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SPEED UP RELIABILITY MODEL OPTIMIZATION WITH HYPERVOLUME CONTRIBUTION CALCULATING algorithm

INTELLIGENT AUTOMATION AND SOFT COMPUTING 2011年第5期17卷 619-629页

作者： Zhou, Xiuling Guo, Ping Chen, C. L. Philip Beijing Normal Univ Lab Image Proc & Pattern Recognit Beijing 100875 Peoples R China Beijing City Univ Artificial Intelligence Inst Beijing Peoples R China Univ Macau Fac Sci & Technol Macau Peoples R China

Software dependability modelling involves simultaneous consideration of several incompatible and often conflicting objectives, while hypervolume-based multi-objective evolutionary algorithm (MOEA) has been shown to produce better results for multi-objective problem in practice. A frame of reliability model optimization with hypervolume based MOEA is presented. Focusing on the key issue of hypervolume based MOEA, a new algorithm, set hypervolume contribution by slicing objective (SHSO), is proposed for calculating the exclusive hypervolume contribution of a subset to the whole nondominated set directly for small dimension. For the special case of SHSO, CHSO (the contribution of a point to hypervolume by slicing objective) is improved with heuristics. The feasibility and efficiency of developed algorithms are shown by experiments.

关键词： Reliability model optimization multi-objective evolutionary algorithm Hypervolume

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multi-objective evolutionary Optimization and Monte Carlo Simulation for Placement of Low Impact Development in the Catchment Scale

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2017年第9期143卷

作者： Giacomoni, M. H. Joseph, John Univ Texas San Antonio Dept Civil & Environm Engn 1 UTSA Circle San Antonio TX 78249 USA

Restoring the hydrologic flow regime of urban areas by promoting infiltration, retention, and evapotranspiration on the site is one of the goals of low-impact development (LID). These goals can be achieved through the implementation of stormwater control measures (SCMs) such as green roofs and permeable pavements. The effectiveness of SCMs can be influenced not only by their design but also by their location. The present study applies multi-objective evolutionary optimization and Monte Carlo simulation approaches to help identify nearoptimal locations of green roofs and permeable pavements in the catchment scale. The Nondominated Sorting Genetic algorithm II was connected to the stormwater management model (SWMM) to identify the location of SCMs and characterize the tradeoffs between flow regime alteration and implementation costs. The impact of implementing SCMs is measured by peak flow, runoff volume, and the hydrologic footprint residence (HFR). The HFR is a new stormwater metric that represents dynamics of inundated areas and residence time of flood waves throughout downstream segments. The approach was tested in an illustrative case study of an 11.7-ha urban catchment divided into five subcatchments. The results indicate that locating SCMs in downstream subcatchments can reduce peak flow more effectively, whereas SCMs placed in upstream subcatchments better reduce the HFR. The proposed methodology can help stormwater managers to better assess the combined performance of LID-SCMs in different hydrologic scales and generate guidelines for prioritizing the implementation or retrofitting of urban areas with green infrastructure. (C) 2017 American Society of Civil Engineers.

关键词： Low-impact development Green roof Permeable pavement Hydrologic footprint residence multi-objective evolutionary algorithm Monte Carlo simulation

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Application of a novel multi-objective optimisation method integrated with the artificial neural networks for optimum design of a plate heat exchanger

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AUSTRALIAN JOURNAL OF MECHANICAL ENGINEERING 2020年第1期18卷 1-15页

作者： Jilak, Ahmad Assareh, Ehsanolah Nedaei, Mojtaba Islamic Azad Univ Dept Mech Engn Dezful Branch Dezful Iran Univ Padua Dept Management & Engn Vicenza Italy Islamic Azad Univ Dept Energy Engn Grad Sch Environm & Energy Sci & Res Branch Tehran Iran

