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A hybrid multi-objective genetic algorithm based on the ELECTRE method for a capacitated flexible job shop scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第1-4期77卷 51-66页

作者： Rohaninejad, Mohamad Kheirkhah, Amirsaman Fattahi, Parviz Vahedi-Nouri, Behdin Bu Ali Sina Univ Dept Ind Engn Hamadan Iran

In this paper, a multi-objective flexible job shop scheduling problem with machines capacity constraints is studied. Minimizing the makespan and overtime costs of machines are considered as two objectives for evaluating solutions. First, a new nonlinear integer programming model is presented to formulate the problem. Inasmuch as this problem is well-known as a NP-hard problem, a hybrid metaheuristic algorithm (CFJSP II) is developed to overcome its complexity. Regarding to the solution space of the problem, for assigning and sequencing operations, a multi-objective genetic algorithm based on the ELECTRE method is presented. Also, a powerful heuristic approach to tradeoff the objective functions is developed. Finally, the proposed algorithm is compared with some well-known multi-objective algorithms such as NSGAII, SPEA2, and VEGA. Regarding to the computational results, it is clear that the proposed algorithm has a better performance especially in the closeness of the solutions to the Pareto optimal front.

关键词： Scheduling Flexible job shop multi-objective genetic algorithm ELECTRE method Pareto optimal front

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Performance Optimization of a STATCOM based on Cascaded multi-Level Converter Topology Using multi-objective genetic algorithm 23

Performance Optimization of a STATCOM based on Cascaded Mult...

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23rd Iranian Conference on Electrical Engineering

作者： Tousi, S. M. Reza Aznavi, Sima Bu Ali Sina Univ Fac Engn Dept Elect Engn Hamadan Iran

ISBN: (纸本)9781479919710

In this paper, a comprehensive study in component sizing of a cascaded multi-level inverter based static synchronous compensator (STATCOM) is presented. multi-objective genetic algorithm (MOGA) is utilized to optimize the performance of STATCOM. "Equivalent dynamic capacitance" concept is considered in order to simplify the complex structure of the multi-level inverter based STATCOM. Cascaded multi-level inverter parameters such as DC capacitance, initial capacitor charge, switching angles and also power system parameters including buffer inductance, transformer ratio and transformer inductance are to be found using optimization algorithm. In contrast with previous researches which have tried to optimize the objectives independently, in this paper the aim of using MOGA is optimizing losses, total harmonic distortion (THD) and maximum DC voltage ripple simultaneously, providing designing constraints with respect to the "equivalent dynamic capacitance" concept. The results show that better performance of STATCOM can be achieved via a thorough analysis by considering all parameters of STATCOM and components of power system as decision variables of a multi-objective optimization problem.

关键词： Static Synchronous Compensator (STATCOM) Cascaded multi-Level Converter Performance Optimization multi-objective genetic algorithm Equivalent Dynamic Capacitance

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multi-objective genetic algorithm for voltage stability enhancement using rescheduling and FACTS devices

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AIN SHAMS ENGINEERING JOURNAL 2014年第3期5卷 789-801页

作者： Roselyn, J. Preetha Devaraj, D. Dash, Subhransu Sekhar SRM Univ Chennai Tamil Nadu India Kalasalingam Univ Srivilliputhur India

This paper presents the application of multi-objective genetic algorithm to solve the Voltage Stability Constrained Optimal Power Flow (VSCOPF) problem. Two different control strategies are proposed to improve voltage stability of the system under different operating conditions. The first approach is based on the corrective control in contingency state with minimization of voltage stability index and real power control variable adjustments as objectives. The second approach involves optimal placement and sizing of multi-type FACTS devices, Static VAR Compensator and Thyristor Controlled Series Capacitor along with generator rescheduling for minimization of voltage stability index and investment cost of FACTS devices. A fuzzy based approach is employed to get the best compromise solution from the trade off curve to aid the decision maker. The effectiveness of the proposed VSCOPF problem is demonstrated on two typical systems, IEEE 30-bus and IEEE 57 bus test systems. (C) 2014 Production and hosting by Elsevier B.V. on behalf of Ain Shams University.

关键词： Generation rescheduling Voltage stability multi-objective genetic algorithm Fuzzy decision making FACTS

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multi-objective optimization of a non-uniform sinusoidal mini-channel heat sink by coupling genetic algorithm and CFD model

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APPLIED THERMAL ENGINEERING 2024年第PartA期257卷

作者： Ge, Ya Yin, Weixing Lin, Yousheng He, Kui He, Qing Huang, Si-Min Dongguan Univ Technol Engn Res Ctr Distributed Energy Syst Guangdong Prov Key Lab Distributed Energy Syst Dongguan 523808 Peoples R China

