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multi-population genetic algorithm and Cuckoo Search Hybrid Technique for Parameter Identification of Fermentation Process Models

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PROCESSES 2023年第2期11卷 427页

作者： Angelova, Maria Roeva, Olympia Vassilev, Peter Pencheva, Tania Bulgarian Acad Sci Inst Biophys & Biomed Engn Sofia 1113 Bulgaria Bulgarian Acad Sci Inst Robot Sofia 1113 Bulgaria

In this paper, a new hybrid MpGA-CS is elaborated between multi-population genetic algorithm (MpGA) and cuckoo search (CS) metaheuristic. Developed MpGA-CS has been adapted and tested consequently for modelling of bacteria and yeast fermentation processes (FP), due to their great impact on different industrial areas. In parallel, classic MpGA, classic CS, and a new hybrid MpGA-CS have been separately applied for parameter identification of E. coli and S. cerevisiae FP models. For completeness, the newly elaborated MpGA-CS has been compared with two additional nature-inspired algorithms;namely, artificial bee colony algorithm (ABC) and water cycle algorithm (WCA). The comparison has been carried out based on numerical and statistical tests, such as ANOVA, Friedman, and Wilcoxon tests. The obtained results show that the hybrid metaheuristic MpGA-CS, presented herein for the first time, has been distinguished as the most reliable among the investigated algorithms to further save computational resources.

关键词： multi-population genetic algorithm cuckoo search hybrid technique parameter identification fed-batch fermentation models E coli S cerevisiae

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Injection molding optimization with weld line design constraint using distributed multi-population genetic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2011年第1-4期52卷 131-141页

作者： Wu, Chun-Yin Ku, Chih-Chiang Pai, Hsin-Yi Tatung Univ Dept Mech Engn Taipei Taiwan Micro Star Int Co Ltd Res Ctr Res & Dev Dept 3 Taipei Taiwan

Weld lines not only detract from an injection-molded part's surface quality, but also significantly reduce its mechanical strength. It is not always easy to completely eliminate weld lines by simply adjusting the relevant injection mold design or the molding conditions. One solution is to prevent the weld lines from forming in regions that are structurally or aesthetically sensitive. The influence of weld lines on the quality of injection-molded parts cannot be overlooked and weld lines should be regarded as an important design constraint, especially for parts with aesthetic concerns. Since precisely predicting the number of weld lines and their positions and lengths is difficult without executing simulation routines, especially when part geometry is considered a design variable, this study adopts an enhanced genetic algorithm, referred to as distributed multi-population genetic algorithm (DMPGA), combining an optimization algorithm and commercial MoldFlow software with a dominance-based constraint-handling technique and a master-slave distributed architecture. MoldFlow obtains relevant data regarding warpage and weld lines and evaluates the corresponding designs. The dominance-based constraint-handling technique handles the weld line design constraint without needing additional penalty factors. Finally, the master-slave distributed architecture reduces the formidable computational time required for injection molding optimization. To illustrate the high viability of DMPGA, this study provides an outer frame of a digital photo frame as an optimization example. The results of this study show that DMPGA cannot only effectively decrease maximum part warpage without violating the weld line constraint, but also conquer hurdles attributed to constraint handling and computational demand.

关键词： Injection molding optimization multi-population genetic algorithm Moldflow simulation Distributed parallel computing

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An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field

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INFORMATION SCIENCES 2018年 453卷 227-238页

作者： Bai, Xiaoshan Yan, Weisheng Ge, Shuzhi Sam Cao, Ming Northwestern Polytech Univ Sch Marine Sci & Technol 127 West Youyi Rd Xian 710072 Shaanxi Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore Univ Groningen Fac Sci & Engn NL-9747 AG Groningen Netherlands

