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IEEE Congress on evolutionary Computation (CEC)

作者： de Moraes, Matheus Bernardelli Coelho, Guilherme Palermo Univ Estadual Campinas Sch Technol Limeira SP Brazil

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

Many real-world optimization problems involve time-consuming fitness evaluation. To reduce the computational cost of expensive evaluations, researchers have been developing surrogate models to approximate the objective function values of unevaluated candidate solutions. However, most of the research has been developed for continuous optimization problems, while only a few of them address surrogate modeling for expensive multi-objective Combinatorial Optimization Problems (COPs). COPs have inherently different challenges than continuous optimization. For example, (i) many COPs have categorical and nominal decision variables;(ii) they often require the combination of both global and local search mechanisms;and (iii) some of them have constraints that make them NP-hard problems, which makes them even more difficult to solve with a reasonable number of fitness evaluations. To address these issues, this paper proposes a surrogate-assisted evolutionary algorithm that combines the decomposition-based algorithm MOEA/D, Tabu Local Search, and Random Forest as a surrogate model to approximate the objective function of unevaluated individuals on multi-objective COPs. Experiments were conducted on constrained and unconstrained well-known multi-objective combinatorial optimization benchmark problems. The experimental results demonstrate that the proposed design outperforms state-of-the-art algorithms without violating the restrictions in the number of objective function evaluations, which indicates that it may be suitable for real-world expensive multi-objective COPs.

关键词： Expensive multi-objective Combinatorial Optimization multi-objective evolutionary algorithm based on decomposition Random Forest Surrogate Models Tabu Search

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An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches

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Journal of Systems Engineering and Electronics 2021年第2期32卷 272-285页

作者： WU Xiuli PENG Junjian XIE Zirun ZHAO Ning WU Shaomin School of Mechanic Engineering University of Science and Technology BeijingBeijing 100083China Kent Business School University of KentKent CT27FSUK

In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse ***,a flexible job shop problem with the variable batches scheduling model is ***,we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting ***,in order to increase the diversity of the population,two methods are *** is the threshold to control the neighborhood updating,and the other is the dynamic clustering algorithm to update the ***,a group of experiments are carried *** results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively,and has effective performance in solving the flexible job shop scheduling problem with variable batches.

关键词： flexible job shop variable batch inverse scheduling multi-objective evolutionary algorithm based on decomposition a batch optimization algorithm with inverse scheduling

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An improved MOEA/D for multi-objective job shop scheduling problem

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2017年第6期30卷 616-640页

作者： Zhao, Fuqing Chen, Zhen Wang, Junbiao Zhang, Chuck Lanzhou Univ Technol Sch Comp & Commun Lanzhou Peoples R China Northwestern Polytech Univ Key Lab Contemporary Design & Integrated Mfg Tech Minist Educ Xian Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

In this article, an improved multi-objective evolutionary algorithm, which is based on decomposition (IMOEA/D) for multiobjective job shop scheduling problem, is proposed to solve multiple objectives job shop scheduling problems. Three minimisation objectives - the maximum completion time (makespan), the total flow time and the tardiness time are considered simultaneously. In the proposed algorithm, several prior rules are presented to construct the initial population with a high level of quality. Meanwhile, according to the contribution of each operator to the external archive, an adaptive mechanism is adopted to select corresponding operators to generate new solutions, which can accelerate convergence speed. Simulation results on the standard test instances show that IMOEA/D has a better convergence performance compared with multi-objective evolutionary algorithms based on Pareto dominance.

关键词： decomposition job shop scheduling multi-objective optimisation multi-objective evolutionary algorithm based on decomposition

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multi-objective covering salesman problem: a decomposition approach using grey wolf optimization

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KNOWLEDGE AND INFORMATION SYSTEMS 2023年第1期65卷 281-339页

作者： Khan, Indadul Basuli, Krishnendu Maiti, Manas Kumar Chandrakon Vidyasagar Mahavidyalaya Dept Comp Sci Paschim Medinipur 721201 W Bengal India West Bengal State Univ Dept Comp Sci Kolkata 700126 W Bengal India Mahishadal Raj Coll Dept Math Purba Medinipur 721628 W Bengal India