In the current study, a plate heat exchanger model is optimised using a multi-objective evolutionary algorithm/decomposition. Considering the importance of the thermal efficiency and the cost of manufacturing the heat exchangers, these two parameters are used as objective functions during the optimisation process. For the purpose of analysis, the epsilon-NTU method was applied to analyse the probability values for the pressure drop and efficiency of the heat exchanger. The experimental data for the analysis were extracted from a gas furnace in a ceramics factory located in the Kerman province. The results of the analysis reported 19.962% enhancement in the efficiency rate as well as 9.533% reduction in cost through the proposed model, which indicated a significant improvement. In another section of this study, by performing a Pareto front analysis, the distribution of the optimum points for the design variables was outlined. As well as that, the correlation between the objective functions and the design variables by employing the artificial neural networks was discussed. The obtained results were then compared with the two reference cases to achieve a better insight towards the new developed model scheme. At the last stage of study, an error analysis concluded that in most cases, the performance and cost error rates are close to zero with the maximum values being in the range of 0.6-1 and 0-0.13, respectively.

关键词： Flat fin heat exchangers plate heat exchangers artificial neural network multi-objective evolutionary algorithm decomposition (MOEA/D)

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Reducing the loss of agricultural productivity due to compact urban development in municipalities of Switzerland

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COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2017年第Sep.期65卷 162-177页

作者： Schwaab, Jonas Deb, Kalyanmoy Goodman, Erik Lautenbach, Sven van Strien, Maarten Gret-Regamey, Adrienne Swiss Fed Inst Technol Inst Spatial & Landscape Planning Zurich Switzerland Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Ctr Study Evolut Act BEACON NSF E Lansing MI 48824 USA Univ Bonn IGG Dept Urban Planning & Real Estate Management Bonn Germany

Globally urban growth destroys fertile soils and endangers food security. Fertile soils are often located in the vicinity of existing urban areas. Thus, preserving high-quality soils can conflict with the objective of developing compact urban patterns. In this study, we assess the trade-off between compact urban patterns and urban patterns that can help reduce the loss of agricultural productivity by maintaining fertile agricultural soils. We assess the trade-offs for selected municipalities in Switzerland using a multi-objective evolutionary algorithm to create a front of non-dominated solutions. These results are used as a benchmark against which we compare simulations of a Business-As-Usual urban expansion in Switzerland to estimate the potential for reducing the loss of agricultural productivity. By analysing the front of non-dominated solutions, we show that there are areas of open land that can be converted into residential land without trading-off compactness against agricultural productivity. We show that there is a large potential for reducing the loss of agricultural productivity when optimizing the configuration of urban development. This potential strongly varies between municipalities and seems to depend primarily on the amount of demand for new urban land within each municipality. The proposed methodology of using multi-objective optimization, followed by a post-optimality analysis and including results from business as-usual development can be used to support the decision-making processes in urban planning. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： municipality loss reduction multi-objective evolutionary algorithm agricultural productivity urban pattern Commerce Urban Development Switzerland Business

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Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach

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BMC GENOMICS 2018年第Sup7期19卷 672-672页

作者： Beltran, Jesus A. Aguilera-Mendoza, Longendri Brizuela, Carlos A. Ctr Sci Res & Higher Educ Ensenada CICESE Dept Comp Sci Carretera Ensenada Tijuana 3918 Ensenada 22860 Baja California Mexico

Background: Antimicrobial peptides are a promising alternative for combating pathogens resistant to conventional antibiotics. Computer-assisted peptide discovery strategies are necessary to automatically assess a significant amount of data by generating models that efficiently classify what an antimicrobial peptide is, before its evaluation in the wet lab. Model's performance depends on the selection of molecular descriptors for which an efficient and effective approach has recently been proposed. Unfortunately, how to adapt this method to the selection of molecular descriptors for the classification of antimicrobial peptides and the performance it can achieve, have only preliminary been explored. Results: We propose an adaptation of this successful feature selection approach for the weighting of molecular descriptors and assess its performance. The evaluation is conducted on six high-quality benchmark datasets that have previously been used for the empirical evaluation of state-of-art antimicrobial prediction tools in an unbiased manner. The results indicate that our approach substantially reduces the number of required molecular descriptors, improving, at the same time, the performance of classification with respect to using all molecular descriptors. Our models also outperform state-of-art prediction tools for the classification of antimicrobial and antibacterial peptides. Conclusions: The proposed methodology is an efficient approach for the development of models to classify antimicrobial peptides. Particularly in the generation of models for discrimination against a specific antimicrobial activity, such as antibacterial. One of our future directions is aimed at using the obtained classifier to search for antimicrobial peptides in various transcriptomes.

