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Dynamic strategy based fast decomposed GA coordinated with FACTS devices to enhance the optimal power flow

ENERGY CONVERSION AND MANAGEMENT 2010年第7期51卷 1370-1380页

作者： Mahdad, Belkacem Bouktir, T. Srairi, K. EL Benbouzid, M. Univ Biskra Dept Elect Engn Biskra 07000 Algeria Dept Elect Engn Oum El Bouaghi 04000 Algeria Univ Brest Lab Brestois Mecan & Syst Brest France

Under critical situation the main preoccupation of expert engineers is to assure power system security and to deliver power to the consumer within the desired index power quality. The total generation cost taken as a secondary strategy. This paper presents an efficient decomposed GA to enhance the solution of the optimal power flow (OPF) with non-smooth cost function and under severe loading conditions. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two sub problems are proposed to coordinate the OPF problem under different loading conditions: the first sub problem related to the active power planning under different loading factor to minimize the total fuel cost, and the second sub problem is a reactive power planning designed based in practical rules to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the proposed algorithm tested on IEEE 30-Bus, 26-Bus and IEEE 118-Bus under different loading conditions and compared with global optimization methods (GA, EGA, FGA, PSO, MTS, MDE and ACO) and with two robust simulation packages: PSAT and MAT-POWER. The results show that the proposed approach can converge to the near solution and obtain a competitive solution at critical situation and with a reasonable time. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Decomposed network parallel genetic algorithm System loadability FACTS SVC Optimal power flow System security Planning and control

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A multi-objective reliability optimization for reconfigurable systems considering components degradation

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2019年 183卷 104-115页

作者： Zhao, Jiangbin Si, Shubin Cai, Zhiqiang Northwestern Polytech Univ Sch Mech Engn Xian 710072 Shaanxi Peoples R China

Reconfigurable systems have been widely used in practical engineering, especially for the reconfigurable computing systems and reconfigurable manufacturing systems. The reliability of reconfigurable systems can be improved by components replacement or components rearrangement without changing their reliability. Combining the advantages of the rearrangement method and replacement method, an integrated method is proposed to improve the reconfigurable system reliability cost-effectively in this paper. Then, a 0-1 integer programming model of multi-objective optimization is established to obtain the reconfiguration with maximum system reliability and minimum reconfiguration cost based on the integrated method. The coarse-grained parallel genetic algorithm (CPGA) is introduced to solve the multi-objective model, while the multiple objectives problem can be converted into a single objective problem through the novel fitness function. Finally, three examples based on the production monitoring system are implemented to illustrate the effectiveness of the CPGA comparing with the replacement based genetic algorithm. The changes of optimal reconfigurations with different parameters of the fitness function and different pre-determined system reliability are also discussed based on the examples.

关键词： Reconfigurable system Multi-objective optimization Reconfiguration cost parallel genetic algorithm

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GeNESiS: gene network evolution simulation software

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BMC BIOINFORMATICS 2008年第1期9卷 1-7页

作者： Kratz, Anton Tomita, Masaru Krishnan, Arun Keio Univ Inst Adv Biosci Tsuruoka Yamagata 9970035 Japan

Background: There has been a lot of interest in recent years focusing on the modeling and simulation of Gene Regulatory Networks (GRNs). However, the evolutionary mechanisms that give rise to GRNs in the first place are still largely unknown. In an earlier work, we developed a framework to analyze the effect of objective functions, input types and starting populations on the evolution of GRNs with a specific emphasis on the robustness of evolved GRNs. Results: In this work, we present a parallel software package, GeNESiS for the modeling and simulation of the evolution of gene regulatory networks (GRNs). The software models the process of gene regulation through a combination of finite-state and stochastic models. The evolution of GRNs is then simulated by means of a genetic algorithm with the network connections represented as binary strings. The software allows users to simulate the evolution under varying selective pressures and starting conditions. We believe that the software provides a way for researchers to understand the evolutionary behavior of populations of GRNs. Conclusion: We believe that GeNESiS will serve as a useful tool for scientists interested in understanding the evolution of gene regulatory networks under a range of different conditions and selective pressures. Such modeling efforts can lead to a greater understanding of the network characteristics of GRNs.

关键词： genetic algorithm Gene Regulatory Network Pane Message Passing Interface parallel genetic algorithm

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Fast source term estimation using the PGA-NM hybrid method

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 62卷 68-79页

作者： Li, Hui Zhang, Jianwen Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Beijing Univ Chem Technol Fluid Mech & Heat Transfer Lab Beijing 100029 Peoples R China

There are significant challenges related to estimating the source term of the atmospheric release. Urged on by robots in performing emergency responding tasks, a fast and accurate algorithm for this inversion problem is indispensable. Sometimes the NM simplex algorithm is efficient in the optimization problem, but sometimes the quality of convergence is unacceptable as a numerical breakdown, even for smooth and well-behaved functions. In contrast, full convergence might be seen in parallel genetic algorithms with a comparative slower convergence. In this paper we combine the PGA and the NM simplex algorithm by initializing simplex from the final individual of PGA results and obtaining the best vertex through simplex algorithm thereafter. A numerical simulation of the proposed algorithm shows noteworthy improvement of efficiency and robustness, compared with the PGA or the NM algorithm only.