In this study, the basic grey wolf optimization (GWO) algorithm ismodified along with K-bit exchange, K-opt operation, and integrated with the structure of multi-objective evolutionary algorithm with decomposition approach (MOEA/D) to solve multi-objective covering salesman problem(MOCSP). The algorithm is named a "multi-objective evolutionary algorithm with decomposition using GreyWolf optimization (MOEA/D-GWO)." The K-opt operation with K = 3 and K = 4 is used to generate the initial solution set. The GWO algorithm is modified with a set of newly introduced perturbation rules. A two-stage updating mechanism has been introduced to improve the quality of a potential solution. The first sage of the process is done by the modified GWO algorithm, and in the second stage, a perturbation technique using K-bits exchange operation is applied. The MOEA/D-GWO algorithm is a two-phase algorithm where in the first phase, the clustering/grouping of cities is done, and in the next phase one city from each cluster/group is selected to search pareto-optimal Hamiltonian cycles in such a way that each cycle maintains the pre-define covering distance. Here, for the first time a heuristic approach is proposed forMOCSP. Different sizes of standard benchmark MOCSP test instances are used to test the performance of the MOEA/D-GWO algorithm. The instances are generated from TSPLIB. Different traditional multi-objective optimization algorithms, like NSGA-II, SPEA2, MOEA/D, MR-ABCWCD, SMPSO, SR4-MOEA/D for MOOP, have been modified according toMOCSP and implemented to compare the efficiency of the proposed approach. Nine standard well-known performance metrics/indicators have been used to analyse the performance of the MOEA/D-GWO algorithm for MOCSP. Different sizes bi-objective instances are generated from TSPLIB, for the illustration and testing the performance of the algorithm. Detailed problem generation approach is also discussed. From all the numerical studies, it is concluded that the

关键词： multi-objective covering salesmen problem Grey wolf optimization multi-objective evolutionary algorithm based on decomposition K-opt operation K-bits exchange

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An Efficient Meta-Heuristic for multi-objective Flexible Job Shop Inverse Scheduling Problem

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IEEE ACCESS 2018年 6卷 59515-59527页

作者： Wu, Rui Li, Yibing Guo, Shunsheng Li, Xixing Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Hubei Peoples R China Hubei Univ Technol Sch Mech Engn Wuhan 430068 Hubei Peoples R China

In reality, uncertainties may still encounter after a scheduling scheme is generated. These may make the original schedule non-optimal or even impossible. Traditional scheduling methods are not effective in dealing with these situations. In response to this phenomenon, by introducing the idea of inverse optimization into the scheduling field, a new scheduling strategy called "inverse scheduling'' has been proposed. To the best of our knowledge, this is the first study to be conducted on flexible job shop inverse scheduling problem (FJISP). In this paper, first, a comprehensive mathematical model with adjustable processing time is established. Then, a hybrid multi-objective evolutionary algorithm based on decomposition and particle swarm optimization is adopted for solving FJISP. To make the proposed algorithm solving FJISP more efficiently, some new strategies are used. A 3-D coding scheme is employed to represent the particles, multiple strategies are designed for generating a high-quality initial population, and effective discrete crossover and mutation operators are specially designed according to the FJISP's characteristics. Finally, computational experiments are carried out using extended benchmarks, and the results demonstrate the effectiveness of the proposed algorithm for solving the FJISP.

关键词： Flexible job shop inverse scheduling problem inverse optimization multi-objective optimization multi-objective evolutionary algorithm based on decomposition particle swarm optimization

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Developing an integrated model for planning the delivery of construction materials to post-disaster reconstruction projects

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2022年第3期9卷 1135-1156页

作者： Gharib, Zahra Yazdani, Maziar Bozorgi-Amiri, Ali Tavakkoli-Moghaddam, Reza Taghipourian, Mohammad Javad Univ Tehran Coll Engn Sch Ind Engn Tehran *** Iran Univ New South Wales Sch Civil & Environm Engn Sydney NSW 2052 Australia Islamic Azad Univ Dept Business Management Chalous Branch Chalous 46615397 Iran

Construction material delivery to post-disaster reconstruction projects is challenging because of the resource and time limitations that follow a large-scale disaster. There is compelling evidence that inadequate planning jeopardises the success of a large number of post-disaster reconstruction projects. Thus, the current study proposes an integrated approach to facilitate the procurement planning of construction materials following a large-scale disaster. The proposed approach clustered the location of construction projects using a differential evolution (DE)-K-prototypes, a new partitional clustering algorithm based on DE and K-prototypes, method. Then, using a permanent matrix prioritises cluster points based on route reliability-affecting factors. The model's objectives are to minimise the total travel time, maximise the reliability of the route, and minimise the total weighted undelivered materials to projects. In the case of distribution of material through land vehicles, the possibility of breakdowns in the vehicle is considered, allowing for the determination of vehicle breakdown under various scenarios and the minimisation of undelivered materials to projects. As a result of the uncertain character of the disaster, the demands of construction projects are fuzzy, and Jimenez's method is used to handle it. Due to the complexity of the problem, two algorithms are proposed, a multi-objective evolutionary algorithm based on decomposition (MOEA/D) and a non-dominated sorting genetic algorithm-II (NSGA-II). The results confirm that the proposed MOEA/D has a higher accuracy while NSGA-II has a shorter computational time. By providing new theoretical perspectives on disaster recovery strategies in the construction sector, this study contributes to the growing body of knowledge about disaster recovery strategies in the sector. The findings of this study can be employed to develop an integrated planning system for the delivery of construction materials to post-disaste

