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PRELIMINARY PLANNING EFFICIENCY EVALUATION FOR SCHOOL BUILDINGS CONSIDERING THE TRADEOFFS OF MOOP AND PLANNING PREFERENCES

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JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 2014年第2期20卷 211-222页

作者： Cheng, Min-Yuan Chen, Ching-Shan Natl Taiwan Univ Sci & Technol Dept Construct Engn Taipei Taiwan Chaoyang Univ Technol Dept Architecture Taichung Taiwan

Seismic resistance and cost effectiveness are often two important building planning objectives for architects. However, these objectives nearly always share a negative correlation with each other, which can cause planning delays and confusion. The conflict between these two is a multi-objective optimization problem (MOOP). Besides, building planning often encompasses both subjective and objective factors. However, most current efficiency evaluation methods focus on the latter and underemphasize the former. Current efficiency evaluation methods are thus not optimized for actual building planning needs. The aim of this study is to develop a new planning efficiency evaluation approach to resolve the above problems. Research methods include the indifference curve, efficient frontier and Data Envelopment Analysis (DEA). The indifference curve deduced the subjective planning preferences of architects;efficient frontier theory constructed the efficient frontier of school buildings;and DEA evaluated the efficiency of various building factors objectively. A total of 326 school buildings in Taichung City, Taiwan in an empirical study designed to illustrate proposed approach effectiveness. The results show that using only objective evaluation or subjective recognition is insufficient to explain the true nature of building planning. Findings can serve as benchmarks for inefficient school buildings at preliminary planning stage.

关键词： school buildings indifference curve efficient frontier data envelopment analysis multi-objective optimization problem benchmark

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multi-objective QPSO for short-term load forecast based on diagonal recursive neural network

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2021年第5期21卷 1113-1124页

作者： Zhang, Lan Xu, Lei Xian Aeronaut Polytech Inst Xian Shaanxi Peoples R China

The short-term load forecast is an important part of power system operation, which is usually a nonlinear problem. The processing of load forecast data and the selection of forecasting methods are particularly important. In order to get accurate and effective prediction for power system load, this article proposes a hybrid multi-objective quantum particle swarm optimization (QPSO) algorithm for short-term load forecast of power system based on diagonal recursive neural network. Firstly, a multi-objective mathematical model for short-term load forecast is proposed. Secondly, the discrete particle swarm optimization (PSO) algorithm is used to select the characteristics of load data and screen out the appropriate data. Finally, the hybrid multi-objective QPSO algorithm is used to train diagonal recursive neural network. The experimental results show that the hybrid multi-objective QPSO for short-term load forecast based on diagonal recursive neural network is effective.

关键词： multi-objective optimization problem diagonal recursive neural network quantum particle swarm optimization algorithm load forecast

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Preference-based optimum design of an integrated structural control system using genetic algorithms

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ADVANCES IN ENGINEERING SOFTWARE 2004年第2期35卷 85-94页

作者： Park, KS Koh, HM Seoul Natl Univ Sch Civil Urban & Geosyst Engn Seoul 151742 South Korea

A new approach for an integrated optimum design of a structural control system is described in this paper. The method considers the structure and active control system as a combined or an integrated system, i.e. the structural sizing variables, locations of controllers and the feedback control gain are both treated as design variables. The size of the structural members, the required control efforts and dynamic responses of the structure are considered as objective functions to be optimized. The simultaneous optimization of the structural control system is essentially formulated as a multi-objective optimization problem. To effectively address this problem, we propose a preference-based optimization model and a genetic algorithm is applied as a numerical searching technique. In the method, for each objective criterion, preference functions are defined that delineate degrees of desirability and optimum variables in both systems are simultaneously found through a preference-guided random searching process. As an example to verify the validity of the proposed approach, an earthquake-excited 10-story building is used and the numerical results are presented. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： structural control integrated system multi-objective optimization problem preference function genetic algorithm

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A new multiobjective evolutionary optimization algorithm based on θ-multiobjective clonal selection

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第3期32卷 1685-1696页

作者： Zareizadeh, Zahra Helfroush, Mohammad Sadegh Kazemi, Kamran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

In this paper a new multi-objective clonal selection algorithm (theta-MCSA) is presented to solve multi-objective problems with multimodal and non-continuous functions. The concept of clonal selection algorithm (CSA) is based on the immune system and white blood cells behavior that select the antibodies similar to antigen for cloning. Although the clonal selection is a robust optimization method, however, as a shortcoming, it takes long time to find optimal Pareto front especially in problems with large search space. To overcome this problem, the proposed method replaces the large search space with the theta-search based on the phase angles. To avoid trapping into local optima in mutation step, two strong mutation methods are implemented according to the iteration number and algorithm efficiency. For converging to uniformly Pareto front in less iterations, the proposed multi-objective algorithm handles the size of the repository and a new population updating mechanism is iteratively applied to select the non-dominate, one-dominate and two-dominate solutions of prior iteration. The experimental results show the efficiency of the proposed theta-MCSA algorithm compared to other methods.

