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A new technique for solving the multi-objective optimization problem using hybrid approach

Mimoun YOUNES,Khodja FOUAD,Belabbes BAGDAD

《能源前沿（英文）》 2014年第8卷第4期页码 490-503 doi: 10.1007/s11708-014-0311-0

摘要： Energy efficiency, which consists of using less energy or improving the level of service to energy consumers, refers to an effective way to provide overall energy. But its increasing pressure on the energy sector to control greenhouse gases and to reduce CO emissions forced the power system operators to consider the emission problem as a consequential matter besides the economic problems. The economic power dispatch problem has, therefore, become a multi-objective optimization problem. Fuel cost, pollutant emissions, and system loss should be minimized simultaneously while satisfying certain system constraints. To achieve a good design with different solutions in a multi-objective optimization problem, fuel cost and pollutant emissions are converted into single optimization problem by introducing penalty factor. Now the power dispatch is formulated into a bi-objective optimization problem, two objectives with two algorithms, firefly algorithm for optimization the fuel cost, pollutant emissions and the real genetic algorithm for minimization of the transmission losses. In this paper the new approach (firefly algorithm-real genetic algorithm, FFA-RGA) has been applied to the standard IEEE 30-bus 6-generator. The effectiveness of the proposed approach is demonstrated by comparing its performance with other evolutionary multi-objective optimization algorithms. Simulation results show the validity and feasibility of the proposed method.

关键词： economic power dispatch (EPD) firefly algorithm (FFA) real genetic algorithm (RGA) hybrid method

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Optimal design of steel skeletal structures using the enhanced genetic algorithm methodology

Tugrul TALASLIOGLU

《结构与土木工程前沿（英文）》 2019年第13卷第4期页码 863-889 doi: 10.1007/s11709-019-0523-9

摘要： This study concerns with the design optimization of steel skeletal structures thereby utilizing both a real-life specification provisions and ready steel profiles named hot-rolled I sections. For this purpose, the enhanced genetic algorithm methodology named EGAwMP is utilized as an optimization tool. The evolutionary search mechanism of EGAwMP is constituted on the basis of generational genetic algorithm (GGA). The exploration capacity of EGAwMP is improved in a way of dividing an entire population into sub-populations and using of a radial basis neural network for dynamically adjustment of EGAwMP’s genetic operator parameters. In order to improve the exploitation capability of EGAwMP, the proposed neural network implementation is also utilized for prediction of more accurate design variables associating with a new design strategy, design codes of which are based on the provisions of LRFD_AISC V3 specification. EGAwMP is applied to determine the real-life ready steel profiles for the optimal design of skeletal structures with 105, 200, 444, and 942 members. EGAwMP accomplishes to increase the quality degrees of optimum designations Furthermore, the importance of using the real-life steel profiles and design codes is also demonstrated. Consequently, EGAwMP is suggested as a design optimization tool for the real-life steel skeletal structures.

关键词： design optimization genetic algorithm multiple populations neural network

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遗传算法在水泥矿山卡车运输调度系统中的应用

戴剑勇,杨仕教,古德生

《中国工程科学》 2006年第8卷第8期页码 77-80

摘要：

根据露天矿山运输调度系统的复杂性与非线性特性，建立了实时运输调度系统模型；运用遗传进化算法中的选择、交叉、变异、插入、迁移算子的寻优迭代计算，成功地解决了在开采工艺、产量、质量等多因素约束条件下的实时运输调度优化问题。并将其用于韶峰水泥原料矿山的生产运输调度系统，既降低了矿山运输成本，又协调了开采工艺、质量、产量之间的关系，取得了较好的效果。同时为矿山企业信息化建设和其他物流企业提供了重要的参考价值。

关键词：遗传算法运输调度系统运输成本

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退火-遗传算法寻优及其实现

王英

《中国工程科学》 2008年第10卷第7期页码 57-59

摘要：

分析了遗传算法及退火算法的优缺点，提出用退火算法改进遗传算法局部的最优值搜索效率低问题。退火算法与遗传算法融合后，使算法在寻优结果上更加迅速精确。通过水泥的配比工程实例，与单纯的遗传算法的结果进行对比，说明该方法是有效的。

关键词：遗传算法退火算法遗传算法改进

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Optimal design of steel portal frames based on genetic algorithms

CHEN Yue, HU Kai

《结构与土木工程前沿（英文）》 2008年第2卷第4期页码 318-322 doi: 10.1007/s11709-008-0055-1

摘要： As for the optimal design of steel portal frames, due to both the complexity of cross selections of beams and columns and the discreteness of design variables, it is difficult to obtain satisfactory results by traditional optimization. Based on a set of constraints of the Technical Specification for Light-weighted Steel Portal Frames of China, a genetic algorithm (GA) optimization program for portal frames, written in MATLAB code, was proposed in this paper. The graph user interface (GUI) is also developed for this optimal program, so that it can be used much more conveniently. Finally, some examples illustrate the effectiveness and efficiency of the genetic-algorithm-based optimal program.