关键词： Gaussian dispersion model Pasquill-Gifford dispersion model Nelder-Mead algorithm parallel genetic algorithm Inversion problem

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Selective vehicle routing problems under uncertainty without recourse

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2014年 62卷 68-88页

作者： Allahviranloo, Mandieh Chow, Joseph Y. J. Recker, Will W. Univ Calif Irvine Inst Transportat Studies Irvine CA 92697 USA Ryerson Univ Dept Civil Engn Toronto ON M5B 2K3 Canada

We argue that the selective vehicle routing problem is more appropriate than the conventional VRP in handling uncertainty with limited resources. However, previous formulations of selective VRPs have all been deterministic. Three new formulations are proposed to account for different optimization strategies under uncertain demand (or utility) level: reliable, robust, and fuzzy selective vehicle routing problems. Three parallel genetic algorithms (PGAs) and a classic genetic algorithm are developed and compared to the deterministic solution. PGAs differ based on their communication strategies and diversity in sub-populations. Results show that a PGA, wherein communication between demes, or subpopulations, occurs in every generation and does not eliminate repeated chromosomes, outperforms other algorithms at the cost of higher computation time. A faster variation of PGA is used to solve the non-convex reliable selective VRP, robust selective VRP and the large-scale fuzzy selective VRP, consisting of 200 nodes. Large scale application demonstrates the value of fuzzy selective vehicle routing problem FSVRP in humanitarian logistics. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Selective vehicle routing problem Stochastic optimization Fuzzy optimization Reliability Robust optimization parallel genetic algorithm Humanitarian logistics

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An asymmetric pinching damaged hysteresis model for glubam members: Parameter identification and model comparison

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COMPUTERS & STRUCTURES 2024年 305卷

作者： Shi, Da Demartino, Cristoforo Marano, Giuseppe Carlo Xu, Yongjia Politecn Torino DISEG Dept Struct Geotech & Bldg Engn Corso Duca Abruzzi 24 I-10129 Turin Italy Zhejiang Univ Coll Civil Engn & Architecture 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Roma Tre Univ Dept Architecture I-00153 Rome Italy Cent South Univ Sch Civil Engn Changsha 410075 Hunan Peoples R China

The performance of glue laminated bamboo (glubam) members is governed by the nonlinear response at their joints, where high deformation levels and stress concentrations are developed. Numerous phenomenological models are presently employed to describe the hysteresis behavior of these joints, while these models always have an excessive number of parameters, and the physical interpretation of these parameters is often challenging. Moreover, some hysteresis models cannot capture all hysteresis features such as asymmetry, pinching, and damage. Consequently, this paper introduces a novel phenomenological-based hysteretic model named Asymmetric Pinching Damaged (APD) model, and implemented it in Abaqus by combining connector and spring elements in series or parallel. This model encompasses asymmetry, pinching, and strength degradation for bamboo joint components, with parameters that possess clear physical meanings and are readily comprehensible. This study also presented a parameter identification framework coupling the parallel genetic algorithm (PGA) and Bayesian Neural Network (BNN). By merging the FE modeling and optimizing algorithms with the interactive application of ABAQUS and Python software platforms, the integrated identification framework is capable of performing multi-threaded parallel computation of finite element models considering the BNN-based uncertainty quantification, thus greatly improving the efficiency of parameter identification.

关键词： Glue laminated bamboo Asymmetric Pinching Damaged (APD) hysteretic models Parameter identification parallel genetic algorithm Bayesian neural network

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Multi-objective fuzzy multiprocessor flowshop scheduling

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APPLIED SOFT COMPUTING 2014年 21卷 139-148页

作者： Behnamian, J. Ghomi, S. M. T. Fatemi Bu Ali Sina Univ Fac Engn Dept Ind Engn Hamadan Iran Amirkabir Univ Technol Dept Ind Engn Tehran Iran

