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Reliability-oriented multi-objective optimal decision-making approach for uncertainty-based watershed load reduction

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SCIENCE OF THE TOTAL ENVIRONMENT 2015年 515卷 39-48页

作者： Dong, Feifei Liu, Yong Su, Han Zou, Rui Guo, Huaicheng Peking Univ Coll Environm Sci & Engn Key Lab Water & Sediment Sci MOE Beijing 100871 Peoples R China Tetra Tech Inc Fairfax VA 22030 USA Yunnan Key Lab Pollut Proc & Management Plateau L Kunming 650034 Peoples R China Peking Univ Inst Water Sci Beijing 100871 Peoples R China

Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore, optimal decision-making modeling in watershed load reduction is suffering due to the following challenges: (a) it is difficult to obtain absolutely "optimal" solutions, and (b) decision schemes may be vulnerable to failure. The probability that solutions are feasible under uncertainties is defined as reliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. This study aimed to maximize reliability levels from considerations of cost and load reductions. The Pareto solutions of the ROMO optimization model were generated with the multiobjective evolutionary algorithm, demonstrating schemes representing different biases towards reliability. The Pareto fronts of six maximum allowable emission (MAE) scenarios were obtained, which indicated that decisions may be unreliable under unpractical load reduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide a shortcut for identifying schemes at specific reliability levels for decision makers. The model results indicated that the ROMO approach can offer decision makers great insights into reliability tradeoffs and can thus help them to avoid ineffective decisions. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Stochastic multi-objective evolutionary algorithm Pareto fronts Tradeoff analysis Back propagation neural network

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An improved MOEA/D algorithm for multi-objective multicast routing with network coding

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APPLIED SOFT COMPUTING 2017年 59卷 88-103页

作者： Xing, Huanlai Wang, Zhaoyuan Li, Tianrui Li, Hui Qu, Rong Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Sichuan Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable delay. Therefore, it is of vital importance to minimize various network resources and end-to-end delays while exploiting promising benefits of network coding. multicast has been used in increasingly more applications, such as video conferencing and remote education. In this paper the multicast routing problem with network coding is formulated as a multi-objective optimization problem (MOP), where the total coding cost, the total link cost and the end-to-end delay are minimized simultaneously. We adapt the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for this MOP by hybridizing it with a population-based incremental learning technique which makes use of the global and historical information collected to provide additional guidance to the evolutionary search. Three new schemes are devised to facilitate the performance improvement, including a probability-based initialization scheme, a problem-specific population updating rule, and a hybridized reproduction operator. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art MOEAs regarding the solution quality and computational time. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Network coding multicast multi-objective evolutionary algorithm

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A multi-objective hot-rolling scheduling problem in the compact strip production

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APPLIED MATHEMATICAL MODELLING 2019年 73卷 327-348页

作者： Pan, Quan-Ke Gao, Liang Wang, Ling Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Shandong Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Tsinghua Univ TNList Dept Automat Beijing 100084 Peoples R China

This paper addresses a hot-rolling scheduling problem from compact strip production processes. At first, a mathematical model that consists of two coupled sub-problems is presented. The first sub-problem is the sheet strip assignment problem that is about how to assign sheet-strips to rolling-turns with the objective of minimizing virtual sheet-strips. The second is the sheet-strip sequencing problem that is about how to sort the sheet-strips in each rolling-turn with the objective of minimizing the maximal changes in thickness between adjacent sheet-strips and the change times of the thickness so as to ensure high quality sheet-strips to be produced. And then, an improved hot-rolling scheduling heuristic is proposed to solve the sheet-strip assignment problem. A multi-objective evolutionary algorithm is developed to find the Pareto optimal or near-optimal solutions for the sheet-strip sequencing problem. Besides, the problem-specific knowledge is explored. The key operators including crossover operator, mutation operator and repair operator are designed for the multi-objective evolutionary algorithm. At last, extensive experiments based on real-world instances from a compact strip production process are carried out. The results demonstrate the effectiveness of the proposed algorithms for solving the hot-rolling scheduling problem under consideration. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Scheduling Heuristic multi-objective evolutionary algorithm Hot-rolling Compact strip production

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Parametric optimization of an inerter-based vibration absorber for wind-induced vibration mitigation of a tall building

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WIND AND STRUCTURES 2020年第3期31卷 241-253页

作者： Wang, Qinhua Qiao, Haoshuai Li, Wenji You, Yugen Fan, Zhun Tiwari, Nayandeep Shantou Univ Dept Civil & Environm Engn Shantou City Guangdong Peoples R China Shantou Univ Guangdong Engn Ctr Struct Safety & Hlth Monitorin Shantou 515063 Peoples R China Shantou Univ Dept Elect & Informat Engn Shantou City Guangdong Peoples R China Shantou Univ Key Lab Digital Signal & Image Proc Guangdong Pro Shantou 515063 Guangdong Peoples R China