关键词： satisfactory genetic-algorithm-based Technical Specification algorithm efficiency

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A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system usingenergy filter algorithm

Aeidapu MAHESH, Kanwarjit Singh SANDHU

《能源前沿（英文）》 2020年第14卷第1期页码 139-151 doi: 10.1007/s11708-017-0484-4

摘要： In this paper, the genetic algorithm (GA) is applied to optimize a grid connected solar photovoltaic (PV)-wind-battery hybrid system using a novel energy filter algorithm. The main objective of this paper is to minimize the total cost of the hybrid system, while maintaining its reliability. Along with the reliability constraint, some of the important parameters, such as full utilization of complementary nature of PV and wind systems, fluctuations of power injected into the grid and the battery’s state of charge (SOC), have also been considered for the effective sizing of the hybrid system. A novel energy filter algorithm for smoothing the power injected into the grid has been proposed. To validate the proposed method, a detailed case study has been conducted. The results of the case study for different cases, with and without employing the energy filter algorithm, have been presented to demonstrate the effectiveness of the proposed sizing strategy.

关键词： PV-wind-battery hybrid system size optimization genetic algorithm

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Hybrid genetic algorithm for bi-objective resource-constrained project scheduling

Fikri KUCUKSAYACIGIL, Gündüz ULUSOY

《工程管理前沿（英文）》 2020年第7卷第3期页码 426-446 doi: 10.1007/s42524-020-0100-x

摘要： In this study, we considered a bi-objective, multi-project, multi-mode resource-constrained project scheduling problem. We adopted three objective pairs as combinations of the net present value (NPV) as a financial performance measure with one of the time-based performance measures, namely, makespan ( ), mean completion time (MCT), and mean flow time (MFT) (i.e., min /max , min /max , and min /max ). We developed a hybrid non-dominated sorting genetic algorithm II (hybrid-NSGA-II) as a solution method by introducing a backward–forward pass (BFP) procedure and an injection procedure into NSGA-II. The BFP was proposed for new population generation and post-processing. Then, an injection procedure was introduced to increase diversity. The BFP and injection procedures led to improved objective functional values. The injection procedure generated a significantly high number of non-dominated solutions, thereby resulting in great diversity. An extensive computational study was performed. Results showed that hybrid-NSGA-II surpassed NSGA-II in terms of the performance metrics hypervolume, maximum spread, and the number of non-dominated solutions. Solutions were obtained for the objective pairs using hybrid-NSGA-II and three different test problem sets with specific properties. Further analysis was performed by employing cash balance, which was another financial performance measure of practical importance. Several managerial insights and extensions for further research were presented.

关键词： backward–forward scheduling hybrid bi-objective genetic algorithm injection procedure maximum cash balance multi-objective multi-project multi-mode resource-constrained project scheduling problem

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Evaluation of a novel Asymmetric Genetic Algorithm to optimize the structural design of 3D regular and

Mohammad Sadegh ES-HAGHI, Aydin SHISHEGARAN, Timon RABCZUK

《结构与土木工程前沿（英文）》 2020年第14卷第5期页码 1110-1130 doi: 10.1007/s11709-020-0643-2

摘要： We propose a new algorithm, named Asymmetric Genetic Algorithm (AGA), for solving optimization problems of steel frames. The AGA consists of a developed penalty function, which helps to find the best generation of the population. The objective function is to minimize the weight of the whole steel structure under the constraint of ultimate loads defined for structural steel buildings by the American Institute of Steel Construction (AISC). Design variables are the cross-sectional areas of elements (beams and columns) that are selected from the sets of side-flange shape steel sections provided by the AISC. The finite element method (FEM) is utilized for analyzing the behavior of steel frames. A 15-storey three-bay steel planar frame is optimized by AGA in this study, which was previously optimized by algorithms such as Particle Swarm Optimization (PSO), Particle Swarm Optimizer with Passive Congregation (PSOPC), Particle Swarm Ant Colony Optimization (HPSACO), Imperialist Competitive Algorithm (ICA), and Charged System Search (CSS). The results of AGA such as total weight of the structure and number of analyses are compared with the results of these algorithms. AGA performs better in comparison to these algorithms with respect to total weight and number of analyses. In addition, five numerical examples are optimized by AGA, Genetic Algorithm (GA), and optimization modules of SAP2000, and the results of them are compared. The results show that AGA can decrease the time of analyses, the number of analyses, and the total weight of the structure. AGA decreases the total weight of regular and irregular steel frame about 11.1% and 26.4% in comparing with the optimized results of SAP2000, respectively.

