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Application of Tabu-BOA to acquire multimodal and multiobjective solutions for electric equipment configuration problems in a power plant

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ELECTRICAL ENGINEERING IN JAPAN 2005年第3期152卷 8-20页

作者： Katsumata, Y Terano, T Tokyo Inst Technol Tokyo 152 Japan

This paper applies the Bayesian optimization Algorithm with Tabu Search (Tabu-BOA) to electric equipment configuration problems in a power plant. Tabu-BOA is a hybrid evolutionary computation algorithm with competent genetic algorithms and meta-heuristics. The configuration problems we consider have complex combinatorial properties, and therefore are hard to formulate and solve via conventional mathematical programming techniques. Using the proposed method, we have solved the following problems (in order of increasing complexity): (I) cost minimization of electric equipment configuration and the corresponding cabling, (2) plus choice of the power plant operation patterns, (3) plus parallel operation of multiple transformers, (4) plus change of the supply voltages (high voltage or low voltage) to the electric power load, and (5) with addition of another objective, that is, both minimization of the cost and maximization of the surplus power supply. (C) 2005 Wiley Periodicals, Inc.

关键词： power plant electric equipment optimum design tabu search Bayesian optimization algorithm multiple solutions multiobjective optimization problem

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A dynamic optimization method for power distribution network operation with high ratio photovoltaics

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第21期16卷 4417-4432页

作者： Liu, Keyan Zhan, Huiyu Wei, Yuhan Kang, Tianyuan China Elect Power Res Inst 15 Xiaoying E Rd Beijing 100192 Peoples R China

With the high ratio distributed photovoltaics (PVs) penetration to the distribution network (DN), the bearing capacity, integration capacity, consuming capacity and controlling capacity of DN for PVs are all facing great challenges. Enhancing the flexible regulation and operation optimization capabilities of DN have become key factors to satisfy these new challenges. For the operation optimization problem of high ratio PVs penetration, a novel quasi-equal curtailment ratio (QCR) constraint of PVs is proposed in the dynamic optimization method for DN operation. Firstly, the uncertainty of high ratio PVs and their active power deviations are discussed. And a dynamic optimization framework of PVs based on optimal power flow (OPF) has been proposed. Secondly, the optimization model for high ratio PVs is formulated with multi-objective of maximum PVs output, minimum voltage deviation and minimum line loss, under the equality and inequality constraints of power flow, PVs power generation limit, QCR of PVs and so on. Then the multi-objective optimization problem (MOP) is transformed into a single-objective optimization problem (SOP) by weighting coefficients, and solved by sequential quadratic programming (SQP) with trust-region (TR) searching algorithm. Finally, the proposed method is tested, verified and compared with the primal dual interior point (PDIP) and traditional SQP algorithms in IEEE 33-bus, PG&E 69-bus, 292-bus and 1180-bus test cases. The experimental results show the rapidity and robustness of the proposed method.

关键词： search problems PV power generation limit operation optimization problem optimal power flow SQP algorithm high ratio distributed photovoltaic penetration quadratic programming QCR multiobjective optimization problem single-objective optimization problem dynamic optimization framework minimum line loss primal dual interior point dynamic optimization method active power deviation bearing capacity trust-region searching algorithm controlling capacity photovoltaic power systems high ratio PVs penetration power generation economics IEEE 292-bus test case power distribution network operation IEEE 33-bus test case minimum voltage deviation distributed power generation PG&E 69-bus test case IEEE 1180-bus test case novel quasiequal curtailment ratio constraint optimization model sequential quadratic programming integration capacity consuming capacity load flow demand side management flexible regulation DN operation power distribution economics PDIP

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Variational principles and optimal solutions of the inverse problems of creep bending of plates

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JOURNAL OF APPLIED MECHANICS AND TECHNICAL PHYSICS 2012年第5期53卷 751-760页

作者： Bormotin, K. S. Oleinikov, A. I. Komsomolsk On Amur State Tech Univ Komsomolsk On Amur 681013 Russia

It is shown that inverse problems of steady-state creep bending of plates in both the geometrically linear and nonlinear formulations can be represented in a variational formulation. Steady-state values of the obtained functionals corresponding to the solutions of the problems of inelastic deformation and elastic unloading are determined by applying a finite element procedure to the functionals. Optimal laws of creep deformation are formulated using the criterion of minimizing damage in the functionals of the inverse problems. The formulated problems are reduced to the problems solved by the finite element method using *** software.