The inerter-based vibration absorber (IVA) is an enhanced variation of Tuned Mass Damper (TMD). The parametric optimization of absorbers in the previous research mainly considered only two decision variables, namely frequency ratio and damping ratio, and aimed to minimize peak displacement and acceleration individually under the excitation of the across-wind load. This paper extends these efforts by minimizing two conflicting objectives simultaneously, i.e., the extreme displacement and acceleration at the top floor, under the constraint of the physical mass. Six decision variables are optimized by adopting a constrained multi-objective evolutionary algorithm (CMOEA), i.e., NSGA-II, under fluctuating across- and along-wind loads, respectively. After obtaining a set of optimal individuals, a decision-making approach is employed to select one solution which corresponds to a Tuned Mass Damper Inerter/Tuned Inerter Damper (TMDI/TID). The optimization procedure is applied to parametric optimization of TMDI/TID installed in a 340-meter-high building under wind loads. The case study indicates that the optimally-designed TB) outperforms TMDI and TMD in terms of wind-induced vibration mitigation under different wind directions, and the better results are obtained by the CMOEA than those optimized by other formulae. The optimal TB) is proven to be robust against variations in the mass and damping of the host structure, and mitigation effects on acceleration responses are observed to be better than displacement control under different wind directions.

关键词： inerter-based vibration absorber multi-objective evolutionary algorithm decision-making approach wind-induced vibration high-rise buildings

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multi-objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems

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IEEE TRANSACTIONS ON RELIABILITY 2010年第3期59卷 563-575页

作者： Wang, Zai Tang, Ke Yao, Xin Univ Sci & Technol China Sch Comp Sci & Technol NICAL Hefei 230027 Anhui Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

Software testing is an important issue in software engineering. As software systems become increasingly large and complex, the problem of how to optimally allocate the limited testing resource during the testing phase has become more important, and difficult. Traditional Optimal Testing Resource Allocation Problems (OTRAPs) involve seeking an optimal allocation of a limited amount of testing resource to a number of activities with respect to some objectives (e.g., reliability, or cost). We suggest solving OTRAPs with multi-objective evolutionary algorithms (MOEAs). Specifically, we formulate OTRAPs as two types of multi-objective problems. First, we consider the reliability of the system and the testing cost as two objectives. Second, the total testing resource consumed is also taken into account as the third objective. The advantages of MOEAs over state-of-the-art single objective approaches to OTRAPs will be shown through empirical studies. Our study has revealed that a well-known MOEA, namely Nondominated Sorting Genetic algorithm II (NSGA-II), performs well on the first problem formulation, but fails on the second one. Hence, a Harmonic Distance Based multi-objective evolutionary algorithm (HaD-MOEA) is proposed and evaluated in this paper. Comprehensive experimental studies on both parallel-series, and star-structure modular software systems have shown the superiority of HaD-MOEA over NSGA-II for OTRAPs.

关键词： multi-objective evolutionary algorithm parallel-series modular software system software engineering software reliability software testing star-structure modular software system

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Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

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OPTICS COMMUNICATIONS 2015年 338卷 77-89页

作者： Diaz-Ramirez, Victor H. Cuevas, Andres Kober, Vitaly Trujillo, Leonardo Awwal, Abdul Inst Politecn Nacl CITEDI Tijuana 22510 BC Mexico CICESE Dept Comp Sci Ensenada 22860 Baja California Mexico Inst Tecnol Tijuana Tijuana 22500 BC Mexico Lawrence Livermore Natl Lab Natl Ignit Facil Livermore CA 94551 USA

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Moreover, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Object recognition Composite correlation filters multi-objective evolutionary algorithm Combinatorial optimization

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Constrained multi-objective optimization algorithm with an ensemble of constraint handling methods

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ENGINEERING OPTIMIZATION 2011年第4期43卷 403-416页

作者： Qu, B. Y. Suganthan, P. N. Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Different constraint handling techniques have been used with multi-objective evolutionary algorithms (MOEA) to solve constrained multi-objective optimization problems. It is impossible for a single constraint handling technique to outperform all other constraint handling techniques always on every problem irrespective of the exhaustiveness of the parameter tuning. To overcome this selection problem, an ensemble of constraint handling methods (ECHM) is used to tackle constrained multi-objective optimization problems. The ECHM is integrated with a multi-objective differential evolution (MODE) algorithm. The performance is compared between the ECHM and the same single constraint handling methods using the same MODE (using codes available from http://***/home/EPNSugan/***). The results show that ECHM overall outperforms the single constraint handling methods.