关键词： optimization steel frame Asymmetric Genetic Algorithm constraints of ultimate load constraints of serviceability limits penalty function

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Application of micro-genetic algorithm for calibration of kinetic parameters in HCCI engine combustion

HUANG Haozhong, SU Wanhua

《能源前沿（英文）》 2008年第2卷第1期页码 86-92 doi: 10.1007/s11708-008-0003-8

摘要： The micro-genetic algorithm (?GA) as a highly effective optimization method, is applied to calibrate to a newly developed reduced chemical kinetic model (40 species and 62 reactions) for the homogeneous charge compression ignition (HCCI) combustion of -heptane to improve its autoignition predictions for different engine operating conditions. The seven kinetic parameters of the calibrated model are determined using a combination of the Micro-Genetic Algorithm and the SENKIN program of CHEMKIN chemical kinetics software package. Simulation results show that the autoignition predictions of the calibrated model agree better with those of the detailed chemical kinetic model (544 species and 2 446 reactions) than the original model over the range of equivalence ratios from 0.1–1.3 and temperature from 300–3 000 K. The results of this study have demonstrated that the mGA is an effective tool to facilitate the calibration of a large number of kinetic parameters in a reduced kinetic model.

关键词： homogeneous different combustion autoignition compression

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Improved genetic algorithm and its application to determination of critical slip surface with arbitrary

LI Liang, CHI Shichun, LIN Gao, CHENG Yungming

《结构与土木工程前沿（英文）》 2008年第2卷第2期页码 145-150 doi: 10.1007/s11709-008-0016-8

摘要： In order to overcome the problem of being trapped by the local minima encountered in applying the simple genetic algorithm (GA) to search the critical slip surface of the slope, an improved procedure based on the harmony search algorithm is proposed. In the searching computation, the new solutions are obtained from the whole information of the current generation. The proposed method may be applied to calculate the minimum factors of safety of two complicated soil slopes. Comparison of the results with existing examples given by other authors has shown that the proposed method is feasible for stability analysis of soil slopes.

关键词： information algorithm Comparison generation feasible

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Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization

Mehdi BABAEI,Ebrahim SANAEI

《结构与土木工程前沿（英文）》 2016年第10卷第4期页码 472-480 doi: 10.1007/s11709-016-0368-4

摘要： In this article, multi-objective optimization of braced frames is investigated using a novel hybrid algorithm. Initially, the applied evolutionary algorithms, ant colony optimization (ACO) and genetic algorithm (GA) are reviewed, followed by developing the hybrid method. A dynamic hybridization of GA and ACO is proposed as a novel hybrid method which does not appear in the literature for optimal design of steel braced frames. Not only the cross section of the beams, columns and braces are considered to be the design variables, but also the topologies of the braces are taken into account as additional design variables. The hybrid algorithm explores the whole design space for optimum solutions. Weight and maximum displacement of the structure are employed as the objective functions for multi-objective optimal design. Subsequently, using the weighted sum method (WSM), the two objective problem are converted to a single objective optimization problem and the proposed hybrid genetic ant colony algorithm (HGAC) is developed for optimal design. Assuming different combination for weight coefficients, a trade-off between the two objectives are obtained in the numerical example section. To make the final decision easier for designers, related constraint is applied to obtain practical topologies. The achieved results show the capability of HGAC to find optimal topologies and sections for the elements.

关键词： multi-objective hybrid algorithm ant colony genetic algorithm displacement weighted sum method steel braced frames

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Fast forward kinematics algorithm for real-time and high-precision control of the 3-RPS parallel mechanism

Yue WANG, Jingjun YU, Xu PEI

《机械工程前沿（英文）》 2018年第13卷第3期页码 368-375 doi: 10.1007/s11465-018-0519-5

摘要：

A new forward kinematics algorithm for the mechanism of 3-RPS (R: Revolute; P: Prismatic; S: Spherical) parallel manipulators is proposed in this study. This algorithm is primarily based on the special geometric conditions of the 3-RPS parallel mechanism, and it eliminates the errors produced by parasitic motions to improve and ensure accuracy. Specifically, the errors can be less than 10^-6 . In this method, only the group of solutions that is consistent with the actual situation of the platform is obtained rapidly. This algorithm substantially improves calculation efficiency because the selected initial values are reasonable, and all the formulas in the calculation are analytical. This novel forward kinematics algorithm is well suited for real-time and high-precision control of the 3-RPS parallel mechanism.