关键词： inverse creep problem damage variational principles multiobjective optimization problem optimal control

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A proximal Newton-type method for equilibrium problems

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optimization LETTERS 2018年第5期12卷 997-1009页

作者： Santos, P. J. S. Santos, P. S. M. Scheimberg, S. Univ Fed Rio de Janeiro PESC Rio de Janeiro Brazil UFPI CMRV Parnaiba Brazil

In this work we extend the method considered by von Heusinger et al. (Math Program 132(1):99-123, 2012) to the setting of equilibrium problems. Moreover, we introduce a family of auxiliary bifunctions containing that one used by them. We prove that the method is locally convergent and we establish the superlinear/quadratic convergence of the algorithm to a solution of the problem under suitable assumptions. An application of the proposed method to multiobjective optimization problems is considered. Some preliminary numerical results are reported showing the performance of our algorithm.

关键词： Equilibrium problem Nonsmooth Newton method Constant rank constraint qualification Variational inequality problem multiobjective optimization problem Computable generalized Jacobian

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FQTSFM: A fuzzy-quantum time series forecasting model

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INFORMATION SCIENCES 2021年 566卷 57-79页

作者： Singh, Pritpal Jagiellonian Univ Inst Theoret Phys Ul Lojasiewicza 11 PL-30348 Krakow Poland Natl Taipei Univ Technol Dept Elect Engn Taipei 10608 Taiwan

The study shows that there are two main problems that affect the performance of fuzzy time series (FTS) models, namely the selection of the universe of discourse and the determination of the fuzzy degree of the memberships. However, the selection of the appropriate universe of discourse along with the degree of memberships is simultaneously associated with the multiobjective optimization problem (MOOP). Therefore, in this study, an improved version of the quantum optimization algorithm (QOA) has been proposed to find their optimal solu-tions. This improved QOA is called fast forward quantum optimization algorithm (FFQOA). Finally, by integrating FFQOA with the FTS modelling approach, a hybrid model called fuzzy-quantum time series forecasting model (FQTSFM) is designed. The main objective of FFQOA in FQTSFM is to select the Pareto-optimal front (POF) from all optimal non-dominated Pareto solutions by using the archive and grid concept during simulation. The main advantages of the proposed FQTSFM are that it converges very fast compared to the existing hybridized based FTS models and is able to evolve one-step ahead forecasted results. The FQTSFM is tested with three different datasets, namely daily average tempera-tures of Taipei, Taiwan Futures Exchange (TAIFEX) index and Taiwan Stock Exchange Corporation (TSEC) weighted index. The performance of the FQTSFM is compared with var-ious established well-known FTS and non-FTS models in terms of different statistical met-rics. In the case of daily average temperatures, TAIFEX index and TSEC weighted index datasets, the FQTSFM achieves correlation coefficients of 0.9988, 0.9978 and 0.9768, respec-tively. For daily average temperatures, TAIFEX index and TSEC weighted index datasets, the Theil's U statistic values of the FQTSFM are 0.0022, 0.0022 and 0.0054, respectively. The FQTSFM has mean squared errors of 0.3959, 22.3965 and 83.6793 for the daily average tem-peratures, the TAIFEX index and the TSEC-weighted index datase

关键词： Fuzzy time series Forecasting multiobjective optimization problem (MOOP) Fast forward quantum optimization algorithm (FFQOA) Degree of membership