This paper investigates the task assignment problem for a team of autonomous aerial/marine vehicles driven by constant thrust and maneuvering in a planar lateral drift field. The aim is to minimize the total traveling time in order to guide the vehicles to deliver a number of customized sensors to a set of target points with different sensor demands in the drift field. To solve the problem, we consider together navigation strategies and target assignment algorithms;the former minimizes the traveling time between two given locations in the drift field and the latter allocates a sequence of target locations to each vehicle. We first consider the effect of the weight of the carried sensors on the speed of each vehicle, and construct a sufficient condition to guarantee that the whole operation environment is reachable for the vehicles. Then from optimal control principles, time optimal path planning is carried out to navigate each vehicle from an initial position to its given target location. Most importantly, to assign the targets to the vehicles, we combine the virtual coding strategy, multiple offspring method, intermarriage crossover strategy, and the tabu search mechanism to obtain a co-evolutionary multi-population genetic algorithm, short-named CMGA. Simulations on sensor delivery scenarios in both fixed and time-varying drift fields are shown to highlight the satisfying performances of the proposed approach against popular greedy algorithms. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Task assignment Time-optimal path planning Drift field Autonomous vehicles multi-population genetic algorithm

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Pressure-driven Background Leakage Models and their Application for Leak Localization Using a multi-population genetic algorithm

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WATER RESOURCES MANAGEMENT 2023年第1期37卷 359-373页

作者： Guan, Yihong Lv, Mou Dong, Shen Qingdao Univ Technol Sch Environm & Municipal Engn Qingdao 266525 Shandong Peoples R China

Model-based techniques can accurately locate the vicinity of leak localization. However, in the traditional hydraulic leakage model (THLM), the nodal flow is composed of the actual water consumption and the background leakage calculated in equal proportion, without considering the influence of pressure on the background leakage. Therefore, in this research, the parameter alpha is obtained through a pipe network experiment. According to the relationship between the background leakage and the pressure, the parameter beta is calculated with a nonlinear genetic algorithm (NGA). The emission coefficient C is obtained based on the length of the pipeline, and then the pressure-driven background leakage model (PDBLM) is built to detect the leak localization using a multi-population genetic algorithm (MPGA). Through the simulation results under three working conditions, it is concluded that the PDBLM model is closer to the operation of the actual pipe network than the THLM model. Additionally, the PDBLM-based Inverse Problem Leak Localization Model can find the actual leakage point more accurately, improve leakage detection efficiency, and reduce water loss.

关键词： Leak localization Water supply network Pressure-driven background leakage model multi-population genetic algorithm Nonlinear genetic algorithm

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Application of improved multi-population genetic algorithm in structural optimization of automotive electrical equipment

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年 1-9页

作者： Sun, Longjie Xian Kedagaoxin Univ Xian 710109 Peoples R China

Although pure electric vehicles have the advantages of environmental protection and energy conservation, due to the constraints of power lithium-ion battery technology, they have a relatively short driving range and face problems such as high battery costs and low charging efficiency. Automotive electrical equipment affects the performance and safety of automobiles. In order to improve the efficiency and reliability of automotive electrical equipment, structural optimization is necessary. This article aims to improve the search ability and convergence speed of the algorithm by optimizing its parameters and introducing new genetic operations, in order to obtain a more optimal structure of automotive electrical equipment. Firstly, the basic principle and process of multi-population genetic algorithm were studied, and its problems in optimizing the structure of automotive electrical equipment were analyzed. Then, based on the characteristics of the problem, improvements were made to the multi-population genetic algorithm to improve its search ability. Through experimental verification, the improved multi-population genetic algorithm has achieved significant results in optimizing the structure of automotive electrical equipment. Compared with traditional algorithms, the improved algorithm has significantly improved its search ability and convergence speed. Through the optimized algorithm, a better structure of automotive electrical equipment was obtained, with higher efficiency and reliability.