关键词： Clonal selection multi-objective optimization problem theta-search method niching non-dominate solution

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Toward integrated design of additive manufacturing through a process development model and multi-objective optimization

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第9-12期96卷 4145-4164页

作者： Asadollahi-Yazdi, Elnaz Gardan, Julien Lafon, Pascal Univ Technol Troyes UMR CNRS 6281 ICD LASMIS 12 Rue Marie Curie BP2060 F-10010 Troyes France EPF Engn Sch 2 Rue Fernand SASTRE F-10430 Rosiere Pres Troyes France

This paper deals with design for manufacturing (DFM) approach for additive manufacturing (AM) to investigate simultaneously the different attributes and criteria of design and manufacturing. The integrated design approach is provided in the product definition level and it gradually maps the customer requirements to the final product model. The main contribution of this paper is an interface processing engine that is an interface between the product model and manufacturing model. This study uses the Skin-Skeleton approach to model the first definition of the product and model the material flow of AM technology as the manufacturing process. This engine is developed through analysis of all AM technologies and identification of their parameters, criteria, and drawbacks. In order to evaluate some product and process parameters, a multi-objective problem is formulated based on the analysis of all AM technologies;production time and material mass are optimized regarding mechanical behavior of the material and roughness of product. The approach is validated by a case study through a bag hook example. From its requirement specification to the proposed approach, this article defines an optimized product and its manufacturing parameters for fused deposition modeling (FDM) technology.

关键词： Additive manufacturing Design for manufacturing Skin-Skeleton approach multi-objective optimization problem Stochastic algorithm Fused deposition modeling

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A novel multi-objective bi-level programming problem under intuitionistic fuzzy environment and its application in production planning problem

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COMPLEX & INTELLIGENT SYSTEMS 2022年第4期8卷 3263-3278页

作者： Singh, V. P. Sharma, Kirti Chakraborty, Debjani Ebrahimnejad, Ali Visvesvaraya Natl Inst Technol Nagpur Dept Math Nagpur 440010 Maharashtra India Indian Inst Technol Kharagpur Dept Math Kharagpur 721302 W Bengal India Islamic Azad Univ Dept Math Qaemshahr Branch Qaemshahr Iran

This paper presents an optimization method to solve a multi-objective model of a bi-level linear programming problem with intuitionistic fuzzy coefficients. The idea is based on TOPSIS (technique for order preference by similarity to ideal solution) method. TOPSIS method is a multiple criteria method that identifies a satisfactory solution from a given set of alternatives based on the minimization of distance from an ideal point and maximization of distance from the nadir point simultaneously. A new model of multi-objective bi-level programming problem in an intuitionistic fuzzy environment has been considered. The problem is first reduced to a conventional multi-objective bi-level linear programming problem using accuracy function. Then the modified TOPSIS method is proposed to solve the problem at both the leader and the follower level where various linear/non-linear membership functions are used to represent the flexibility in the approach of decision-makers (DMs). The problem is solved hierarchically, i.e., first the problem at the leader level is solved and then the feasible region is extended by relaxing the decision variables controlled by the leader. The feasible region is extended to obtain a satisfactory solution for the DMs at both levels. Finally, the application of the proposed approach in the production planning of a company has been presented. An illustrative numerical example is also given to explain the methodology defined in this paper.

关键词： multi-objective optimization problem Bi-level programming problem TOPSIS method Intuitionistic fuzzy number Fuzzy optimization

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Compact Dual-Band Dual-Sense Circularly Polarized Fragmental Patch Antenna Optimized by Improved Simulated-Annealing-Based Algorithm

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION

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IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2024年第6期5卷 1847-1853页

作者： Shu, Tianyu Feng, Bowen Zhang, Long Chen, Chuyue Chen, Hui Chen, Yaling Zhang, Qinyu Harbin Inst Technol Shenzhen Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen 518055 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China

This study presents a compact dual-band dual-sense circularly polarized (CP) fragmental patch antenna that advanced by an improved simulated-annealing-based optimization algorithm. This design replaces truncated corners of traditional truncated patch antennas with fragmental structures, generating dual-band dual-CP radiation with a small frequency ratio and a high front-to-back ratio. The proposed optimized framework tackles the multi-objective optimization problem through hierarchical optimization to achieve an optimal balance among various performance metrics. Furthermore, the simulated annealing is improved using a matrix-based dynamic step-size perturbation mechanism and a nested cyclic process, averting premature convergence and ensuring multifaceted objective enhancement. The measurement results reveal that the proposed antenna can operate with a small frequency ratio of 1.07, providing left-hand CP radiation from 4.99 to 5.02 GHz and right-hand CP radiation 5.34 to 5.41 GHz, respectively. This compact cost-efficient design demonstrates potential for diverse antenna applications.