关键词： Antimicrobial peptides Feature weighting Molecular descriptors Classification multi-objective evolutionary algorithm Peptide representation

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multi-objective evolutionary architectural pruning of deep convolutional neural networks with weights inheritance

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INFORMATION SCIENCES 2024年 685卷

作者： Chung, K. T. Lee, C. K. M. Tsang, Y. P. Wu, C. H. Asadipour, Ali Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Lab Artificial Intelligence Design Hong Kong Sci Pk Hong Kong Peoples R China Hang Seng Univ Hong Kong Dept Supply Chain & Informat Management Hong Kong Peoples R China Royal Coll Art Comp Sci Res Ctr London England

Despite the ongoing success of artificial intelligence applications, the deployment of deep learning models on end devices remains challenging due to the limited onboard computational resources. A way to tackle this challenge is model compression through network pruning, which removes unnecessary parameters to reduce model size without significantly affecting performance. However, existing iterative methods require designated pruning rates and obtain a single pruned model, which lacks the flexibility to adapt to devices with heterogeneous computational capabilities. This paper considers network pruning in Deep Convolutional Neural Networks (DCNNs) and proposes a novel algorithm for structured filter pruning in DCNNs using a multi- objective evolutionary approach with a novel weights inheritance scheme and representation scheme to reduce the time cost of the optimization process. The proposed method provides solutions with multiple levels of tradeoff between performance and efficiency for various hardware specifications on edge devices. Experimental results demonstrate the effectiveness of the proposed framework in optimizing popular DCNN models in terms of model complexity and accuracy. Notably, the framework successfully made significant reductions in floating-point operations ranging from 40% to 90% of VGG-16/19 and ResNet-56/110 with negligible loss in accuracy on the CIFAR-10/100 dataset.

关键词： Network pruning multi-objective evolutionary algorithm Deep convolutional neural networks Deep learning

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Combining Crowding Estimation in objective and Decision Space With multiple Selection and Search Strategies for multi-objective evolutionary Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第3期44卷 378-393页

作者： Xia, Hu Zhuang, Jian Yu, Dehong Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

Many multi-objective evolutionary algorithms (MOEAs) have been successful in approximating the Pareto Front. However, well-distributed solutions in the objective and decision spaces are still required in many real-life applications. In this paper, a novel MOEA is proposed to this problem. Distinct from other MOEAs, the proposed algorithm suggests a framework, which includes two crowding estimation methods, multiple selection methods for mating and search strategies for variation, to improve the MOEA's searching ability, and the diversity of its solutions. The algorithm emphasizes the importance of using the decision space and the objective space diversities. The objective space crowding and decision space crowding distances are designed using different ideas. To produce new individuals, three different types of mating selections and their respective search strategies are constructed for the main population and the two sparse populations, with the help of the two crowding measurements. Finally, based on the experimental tests on 17 unconstrained multi-objective optimization problems, the proposed algorithm is demonstrated to have better results compared to several state-of-the-art MOEAs. A detailed analysis on the effectiveness and robustness of the framework is also presented.

关键词： multi-objective evolutionary algorithm decision space diversity multiple search strategies multiple mating selections

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Kinetic facades: An evolutionary-based performance evaluation framework

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JOURNAL OF BUILDING ENGINEERING 2022年 53卷