关键词： multi-objective evolutionary algorithm multi-objective differential evolution constrained multi-objective optimization ensemble of constraint handling methods

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An adaptive penalty-based boundary intersection method for many-objective optimization problem

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INFORMATION SCIENCES 2020年 509卷 356-375页

作者： Qi, Yutao Liu, Dazhuang Li, Xiaodong Lei, Jiaojiao Xu, Xiaoying Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China RMIT Univ Sch Comp Sci & IT Melbourne Vic Australia

Compared with domination-based methods, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is less prone to the difficulty caused by an increase in the number of objectives. It is a promising algorithmic framework for solving many-objective optimization problems (MaOPs). In MOEA/D, the target MaOP is decomposed into a set of single-objective problems by using a scalarizing function with evenly specified weight vectors. Among the available scalarizing functions, penalty-based boundary intersection (PBI) with an appropriate penalty parameter is known to perform well. However, its performance is heavily influenced by the setting of the penalty factor (0), which can take a value from zero to +infinity. A limited amount of work has thus far considered the choice of an appropriate value of theta. This paper presents a comprehensive experimental study on WFG and WFG-extend problems featuring two to 15 objectives. A range of values of theta is investigated to understand its influence on the performance of the PBI-based MOEA/D (MOEA/D-PBI). Based on the observations, the range of values of theta are divided into three sub-regions, and a two-stage adaptive penalty scheme is proposed to adaptively choose an appropriate value from 0.001 to 8000 during an optimization run. The results of experiments show that, the robustness of MOEA/D-PBI can be significantly enhanced using the proposed scheme. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Many-objective optimization multi-objective evolutionary algorithm based on decomposition Penalty-based boundary intersection Adaptive penalty scheme

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A novel solver formulti-objective optimization: dynamic non-dominated sorting genetic algorithm (DNSGA)

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SOFT COMPUTING 2022年第2期26卷 725-747页

作者： Long, Qiang Li, Guoquan Jiang, Lin Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Chongqing Normal Univ Sch Math Sci Chongqing 400047 Peoples R China Curtin Univ Sch Engn Comp & Math Bentley WA 6102 Australia

Non-dominated sorting is a critical component of all multi-objective evolutionary algorithms (MOEAs). A large percentage of computational cost of MOEAs is spent on non-dominated sorting. So, the complexity of non-dominated sorting method in a large extent decides the efficiency of the MOEA. In this paper, we present a novel non-dominated sorting method called the dynamic non-dominated sorting (DNS). It is based on the sorting of real number sequence instead of dominance comparisons. The computational complexity of DNS is O(mN log N) (m is the number of objectives, N is the population size), which equals to the best record so far. In the numerical experiments, we verify the outperformance of DNS comparing with other non-dominated sorting methods. Based on DNS, we introduce a novel multi-objective genetic algorithm called the dynamic non-dominated sorting genetic algorithm (DNSGA). Numerical experiments on DNSGA are also given. The results show that DNSGA outperforms some other MOEAs on both general-scale and large-scale multi-objective problems.

关键词： Dynamic non-dominated sorting genetic algorithm multi-objective optimization multi-objective evolutionary algorithm Test problems

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An optimization capacity design method of household integrated energy system based on multi-objective egret swarm optimization

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IET RENEWABLE POWER GENERATION 2023年第8期17卷 1993-2008页

作者： Liu, Yakui Yu, Chenhui Li, Dejun Wang, Longquan Li, Xing Li, Hongyun Wang, Fengchao Qingdao Univ Technol Sch Mech & Automot Engn Qingdao Peoples R China Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian Peoples R China

The construction of a household integrated energy system will reduce greenhouse gas emissions and promote sustainable development. Firstly, a household energy system is proposed, which consists of a photovoltaic, wind turbine, electrolysis cell, hydrogen storage tank, and hydrogen-fired gas turbine. The proposed system is modelled as a bi-objective optimization problem in which the minimum daily system economic cost, and the minimum loss of energy supply probability. Secondly, a novel multi-objective egret swarm optimization algorithm with strong search capability and fast convergence is proposed. Thirdly, a household load corresponding to a typical day in spring is chosen as the study case. The optimization results show that the daily system economic cost with the optimal number of devices is 97.48 RMB, and the loss of energy supply probability is 8.33% at the lowest. Finally, to validate the efficiency of the proposed method, the proposed method is compared with NSGA-II (a widely used multi-objective evolutionary algorithm). The comparison indicates that the proposed method has a better diversity due to the random searchability. As a consequence, the proposed method can be used in the optimization capacity design of the integrated energy system.

关键词： energy management systems energy storage multi-objective evolutionary algorithm

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