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Integration of a fuzzy neural network and multi-objective genetic algorithm for optimisation of BLU light guide plate injection moulding parameters

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INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY 2012年第1-4期45卷 83-95页

作者： Wang, Shen-Tsu Natl Pingtung Inst Commerce Commerce Automat & Management Dept Pingtung 900 Taiwan

Backlight module unit (BLU) is a key component of LCD, and thinning of the BLU light guide plate is the focus of development. Most light guide plates are manufactured using the injection moulding process. The experimental design in this study is the most common research approach applied in the setting of light guide plate parameters. However, the parameter interaction and system non-linearity can easily result in prediction errors of parameters. Meanwhile, since the optimal moulding conditions have not been achieved, the best effects of display illumination and brightness cannot be achieved. Hence, this study first applied the grey relational sorting method to determine the major control factors influencing the BLU light guide plate, and then built the approximate model of a fuzzy neural network, according to experimental planning samples, before using the multi-objective genetic algorithm to determine the optimal manufacturing parameter design. The results suggested that the process capability index C-pk value of the light guide plate X-axis size is improved from 0.43 to 1.56, and has stabilised product quality and enhanced process capability. This study proved that the prediction model of process optimisation, as obtained by using this research method, is effective.

关键词： fuzzy neural network multi-objective genetic algorithm process capability index BLU light guide plate

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multi-objective optimization design for the blade angles of hydraulic torque converter with adjustable pump

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2020年第13期234卷 2523-2536页

作者： Zheng, Zhi-Ying Liu, Quan-Zhong Deng, Yong-Kang Li, Biao Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Peoples R China

To improve the efficiency of a hydraulic torque converter with adjustable pump at low load and thus increase the operation scope of high efficiency, multi-objective optimization design is carried out for the blade angles by incorporating three-dimensional steady computational fluid dynamics numerical simulation, design of experiments, Kriging surrogate model and multi-objective genetic algorithm. The results show that the angle of blade trailing edge in first-stage stator is the main influencing factor of the efficiency of hydraulic torque converter with adjustable pump. All the peak efficiencies of hydraulic torque converter with adjustable pump at three openings of the pump are improved after optimization, and the increased extent increases with decreasing opening of the pump. The operation scope of high efficiency consequently increases from 2.46 to 2.67. Besides, the improvement for the efficiency of hydraulic torque converter with adjustable pump is achieved by increasing the efficiency of the pump. The increase of angle of blade trailing edge in first-stage stator and the decrease of angle of blade leading edge in second-stage turbine after optimization induce the positive angle of attack at the inlet of second-stage turbine, thus realizing the performance optimization of hydraulic torque converter with adjustable pump. This also explains the increased proportion of the torque of second-stage turbine at larger speed ratios after optimization and the fact that the angle of blade trailing edge in first-stage stator is the main influencing factor of the efficiency of hydraulic torque converter with adjustable pump. The established multi-objective optimization method provides a reference solution for the optimization design of blade angles and for the improvement of integrated efficiency of hydraulic torque converter.

关键词： Hydraulic torque converter adjustable pump design of experiments Kriging surrogate model multi-objective genetic algorithm CFD numerical simulation

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Sounding rocket SS-520 as a CubeSat launch vehicle

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ACTA ASTRONAUTICA 2020年 170卷 206-223页

作者： Ito, T. Yamamoto, T. Nakamura, T. Habu, H. Ohtsuka, H. Japan Aerosp Explorat Agcy Chuo Ku 3-1-1 Yoshinodai Sagamihara Kanagawa 2525210 Japan IHI AEROSP Co Ltd 900 Fujiki Tomioka Gunma 3702398 Japan