Wavy mini-channel heat sinks (MCHS) can disrupt the development of the thermal boundary layer, resulting in superior performance compared to straight MCHS. For complete utilization of the coolant, this study proposed and optimized a non-uniform sinusoidal MCHS with varying wavelength or varying amplitude along the flow direction. The optimization for five design variables was accomplished through a multi-objective genetic algorithm, where the thermal resistance B or the pressure drop gyp p were defined as two objectives. The computational fluid dynamics (CFD) software was employed to solve all direct problems encountered during the evolutionary process. Compared to the straight MCHS with different channel widths, the optimal designs achieved reductions of 27.74 % in B or 59.51 % in gy p . Simultaneously, all the optimal values of the wavelength ratio and amplitude ratio indicate that enhancing the heat transfer performance downstream is more efficient. It was observed that among the five design variables, the number of wave cycles is the most relevant parameter with a Spearman's correlation up to 0.983. Subsequently, a multiple-criteria decision-making approach was employed to ascertain the best compromise solution to balance the two objectives. Although the B of the best compromise solution could be higher by 38.57 % than that of the lowest B solution, the gyp p presents a more substantial reduction of 92.45 % after the trade-off. Compared to the straight MCHS with the same gy p , the best compromise solution with varying wavelength reduces the B and the standard temperature deviation by 26.20 % and 76.98 % respectively, while lowering the average base temperature by 3.07 K. Therefore, the optimal non-uniform wavy MCHS, along with the optimization approach presented, is meaningful for practical applications.

关键词： Heat sink Non-uniform wavy channel multi-objective genetic algorithm multi-criteria decision-making Heat transfer enhancement

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Machine learning and genetic algorithm as tools for single and multi-objective shape optimization of micromixers with Cantor fractal structure

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BRAZILIAN JOURNAL OF CHEMICAL ENGINEERING 2024年第4期41卷 1327-1344页

作者： Cao, Jingzhi Chen, Xueye Ludong Univ Sch Chem & Mat Sci 186 Middle Hongqi Rd Yantai 264025 Shandong Peoples R China Ludong Univ Coll Transportat Yantai 264025 Shandong Peoples R China

Shape optimization of micromixers with Cantor fractal structures on the top and bottom walls has been conducted using a combination of three-dimensional Navier-Stokes analysis, Machine Learning (ML), and multi-objective genetic algorithm (MOGA) for both single and multi-objective optimization objectives. Numerical analysis has been systematically conducted employing two distinct working fluids, namely water and ethanol, while operating at a Reynolds number (Re) of unity (Re = 1). The optimization endeavor has been executed by manipulating two design variables that pertain to dimensionless geometric parameters. The single-objective optimization is centered on the selection of mixing effectiveness as the primary objective function, whereas the multi-objective optimization entails the consideration of two objective functions, specifically, the pressure drop and the mixing index at the exit. The Latin hypercube sampling (LHS) method is employed as an experimental design technique to systematically explore the parameter space within the design domain. It is utilized for the purpose of strategically selecting design points within this domain. Additionally, surrogate models for the objective functions are established through the application of ML. In the context of single-objective optimization, the Sequential Linear Programming (SLP) method is employed to iteratively derive the optimal objective function. Meanwhile, for multi-objective optimization, the genetic algorithm (GA) is applied to delineate the Pareto optimal frontier (POF) of the micromixer. Subsequently, K-means clustering is utilized as a technique for the purpose of classifying the optimized outcomes, followed by the selection of representative points within the solution space. In the context of single-objective optimization, it is observed that the micromixer featuring a Cantor fractal structure exhibits a noteworthy enhancement in mixing efficiency, surpassing that of the reference design (RD) by a substant

关键词： multi-objective genetic algorithm Machine learning Pareto optimal front Single and multi-objective shape optimization Cantor fractal structure

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Optimal placement of piezoelectric actuators and sensors on a smart beam and a smart plate using multi-objective genetic algorithm

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SMART STRUCTURES AND SYSTEMS 2015年第4期15卷 1041-1062页

作者： Nestorovic, Tamara Trajkov, Miroslav Garmabi, Seyedmehdi Ruhr Univ Bochum Mech Adapt Syst D-44801 Bochum Germany

In this paper a method of fmding optimal positions for piezoelectric actuators and sensors on different structures is presented. The genetic algorithm and multi-objective genetic algorithm are selected for optimization and H-infinity norm is defined as a cost function for the optimization process. To optimize the placement concerning the selected modes simultaneously, the multi-objective genetic algorithm is used. The optimization is investigated for two different structures: a cantilever beam and a simply supported plate. Vibrating structures are controlled in a closed loop with feedback gains, which are obtained using optimal LQ control strategy. Finally, output of a structure with optimized placement is compared with the output of the structure with an arbitrary, non-optimal placement of piezoelectric patches.