关键词： multi-population genetic algorithm Electric vehicles Electrical equipment Structural optimization

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Derived multi-population genetic algorithm for Adaptive Fuzzy C-Means Clustering

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NEURAL PROCESSING LETTERS 2023年第3期55卷 2023-2047页

作者： Ding, Weiping Feng, Zhihao Andreu-Perez, Javier Pedrycz, Witold Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Univ Essex Ctr Computat Intelligence Sch Comp Sci & Elect Engn Colchester Essex England Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada

Fuzzy C-Means (FCM) is a common data analysis method, but the clustering effect of this algorithm is easily affected by the initial clustering centers. Currently, scholars often use the multiple population genetic algorithm (MPGA) to optimize the clustering centers, but the MPGA has insufficient global search ability and lacks self-adaptability, is prone to premature convergence, and has poor initial clustering centers. Therefore, this paper proposes an adaptive FCM clustering algorithm DMGA-FCM based on a derivative multiple population genetic algorithm (DMGA). In DMGA-FCM algorithm, firstly, the derivative operator, which is proposed for the first time in this paper, performs derivative operations on initialized populations to improve the algorithm's searchability and deal with the lack of inter-population search ability. Secondly, the adaptive probability fuzzy control operator is used to dynamically adjust the genetic probability to improve the adaptability of the algorithm, which in turn enhances the global merit-seeking ability of the DMGA algorithm and avoids premature convergence. Finally, the initial clustering center of FCM algorithm is optimized with DMGA to enhance the clustering effect of the algorithm. The analysis of simulation experiments and MRI brain map application results show that the DMGA-FCM algorithm can obtain a better clustering effect of medical data and image clustering segmentation effect compared with other related FCM algorithms.

关键词： Fuzzy C-means clustering Clustering centers multi-population genetic algorithm Derivative operator Fuzzy control

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Variable Universe Fuzzy Control of Adjustable Hydraulic Torque Converter Based on multi-population genetic algorithm

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IEEE ACCESS 2019年 7卷 29236-29244页

作者： Li Mingxue Yang Guolai Li Xiaoqing Bai Guixiang Lanzhou Univ Technol Sch Energy & Power Engn Lanzhou 730050 Gansu Peoples R China Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou 730070 Gansu Peoples R China

In order to optimize the control method of guide vane opening in an adjustable hydraulic torque converter, this paper presents a variable universe fuzzy control method for servo system of the adjustable hydraulic torque converter based on the multi-population genetic algorithm. This control method got a kind of adaptive control as optimizing proportional exponential contraction-expansion factors of the current loop with a genetic algorithm, thus transforming the fuzzy variable universe of the current loop. In this method, we designed a variable universe adaptive fuzzy controller with S-function on an adjustable hydraulic torque converter servo system and conducted some simulation experiments. The simulation results showed that the variable universe control based on genetic algorithm improved the anti-interference and robustness of adjustable hydraulic torque converter servo system compared with control methods in the previous literature. This control method could be a desirable way to adjust guide vane opening with the properties of better accuracy, rapidity, and reliability. Thus, the control method we designed will provide an effective, fast, and stable control strategy for the uncertain system with the properties of nonlinearity, strong disturbance, and uncertain time-varying.

关键词： Adjustable hydraulic torque converter variable universe fuzzy control multi-population genetic algorithm guide vane opening servo motor

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On-demand optimize design of sound-absorbing porous material based on multi-population genetic algorithm

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E-POLYMERS 2020年第1期20卷 122-132页

作者： Wang, Yonghua Liu, Shengfu Wu, Haiquan Zhang, Chengchun Xu, Jinkai Yu, Huadong Changchun Univ Sci & Technol Minist Educ Key Lab Cross Scale Micro & Nano Mfg Changchun 130022 Peoples R China Jilin Univ Key Lab Bion Engn Minist Educ Changchun 130022 Jilin Peoples R China COMAC Shanghai Aircraft Design & Res Inst Shanghai 200120 Peoples R China