This paper considers a bi-objective hybrid flowshop scheduling problems with fuzzy tasks' operation times, due dates and sequence-dependent setup times. To solve this problem, we propose a bi-level algorithm to minimize two criteria, namely make span, and sum of the earliness and tardiness, simultaneously. In the first level, the population will be decomposed into several sub-populations in parallel and each sub-population is designed for a scalar bi-objective. In the second level, non-dominant solutions obtained from sub-population bi-objective random key genetic algorithm (SBG) in the first level will be unified as one big population. In the second level, for improving the Pareto-front obtained by SBG, based on the search in Pareto space concept, a particle swarm optimization (PSO) is proposed. We use a defuzzification function to rank the Bell-shaped fuzzy numbers. The non-dominated sets obtained from each of levels and an algorithm presented previously in literature are compared. The computational results showed that PSO performs better than others and obtained superior results. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Fuzzy hybrid flowshop scheduling Bi-objective optimization Pareto optimal solution Particle swarm optimization parallel genetic algorithm Bell-shaped fuzzy number

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genetic algorithm for optimizing the nonlinear time alignment of automatic speech recognition systems

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 1996年第5期43卷 559-566页

作者： Kwong, S Chau, CW Halang, WA City University of Hong Kong Hong Kong China Hagen Germany

Dynamic time warping (DTW) is a nonlinear time-alignment technique for automatic speech recognition (ASR) systems, It had been widely used in many commercial and industrial products, ranging from electronic dailies/dictionaries to wireless voice digit dialers, DTW has the advantages of fast training and searching times, which makes it more popular than other available ASR techniques, However, there exist some limitations to DTW, such as the stringent rule on slope weighting, the nontrivial computation of the K-best paths, and the significant increase in computational time when the endpoint constraint is relaxed or the variations of the length of pattern increased, In this paper, a stochastic method called the genetic algorithm (GA), which is used to solve the nonlinear time alignment problem, is presented, Experimental results show that the GA has a better performance than the DTW, In addition, two derivatives of GA: the hybrid GA and the parallel GA are also presented.

关键词： dynamic time warping automatic speech recognition stochastic method genetic algorithm hybrid genetic algorithm parallel genetic algorithm

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Development of novel hybrid pre-separation/extractive reactive distillation processes for the separation of methanol/methyl acetate/ethyl acetate

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SEPARATION AND PURIFICATION TECHNOLOGY 2024年 348卷

作者： Zhu, Jiaxing Hao, Lin Zhu, Zhenxing Wei, Hongyuan Southwest Univ Sch Chem & Chem Engn Chongqing Peoples R China Tianjin Univ Sch Chem Engn & Technol Tianjin Univ Chem Proc Safety Lab Joint Green Separat & Chem Proc Safety Lab Tianjin Peoples R China Sinopec Res Inst Petr Proc Beijing 100083 Peoples R China

The treatment of waste streams is an important research topic for the sustainable development of chemical process. Recently, reactive distillation-based (RD) processes with ethylene oxide hydrolysis have been developed to separate water-containing azeotropic mixtures. To separate Serafimov's class 2.0-2b mixtures containing methanol/methyl acetate, a methyl acetate transesterification reaction between propylene glycol monomethyl ether is introduced to convert methyl acetate to methanol and coproduce produce propylene glycol monomethyl ether acetate as advanced solvents. Three-column reactive-extractive distillation (TCRED) and extractivereactive distillation (TCERD), and double-column pre-separation-reactive distillation (DCPSRD) processes, are proposed. Then, we use a parallel genetic algorithm to optimize processes to maximize total net revenue. The case study is methanol/methyl acetate/ethyl acetate. Though TCRED is not suitable due to the occurrence of ethyl acetate transesterification reactions, to compare economic performances of three RD-based processes, TCRED is regarded as a pseudo process. The total net revenue of three RD-based processes is relatively larger than (similar to 20 % increase) that of the conventional extractive distillation process. Compared with TCRED process, TCERD and DCPSRD can achieve $582336 and $1045995 increase in TNR, 27.43 % and 49.99 % TAC reduction, respectively. In summary, proposed RD-based processes are promising to separate Serafimov's class 2.0-2b mixtures containing methanol/methyl acetate, especially TCRED and DCPSRD.

关键词： Reactive distillation Extractive distillation Methyl acetate transesterification reaction parallel genetic algorithm Waste stream

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Guaranteeing the probability of success using repeated runs of genetic algorithm

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IMAGE AND VISION COMPUTING 2001年第8期19卷 551-560页

作者： Yuen, SY Fong, CK Lam, HS City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

Though genetic algorithm (GA) has found widespread application, there appears to be no guarantee of success or quantitative measure of the probability of success in a given application. This paper addresses this problem using the notion of repeatedly applying a GA. Several alternative interpretations of the algorithm are offered. The Q factor is introduced to characterize the efficacy of any GA. The repeated algorithm is applied to a six-degree object detection problem and experimental results are reported. A general methodology is given on the design of GA in a particular problem based on defining the maximum variation of a problem, using the training set to estimate the average probability of a single run to the desired level of statistical confidence, and using the testing set to verify the required performance. This paper paves the way for applying the GA to robust industrial applications for which the probability of convergence to the globally correct solution is required to be arbitrarily high. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： parallel genetic algorithm repeated run probability of success

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