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Quadratic Form optimization with Fuzzy Number Parameters: multiobjective Approaches

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2020年第4期22卷 1191-1197页

作者： Al-Mumtazah, Niswatus S. Surono, Sugiyarto Ahmad Dahlan Univ Fac Sci & Appl Technol Dept Math Yogyakarta Indonesia

problems in daily life can be modeled into mathematical forms, one of them is the optimization of the quadratic form where the objective functions are the quadratic function. There are many real problems involving variables that cannot be stated numerically so that fuzzy logic appears. The purpose of this research is to optimize the quadratic form with all its parameters in the form of a fuzzy number by using the method of a triangular fuzzy number. This research resulted in a new completion method by utilizing definitions and arithmetic on the triangular fuzzy numbers. The resulted method is by changing a single fuzzy quadratic program becomes a simple quadratic multiobjective program. By using Sum of Objective Method, the multiobjective problem can be changed into single optimization problem. This single optimization problem is then completed using Karush-Kuhn-Tucker method, resulting in three optimal values which will form optimal values in the form of triangular fuzzy numbers.

关键词： optimization Quadratic Fuzzy program Triangular fuzzy numbers Karush-Kuhn-Tucker conditions multiobjective optimization problem Sum of Objective Method

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Bi-objective green vehicle routing problem

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2022年第3期29卷 1602-1626页

作者： Erdogdu, Kazim Karabulut, Korhan Yasar Univ Dept Software Engn TR-35100 Izmir Turkey

The green vehicle routing problem (GVRP) is a variant of the vehicle routing problem (VRP), which increasingly attracts many researchers in recent years due to the growing global environmental issues. As the transportation of the products grows, the number of vehicles in fleets and the pollutants caused by these vehicles also grow, which in turn negatively affects human health. In this paper, a biobjective GVRP was studied. The two objectives are minimizing the total distance and minimizing the total fuel consumption of all vehicle routes. As a solution method, an adaptive large neighborhood search was hybridized with two new local search heuristics. The proposed method was applied to two well-known benchmark problem sets for VRPs and new approximate Pareto fronts were obtained for these benchmark sets.

关键词： green vehicle routing problem multiobjective optimization problem adaptive large neighborhood algorithm evolutionary algorithms multiobjective algorithms

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A Hybrid multiobjective Particle Swarm optimization Algorithm Based on R2 Indicator

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IEEE ACCESS 2018年 6卷 14710-14721页

作者： Wei, Li-Xin Li, Xin Fan, Rui Sun, Hao Hu, Zi-Yu Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China

When dealing with complex multiobjective problems, particle swarm optimization algorithm is easy to fall into local optimum and lead to uneven distribution. Therefore, this paper presents a hybrid multiobjective particle swarm optimization algorithm based on R2 indicator (R2HMOPSO) for solving multiobjective optimization problem. The proposed algorithm uses the sigmoid function mapping method to adjust the inertia weight and learning factors in order to tradeoffs the exploration and exploitation process effectively. In addition, simulation binary crossover operator is designed to reinitialize the particles to improve the search capability of the algorithm and to prevent particles from falling into local optimum and premature convergence. R2 indicator is incorporated into the R2HMOPSO algorithm so as to deal with the solutions of uneven distribution on the true Pareto front. Besides, polynomial mutation is used to maintain diversity in the external archive. The improved algorithm is evaluated on standard benchmarks. By comparing it with four state-of-the-art multiobjective optimization algorithms, the simulation results show that R2HMOPSO algorithm is competitive and effective in terms of convergence and distribution.