作者： Sadegh, Salman Oukati Gasparri, Eugenia Brambilla, Arianna Globa, Anastasia Politecn Milan Dept Architecture Built Environm & Construction En Milan Italy Univ Sydney Sch Architecture Design & Planning Sydney Australia

multi-functional kinetic facades have great potential to adapt to the environment, reduce building energy consumption, regulate shading and natural ventilation, and improve human thermal and visual comfort. However, due to their complex nature, kinetic facades systems are often associated with highly onerous time-consuming design and fabrication processes, which significantly limit their diffusion and market uptake. This research aims to propose an integrated, systematic framework that supports kinetic facades' performance design and decision making since projects' early design stages. The proposed method makes use of multi-objective evolutionary algorithms (MOEA) which have proven successful in providing optimum solutions for complex problems. The efficacy of the framework is then validated based on a case study analysis to improve daylighting performance in an office building context. The results indicate that the integration of the proposed framework can provide a variety of data to support performance evaluation and to identify the most efficient kinetic facades solutions;moreover, this method provided a variety of optimal solutions using Pareto front and the Ranking Method;each being advantageous in a specific set of criteria as evidenced by improved useful daylight illuminance by up to 30%, daylight autonomy by up to 20% while keeping the under-lit and over-lit values as low as under 10%. Finally, future research can focus on the integration of soft computing methods to create a smart and intelligent performance evaluation framework for kinetic facades.

关键词： Adaptive building envelope Responsive building fa?ade multi-objective evolutionary algorithm Evaluation framework Building performance

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Use of Energy-Based Domain Knowledge as Feedback to evolutionary algorithms for the Optimization of Water Distribution Networks

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WATER 2020年第11期12卷 3101-3101页

作者： Paez, Diego Salcedo, Camilo Garzon, Alexander Gonzalez, Maria Alejandra Saldarriaga, Juan Queens Univ Dept Civil Engn Kingston ON K7L 3N6 Canada Univ Arizona Dept Civil & Architectural Engn & Mech Tucson AZ 85721 USA Univ Andes Water Supply & Sewer Syst Res Ctr CIACUA Bogota 111711 Colombia Univ Andes Dept Civil & Environm Engn Bogota 111711 Colombia

The optimization of water distribution networks (WDN) has evolved, requiring approaches that seek to reduce capital costs and maximize the reliability of the system simultaneously. Hence, several evolutionary algorithms, such as the non-dominated sorting-based multi-objective evolutionary algorithm (NSGA-II), have been widely used despite the high computational costs required to achieve an acceptable solution. Alternatively, energy-based methods have been used to reach near-optimal solutions with reduced computational requirements. This paper presents a method to combine the domain knowledge given by energy-based methods with an evolutionary algorithm, in a way that improves the convergence rate and reduces the overall computational requirements to find near-optimal Pareto fronts (PFs). This method is divided into three steps: parameters calibration, preprocessing of the optimal power use surface (OPUS) results, and periodic feedback using OPUS in NSGA II. The method was tested in four benchmark networks with different characteristics, seeking to minimize the costs of the WDN and maximizing its reliability. Then the results were compared with a generic implementation of NSGA-II, and the performance and quality of the solutions were evaluated using two metrics: hypervolume (HV) and modified inverted generational distance (IGD+). The results showed that the feedback procedure increases the efficiency of the algorithm, particularly the first time the algorithm is retrofitted.

关键词： multi-objective evolutionary algorithm NSGA-II optimal power use surface (OPUS) hypervolume IGD plus domain knowledge hot start

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Improving NSGA-II algorithm Based on Minimum Spanning Tree

Improving NSGA-II Algorithm Based on Minimum Spanning Tree

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7th International Conference on Simulated Evolution and Learning

作者： Li, Miqing Zheng, Jinhua Wu, Jun Xiangtan Univ Inst Informat Engn Xiangtan 411105 Peoples R China

ISBN: (纸本)9783540896937

Diversity maintenance is an importance part of multi-objective evolutionary algorithm. In this paper, a new variant for the NSGA-II algorithm is proposed. The basic idea is that using the crowding distance method designed by minimum spanning tree to maintain the distribution of solutions. From an extensive comparative study with NSGA-II on a number of two and three objective test problems, it is observed that the proposed algorithm has good performance in distribution, and is also rather competitive to NSGA-II concerning the convergence.

关键词： multi-objective optimization multi-objective evolutionary algorithm Crowding distance Minimum spanning tree

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