Porous material (PM) shows good sound absorption performance, however, the sound absorbing property of PM with different parameters are greatly different. In order to match the most suitable absorbing materials with the most satisfactory sound-absorbing performance according to the noise spectrum in different practical applications, multi-population genetic algorithm is used in this paper to optimize the parameters of porous sound absorbing structures that are commonly used according to the actual demand of noise reduction and experimental verification. The results shows that the optimization results of multi-population genetic algorithm are obviously better than the standard genetic algorithm in terms of sound absorption performance and sound absorption bandwidth. The average acoustic absorption coefficient of PM can reach above 0.6 in the range of medium frequency, and over 0.8 in the range of high frequency through optimization design. At a mid-to-high frequency environment, the PM has a better sound absorption effect and a wider frequency band than that of micro-perforated plate. However, it has a poor sound absorption effect at low frequency. So it is necessary to select suitable sound absorption material according to the actual noise spectrum.

关键词： multi-population genetic algorithm parameter optimize porous material

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A novel method based on multi-population genetic algorithm for CCHP-GSHP coupling system optimization

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ENERGY CONVERSION AND MANAGEMENT 2015年第Nov.期105卷 1138-1148页

作者： Zeng, Rong Li, Hongqiang Liu, Lifang Zhang, Xiaofeng Zhang, Guoqiang Hunan Univ Coll Civil Engn Changsha 410082 Hunan Peoples R China

A hybrid system, which couples Combined Cooling, Heating and Power system (CCHP) and Ground Source Heat Pump system (GSHP), simply or rather to say the CCHP-GSHP coupling system, or hybrid system, may efficiently solve the problems when a CCHP or a GSHP operates independently and obtain better system performance. This paper proposes a novel method to optimize the capacity and operation strategy for CCHP-GSHP coupling system. In this method, primary energy saving ratio, CO2 emission reduction ratio and annual total cost saving ratio are optimization goals;the rated thermal capacity of gas engine in CCHP, the heating/cooling provided by GSHP system to total heating/cooling load ratio and the critical value to determine whether run the gas engine are variables. multi-population genetic algorithm (MPGA) is selected to solve the optimal model. Furthermore, a case study based on a hotel building is presented and studied to verify the effectiveness of this optimizing method and the corresponding algorithm to solve the model. The results in the case study show that the primary energy saving ratio, carbon dioxide emission reduction ratio, annual total cost saving ratio, comprehensive performance of the hybrid system comparing with the separated generation system. are 26.10%, 35.02% 15.13% and 25.42%, respectively. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Combined cooling, heating and power Ground source heat pump Comprehensive performance multi-population genetic algorithm Optimal method Annual saving ratio

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A hybrid multi-population genetic algorithm for the dynamic facility layout problem

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APPLIED SOFT COMPUTING 2014年第0期24卷 457-469页

作者： Pourvaziri, Hani Naderi, B. Islamic Azad Univ Dept Ind & Mech Engn Qazvin Iran Univ Kharazmi Fac Engn Dept Ind Engn Karaj Iran

Due to inherent complexity of the dynamic facility layout problem, it has always been a challenging issue to develop a solution algorithm for this problem. For more than one decade, many researchers have proposed different algorithms for this problem. After reviewing the shortcomings of these algorithms, we realize that the performance can be further improved by a more intelligent search. This paper develops an effective novel hybrid multi-population genetic algorithm. Using a proposed heuristic procedure, we separate solution space into different parts and each subpopulation represents a separate part. This assures the diversity of the algorithm. Moreover, to intensify the search more and more, a powerful local search mechanism based on simulated annealing is developed. Unlike the available genetic operators previously proposed for this problem, we design the operators so as to search only the feasible space;thus, we save computational time by avoiding infeasible space. To evaluate the algorithm, we comprehensively discuss the parameter tuning of the algorithms by Taguchi method. The perfectly tuned algorithm is then compared with 11 available algorithms in the literature using well-known set of benchmark instances. Different analyses conducted on the results, show that the proposed algorithm enjoys the superiority and outperformance over the other algorithms. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Dynamic facility layout problem multi-population genetic algorithm Heuristics

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