关键词： Antennas optimization Dual band Patch antennas Perturbation methods Convergence Antenna arrays Circular polarization dual-band dual-sense multi-objective optimization problem simulated annealing

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Analysis of Pareto Solutions Based on Non-Correspondence in Spread Between objective Space and Design Variable Space

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2015年第5期19卷 681-687页

作者： Yoshida, Toru Yoshikawa, Tomohiro Nagoya Univ Dept Computat Sci & Engn Chikusa Ku Nagoya Aichi 4648603 Japan

Recently, many studies have been conducted on multi-objective Genetic Algorithm (MOGA), in which Genetic Algorithms are applied to multi-objective optimization problems (MOPs). Among various applications, MOGA is also applied to engineering design problems, which require not only high-performance Pareto solutions to be obtained, but also an analysis of the obtained Pareto solutions and extraction of design knowledge about the problem itself. In order to analyze the Pareto solutions obtained by MOGA, it is necessary to consider the objective space and the design variable space. The aim of this study is to extract and analyze solutions of relevant interest to designers. In this paper, we propose three solutions to analyze and extract design knowledge from MOGA. (1) We define "Non-Correspondence in Spread" between the objective space and the design variable space. (2) We try to extract the Non-Correspondence area in Spread using the index defined in this paper. (3) We apply the defined index to genetic search to obtain Pareto solutions that have different design variables and similar fitness values. This paper applies the above index to the trajectory design optimization problem and extracts Non-Correspondence area in Spread from the obtained Pareto solutions. This paper also shows that robust Pareto solutions can be obtained using genetic search using the defined index.

关键词： multi-objective optimization problem MOGA non-correspondence objective space design variable space

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multi-objective optimal design of semi-active fluid viscous dampers for nonlinear structures using NSGA-II

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STRUCTURES 2020年 24卷 678-689页

作者： Bakhshinezhad, Sina Mohebbi, Mohtasham Univ Mohaghegh Ardabili Fac Engn Ardebil *** Iran

This paper presents an effective method to design semi-active fluid viscous damper (SAFVD) system for mitigating the seismic responses of the nonlinear frames considering multiple safety and convenience criteria. This method is based on defining a multi-objective optimization problem with discrete objective functions of minimization of inter-story drift as safety criterion as well as absolute acceleration as convenience criterion where an improved version of the non-dominated sorting genetic algorithm (NSGA-II) has been used to solve it and find out the Pareto-optimal solutions. For this purpose, the maximum damping coefficient of the semi-active damper and parameters of the control algorithm have been selected as design variables. The effect of the uncertainty of the input seismic excitation has been considered using the mean responses under multiple real earthquakes recommended for the site that the structure is located in. To assess the effectiveness of the proposed methodology, a numerical example has been conducted on an eight-story nonlinear shear building frame with hysteretic bilinear elasto-plastic behavior. The results show that the designed optimal SAFVD system has the capability of a significant reduction in seismic responses and improving both the safety and convenience of the entire building. Also comparing the seismic performance of SAFVD system with that of active control system shows that SAFVD could perform as effective as active control system in structural response mitigation.

关键词： Semi-active fluid viscous damper multi-objective optimization problem Non-dominated sorting genetic algorithm (NSGA-II) Nonlinear structure Instantaneous optimal control multiple design earthquakes

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EnLoc: Data Locality-aware Energy-efficient Scheduling Scheme for Cloud Data Centers

EnLoc: Data Locality-aware Energy-efficient Scheduling Schem...

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IEEE International Conference on Communications (ICC) / Workshop on Integrating UAVs into 5G

作者： Kaur, Kujeet Kumar, Neeraj Garg, Sahil Rodrigues, Joel J. P. C. Thapar Univ Comp Sci & Engn Dept Patiala Punjab India Natl Inst Telecommun Inatel Santa Rita Do Sapucai Brazil Inst Telecomunicacoes Aveiro Portugal ITMO Univ St Petersburg Russia Univ Fortaleza UNIFOR Fortaleza Ceara Brazil

ISBN: (纸本)9781538631805

With the rapid proliferation of big data, real-time processing of huge datasets becomes a challenging task;primarily because of their heterogeneous nature. Due to this, one of the most serious concerns of the modern cloud data centers is massive energy consumption during job execution. Hence, energy-aware task scheduling with data placement are considered as two important parameters for enhanced energy efficiency of modern cloud data centers. Moreover, considering the "pay-per-use" model of cloud computing infrastructure, it is important to maintain desirable service level agreement (SLA) while attaining improved data locality. Poor task scheduling decisions with limited focus of data locality are the prime reasons for escalated data communications and energy utilization levels. In order to deal with the aforementioned issues, data locality-aware energy-efficient (EnLoc) scheme for task scheduling and data placement has been proposed, particularly for MapReduce framework. The proposed EnLoc scheme is a multi-objective optimization problem (MOOP) and is solved using multi-objective evolutionary algorithm with "Tchebycheff decomposition";wherein the formulated MOOP is decomposed into theoretically finite number of subproblems to get optimal scheduling and placement decisions. The proposed scheme has been evaluated on real-time data traces acquired from OpenCloud Hadoop Cluster. The results obtained clearly demonstrate that the proposed EnLoc scheme outperforms the existing schemes in terms of energy efficiency, SLA assurance, and data locality.

关键词： Data locality energy efficiency MapReduce multi-objective optimization problem multi-objective evolutionary algorithm with decomposition scheduling

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