关键词： post-disaster reconstruction non-dominated sorting genetic algorithm-II post disaster construction material multi-objective evolutionary algorithm based on decomposition augmented epsilon-constraint method

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Determining an appropriate partitioning method to reduce the power system dimensions for real time voltage control

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2018年 100卷 58-68页

作者： Karbalaei, Farid Shahbazi, Hamidreza Shahid Rajaee Teacher Training Univ Fac Elect Engn Tehran Iran

One of the ways to increase the speed of adaptive optimal coordinated voltage control (OCVC) is the reduction of the power system dimensions. To this end, after occurrence of a disturbance, the power system is initially partitioned into interior, boundary and exterior zones. Partitioning is based on the fact that voltage instability is a local phenomenon at the early moments after fault occurrence;therefore the variation of boundary and exterior variables is small. Then, the equations of boundary and exterior zones are linearized, and the variables and equations of exterior zone-except for control variables-are removed. This will lead to a considerable decrease in the number of power system equations. Three partitioning based on admittance matrix system, active and reactive power flow in transmission lines are taken into consideration and the appropriate method resulting in the minimal linearization error is selected. This paper also uses the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to increase the problem solving speed in adaptive optimal coordinated voltage control. The simulation results on the New England 39-bus and IEEE 118-bus systems demonstrate that calculations speed have been remarkably improved while retaining the required precision.

关键词： Adaptive optimal coordinated voltage control Spectral graph partitioning System dimensions reduction multi-objective evolutionary algorithm based on decomposition

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Stochastic Disassembly Sequence Optimization for Profit and Energy Consumption

Stochastic Disassembly Sequence Optimization for Profit and ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Fu, Yaping Zhou, MengChu Guo, Xiwang Qi, Liang Qingdao Univ Sch Business Qingdao Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Liaoning Shihua Univ Coll Comp & Commun Engn Fushun Peoples R China Shandong Univ Sci & Technol Dept Comp Sci & Tech Qingdao Peoples R China

ISBN: (纸本)9781538666500

Industrial products' reuse, recovery and recycling are very important because of their environmental and economic benefits. Effective disassembly sequencing can improve recovery revenue and reduce environment impact. In this work, a stochastic dual-objective disassembly sequencing problem is establishers which includes maximizing disassembly profit and minimizing energy consumption. Two popular and classical multi-objective evolutionary algorithms, i.e., nondominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition, are used to deal with this problem. By conducting simulation experiments on several numerical cases and analyzing experimental results with wellknown performance metrics, i.e., inverted generational distance and hypervolume, this work concludes that both can be used to obtain highly desired solutions.

关键词： Disassembly sequence optimization nondominated sorting genetic algorithm II multi-objective evolutionary algorithm based on decomposition energy consumption optimization

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Research of multi-point infill criteria based on multi-objective optimization front and its application on aerodynamic shape optimization

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ADVANCES IN MECHANICAL ENGINEERING 2017年第6期9卷

作者： Ma, Yang Zhou, Wei Han, Qilong PLA Rocket Force Univ Engn Dept Power Engn Xian 710025 Shanxi Peoples R China Northwestern Polytech Univ Sci & Technol Aerosp Flight Dynam Lab Xian Peoples R China Northwestern Polytech Univ Sch Astronaut Xian Peoples R China

In this article, efficient multi-point infill criteria based on multi-objective optimization front and its applications on analytic functions and airfoil aerodynamic shape optimization are researched. In the expensive optimization, an excellent infill algorithm should meet the demand of local exploitation and global exploration at the same time, which stand for the optimization accuracy and efficiency, respectively. In the proposed infill criteria, the predicted value and a statistic variable are chosen as objectives to implement optimization, and the obtained front stands for the points which strike a balance between local optimization accuracy and global optimization efficiency. The test results of four analytic functions, which has 2 to 20 variables, show that compared with expected improvement algorithm, the proposed multi-point infill algorithm can save 85% iteration times, so the computation efficiency could be increased remarkably. The airfoil optimization design is adopted to test the validity and efficiency of the proposed infill criteria. In the airfoil optimization design, the computational fluid dynamic method is employed to obtain the aerodynamic forces of airfoils, and the Kriging surrogate model is introduced to reduce the computational cost. The global optimization is implemented by adopting multi-objective evolutionary algorithm based on decomposition as optimization algorithm, and the drag coefficient and section area of the airfoil are taken as optimization objectives. The proposed infill method and expected improvement method are employed to implement the optimization design process. With roughly equal times of aerodynamic computation, both of the algorithms can obtain the reliable Pareto front;however, the number of infill iteration of proposed infill method is only 17% of that of expected improvement method.

关键词： Infill criteria Kriging surrogate model airfoil optimization design expensive optimization multi-objective evolutionary algorithm based on decomposition

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