关键词： multiobjective optimization problem R2 indicator particle swarm algorithm decomposition method

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Recent advances in hybrid evolutionary algorithms for multiobjective manufacturing scheduling

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 112卷 616-633页

作者： Gen, Mitsuo Zhang, Wenqiang Lin, Lin Yun, YoungSu Fuzzy Log Syst Inst Dept Res Iizuka Fukuoka Japan Tokyo Univ Sci Res Inst Sci & Technol Tokyo 162 Japan Henan Univ Technol Coll Informat Sci & Engn Zhengzhou Henan Peoples R China Minist Educ Key Lab Grain Informat Proc & Control Zhengzhou Henan Peoples R China Dalian Univ Technol Sch Software Engn Dalian Peoples R China Chosun Univ Div Business Adm Gwangju South Korea

In real manufacturing systems there are many combinatorial optimization problems (COP) imposing on more complex issues with multiple objectives. However it is very difficult for solving the intractable COP problems by the traditional approaches because of NP-hard problems. For developing effective and efficient algorithms that are in a sense "good," i.e., whose computational time is small as within 3 min, we have to consider three issues: quality of solution, computational time and effectiveness of the nondominated solutions for multiobjective optimization problem (MOP). In this paper, we focus on recent hybrid evolutionary algorithms (HEA) to solve a variety of single or multiobjective scheduling problems in manufacturing systems to get a best solution with a smaller computational time. Firstly we summarize multiobjective hybrid genetic algorithm (Mo-HGA) and hybrid sampling strategy-based multiobjective evolutionary algorithm (HSS-MoEA) and then propose HSS-MoEA combining with differential evolution (HSS-MoEA-DE). We also demonstrate those hybrid evolutionary algorithms to bicriteria automatic guided vehicle (B-AGV) dispatching problem, robot-based assembly line balancing problem (R-ALB), bicriteria flowshop scheduling problem (B-FSP), multiobjective scheduling problem in thin-film transistor-liquid crystal display (TFT-LCD) module assembly and bicriteria process planning and scheduling (B-PPS) problem. Also we demonstrate their effectiveness of the proposed hybrid evolutionary algorithms by several empirical examples. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Combinatorial optimization problem multiobjective optimization problem Hybrid evolutionary algorithms Genetic algorithm AGV dispatching Assembly line balancing Flowshop scheduling model TFT-LCD module assembly model Process planning and scheduling model

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Genetic algorithm for the personnel assignment problem with multiple objectives

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INFORMATION SCIENCES 2007年第3期177卷 787-803页

作者： Toroslu, Ismail H. Arslanoglu, Yllmaz Middle E Tech Univ Dept Comp Engn TR-06531 Ankara Turkey

The assignment problem is a well-known graph optimization problem defined on weighted-bipartite graphs. The objective of the standard assignment problem is to maximize the summation of the weights of the matched edges of the bipartite graph. In the standard assignment problem, any node in one partition can be matched with any node in the other partition without any restriction. In this paper, variations of the standard assignment problem are defined with matching constraints by introducing structures in the partitions of the bipartite graph, and by defining constraints on these structures. According to the first constraint, the matching between the two partitions should respect the hierarchical-ordering constraints defined by forest and level graph structures produced by using the nodes of the two partitions respectively. In order to define the second constraint, the nodes of the partitions of the bipartite graph are distributed into mutually exclusive sets. The set-restriction constraint enforces the rule that in one of the partitions all the elements of each set should be matched with the elements of a set in the other partition. Even with one of these constraints the assignment problem becomes an NP-hard problem. Therefore, the extended assignment problem with both the hierarchical-ordering and set-restriction constraints becomes an NP-hard multi-objective optimization problem with three conflicting objectives;namely, minimizing the numbers of hierarchical-ordering and set-restriction violations, and maximizing the summation of the weights of the edges of the matching. Genetic algorithms are proven to be very successful for NP-hard multi-objective optimization problems. In this paper, we also propose genetic algorithm solutions for different versions of the assignment problem with multiple objectives based on hierarchical and set constraints, and we empirically show the performance of these solutions. (C) 2006 Elsevier Inc. All rights reserved.

关键词： assignment problem bipartite graph matching hierarchical-ordering constraint set constraint NP-complete multiobjective optimization problem genetic algorithm

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