关键词： optimal location smart structures H-infinity norm genetic algorithm multi-objective genetic algorithm

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Stochastic quality-cost optimization system hybridizing multi-objective genetic algorithm and quality function deployment

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JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 2015年第4期21卷 407-422页

作者： Lim, Tae-Kyung Jang, Won-Suk Choi, Jae-ho Lee, Dong-Eun Kyungpook Natl Univ Sch Architecture & Civil Engn Taegu 702701 South Korea Yeungnam Univ Dept Civil Engn Gyeongsa Si 712749 Gyeongsangbuk D South Korea Dong A Univ Dept Civil Engn Pusan 604714 South Korea Kyungpook Natl Univ Sch Architecture & Civil Engn Taegu 702701 South Korea

This paper introduces an automated tool, the stochastic quality-cost optimization (SQCO) system, that hybridizes multi-objective genetic algorithm (MOGA) and Quality Function Deployment (QFD). The system identifies the optimal trade-off between a construction owner's satisfaction and a contractor's satisfaction. It is important to reconcile the project participants' conflicting interests because the construction owner aims to maximize the quality of construction while the contractor aims to minimize the cost of construction. MOGA is used to optimize resource allocation when owner satisfaction and contractor satisfaction are pursued at the same time under a limited budget. multi-objective optimization is integrated with simulation to effectively deal with the uncertainties of the QFD input and the variability of the QFD output. This study is of value to practitioners because SQCO allows for the establishment of a quality plan that satisfies all of the multi project participants. The study is also of relevance to researchers in that it allows researchers to expeditiously identify an optimal design alternative of construction methods and operations. A test case implemented with a curtain-wall unit verifies the usability and validity of the system in practice.

关键词： quality multi-objective genetic algorithm optimization Quality Function Deployment (QFD) curtain-wall building envelope cost

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Augmented genetic algorithm v3 for multi-objective PDN Decap Optimization

Augmented Genetic Algorithm v3 for Multi-Objective PDN Decap...

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Joint International Symposium on Electromagnetic Compatibility, Signal & Power Integrity: EMC Japan/AsiaPacific International Symposium on Electromagnetic Compatibility (EMC/APEMC)

作者： Manoharan, Haran Juang, Jack Wang, Hanfeng Pan, Jingnan Gao, Xu Qiu, Kelvin Hwang, Chulsoon Missouri Univ Sci & Technol EMC Lab Rolla MO 65409 USA Google Inc Mountain View CA USA

ISBN: (纸本)9784885523472;9798350349498

This paper proposes a novel approach for generating the initial population, termed disproportionate initialization, to enhance the genetic algorithm's efficiency in optimizing the number of decaps. This method is specifically designed to accelerate convergence, improving the algorithm's ability to find optimal solutions. The algorithm is improved to simultaneously reduce the number of decoupling capacitors and achieve the lowest cost, aiming to identify the global minima for comprehensive PDN optimization.

关键词： multi-objective genetic algorithm disproportionate initial population population diversity decap optimization

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Optimization of Combined Scroll Profile Based on multi-objective genetic algorithm

Optimization of Combined Scroll Profile Based on Multi-objec...

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International Conference on Information Sciences,Machinery,Materials and Energy（ICISMME 2015）

作者： Tao Liu Fuyong Hou Lanzhou University of Technology

This In the design of scroll compressor,in order to improve the performance of the combined profile by involute of circle and high order curve,the mathematical model was established which takes the stroke volume and area utilization coefficient as the objective *** optimize it by the MATLAB genetic algorithm toolbox and have a contrast of the original parameters with the optimized *** optimal result shows that the result after optimization has higher stroke volume and area utilization coefficient.

关键词： combined profile stroke volume area utilization coefficient multi-objective genetic algorithm

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multiple sequence alignment with affine gap by using multi-objective genetic algorithm

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014年第1期114卷 38-49页

作者： Kaya, Mehmet Sarhan, Abdullah Alhajj, Reda Firat Univ Dept Comp Engn TR-23119 Elazig Turkey Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada Global Univ Dept Comp Sci Beirut Lebanon

multiple sequence alignment is of central importance to bioinformatics and computational biology. Although a large number of algorithms for computing a multiple sequence alignment have been designed, the efficient computation of highly accurate and statistically significant multiple alignments is still a challenge. In this paper, we propose an efficient method by using multi-objective genetic algorithm (MSAGMOGA) to discover optimal alignments with affine gap in multiple sequence data. The main advantage of our approach is that a large number of tradeoff (i. e., non-dominated) alignments can be obtained by a single run with respect to conflicting objectives: affine gap penalty minimization and similarity and support maximization. To the best of our knowledge, this is the first effort with three objectives in this direction. The proposed method can be applied to any data set with a sequential character. Furthermore, it allows any choice of similarity measures for finding alignments. By analyzing the obtained optimal alignments, the decision maker can understand the tradeoff between the objectives. We compared our method with the three well known multiple sequence alignment methods, MUSCLE, SAGA and MSA-GA. As the first of them is a progressive method, and the other two are based on evolutionary algorithms. Experiments on the BAliBASE 2.0 database were conducted and the results confirm that MSAGMOGA obtains the results with better accuracy statistical significance compared with the three well-known methods in aligning multiple sequence alignment with affine gap. The proposed method also finds solutions faster than the other evolutionary approaches mentioned above. (C) 2014 Elsevier Ireland Ltd. All rights reserved.

关键词： multiple sequence alignment multi-objective genetic algorithm Bioinformatics

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