity using novel objectives beyond scheduling in the cloud. In this study, a task scheduling model with five common objectives and two latency metrics is presented. We propose a latency aware multi-objective multi-rank scheduling algorithm, LAMOMRank, for fog computing. The performance of our algorithm was compared with that of three well known multi-objective scheduling algorithms, Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto evolutionary Algorithm (SPEA2) and multi-objective Heterogeneous Earliest Finish Time (MOHEFT) algorithm, using three multi-objective metrics and two latency addressing metrics. We populate workload sets using Pegasus workflows and the DeFog benchmark to be distributed over two fog clusters generated with various Amazon Web Services instances. The empirical results validate the significance of our algorithm for better latency fronts including the response latency and task delivery time without performance degradation on multi-objective metrics.

关键词： Fog computing task scheduling latency-constrained applications multi-objective optimization multi-objective evolutionary algorithms directed acyclic graphs

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