关键词： 3-RPS parallel mechanism forward kinematics numerical algorithm parasitic motion

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combustion, performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting geneticalgorithm-II

Sunil Dhingra,Gian Bhushan,Kashyap Kumar Dubey

《机械工程前沿（英文）》 2014年第9卷第1期页码 81-94 doi: 10.1007/s11465-014-0287-9

摘要：

The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition (CI) engine using jatropha biodiesel. Response surface methodology based on Central composite design (CCD) is used to design the experiments. Mathematical models are developed for combustion parameters (Brake specific fuel consumption (BSFC) and peak cylinder pressure (P_max)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NO_x, unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, P_max), performance (BTE) and emission (CO, NO_x, HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, P_max, CO, NO_x, HC, smoke, a multi-objective optimization problem is formulated. Non-dominated sorting genetic algorithm-II is used in predicting the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine output and emission parameters depending upon their own requirements.

关键词： jatropha biodiesel fuel properties response surface methodology multi-objective optimization non-dominated sorting genetic algorithm-II

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基于蛙跳思想的量子编码遗传算法

许波,彭志平,余建平,柯文德

《中国工程科学》 2014年第16卷第3期页码 108-112

摘要：

量子门旋转相位、变异概率大小的确定，是目前制约量子遗传算法效率的两个主要问题。本文提出一种基于蛙跳思想的量子编码遗传算法（QRGA），该算法采用自适应的方式对量子旋转门旋转角进行调整，并基于模糊逻辑将蛙跳的步长进行量化以指导变异概率调整，保证进化的方向性和提高算法效率，对比实验结果表明算法可以避免陷入局部最优解，并能快速收敛到全局最优解，在运行时间和解的性能上都取得了较好的效果。

关键词：量子编码量子遗传算法蛙跳算法群体智能

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基于加速遗传算法的组合证券投资决策

王硕,唐小我,曾勇

《中国工程科学》 2002年第4卷第9期页码 59-62

摘要：

应用加速遗传算法解决组合证券投资决策问题，可以克服传统遗传算法的缺点：对搜索空间（优化变量空间）的大小变化适应能力差，计算量大，易出现早熟收敛，控制参数的设置技术无明确准则指导等，与已有结果相比，对协方差矩阵无正定性要求，目标函数可以推广到规模庞大，提高预测精度等优点。

关键词：加速遗传算法组合证券投资决策

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标题作者时间类型操作

A new technique for solving the multi-objective optimization problem using hybrid approach

Mimoun YOUNES,Khodja FOUAD,Belabbes BAGDAD

期刊论文

Optimal design of steel skeletal structures using the enhanced genetic algorithm methodology

Tugrul TALASLIOGLU

期刊论文

遗传算法在水泥矿山卡车运输调度系统中的应用

戴剑勇,杨仕教,古德生

期刊论文

退火-遗传算法寻优及其实现

王英

期刊论文

Optimal design of steel portal frames based on genetic algorithms

CHEN Yue, HU Kai

期刊论文

A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system usingenergy filter algorithm

Aeidapu MAHESH, Kanwarjit Singh SANDHU

期刊论文

Hybrid genetic algorithm for bi-objective resource-constrained project scheduling

Fikri KUCUKSAYACIGIL, Gündüz ULUSOY

期刊论文

Evaluation of a novel Asymmetric Genetic Algorithm to optimize the structural design of 3D regular and

Mohammad Sadegh ES-HAGHI, Aydin SHISHEGARAN, Timon RABCZUK

期刊论文

Application of micro-genetic algorithm for calibration of kinetic parameters in HCCI engine combustion

HUANG Haozhong, SU Wanhua

期刊论文

Improved genetic algorithm and its application to determination of critical slip surface with arbitrary

LI Liang, CHI Shichun, LIN Gao, CHENG Yungming

期刊论文

Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization

Mehdi BABAEI,Ebrahim SANAEI

期刊论文

Fast forward kinematics algorithm for real-time and high-precision control of the 3-RPS parallel mechanism

Yue WANG, Jingjun YU, Xu PEI

期刊论文

combustion, performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting geneticalgorithm-II

Sunil Dhingra,Gian Bhushan,Kashyap Kumar Dubey

期刊论文

基于蛙跳思想的量子编码遗传算法

许波,彭志平,余建平,柯文德

期刊论文

基于加速遗传算法的组合证券投资决策

王硕,唐小我,曾勇

期刊论文

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