This paper investigates the launch capability of the SS-520 as a CubeSat launch vehicle. The SS-520 was developed by JAXA originally as a two-stage, spin-stabilized, solid-propellant sounding rocket. With less than 2.6 tons in total mass and 10 m in length, the SS-520-5 successfully launched a single 3U-sized CubeSat into orbit on February 3, 2018. The SS-520-5 obtained its capability as a CubeSat launch vehicle by installing a 3rd stage solid motor in addition to the RCS between the 1st and 2nd stages. However, its launch capability was limited due to its rocket system configuration. In order to pursue the SS-520's launch capability, two effective modifications from the SS-520-5 are proposed: thrust enhancement of the 1st stage motor and installation of an additional RCS between the 2nd and 3rd stages. The framework of launch capability analysis is established by a multi-objective genetic algorithm (MOGA), where its two objectives are selected as the altitudes of perigee and apogee. The analysis reveals that the two proposed modifications to the SS-520-5 work effectively but differently. The 10% increase of the 1st stage enhancement is particularly effective when the target altitude of perigee is low (e.g., 200 km), whereas the installment of the additional RCS with 30 kg increases accessibility to a much higher altitude of perigee, even to circular orbit reaching altitudes of 550 km for a 1U-sized CubeSat and 280 km for a 6U-sized CubeSat. The simultaneous application of both modifications would result in launch capability able to deliver a 10-kg payload. From a more general perspective, the results in this paper suggest that it is possible for a very small launch vehicle (VSLV) of the 3-ton class and 10m in length to deliver a 10-kg-class payload into low Earth orbit.

关键词： SS-520 Very small launch vehicle (VSLV) Mission design and analysis multi-objective genetic algorithm Attitude control for spinning spacecraft

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Application of entransy theory on structure optimization of serrated fin in plate-fin heat exchanger

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APPLIED THERMAL ENGINEERING 2020年 173卷 114809-114809页

作者： Li, Ke Wen, Jian Liu, Yuce Liu, Huaqing Wang, Simin Tu, Jiyuan Xi An Jiao Tong Univ Sch Energy & Power Engn Dept Refrigerat & Cryogen Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Chem Engn & Technol Dept Proc Equipment & Control Engn Xian 710049 Peoples R China Tsinghua Univ Inst Nucl & New Energy Technol Key Lab Adv Reactor Engn & Safety Minist Educ Beijing 100084 Peoples R China

The effects of geometrical parameters of serrated fin in plate-fin heat exchanger (PFHE) on entransy-dissipationbased thermal resistance (EDTR) caused by heat transfer (R-ht) and fluid friction (R-ff ) are investigated by design of experiment(DOE) and response surface methodology (RSM) in this paper. The results show that the fin height, fin interrupted length and fin space are positively correlated with R-ht, while the fin thickness and PFHE length are negatively correlated with R-ht. R-ff is far larger than R-ht, for about an order of magnitude, and R-ff increases almost linearly with the increase of Re when geometric parameters are fixed. R-ff increases with fin thickness and PFHE length, and decreases with fin height, fin interrupted length and fin space. Furthermore, multi-objective genetic algorithm (MOGA) is carried out. Compared with the original design, R-ht of optimal structure 1 and 2 obtained based on two objectives of minimizing R-ff and minimizing R-ht reduces by 27.9% and 41.2%, respectively, and R-ff reduces by 65.3% and 55.8% respectively. Compared with the optimal results based on traditional objective functions (maximize j and minimize f), R-ht and R-ff obtained based on EDTR objective functions reduce by an average of 37.6% and 30.3% respectively, and the JF factor of EDTR optimal results increases by an average of 10.1%.The results show that the optimization results based on EDTR objective functions are better than that based on traditional objective functions for serrated fin in PFHE.

关键词： Plate-fin heat exchanger Serrated fin Response surface multi-objective genetic algorithm Entransy-dissipation-based thermal resistance

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An efficient hybrid multi-objective memetic algorithm for the frequency assignment problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 87卷 103265-000页

作者： Kiouche, Abd Errahmane Bessedik, Malika Benbouzid-SiTayeb, Fatima Keddar, Mohamed Reda Ecole Natl Super Informat ESI LMCS BP 68M 16309 Algiers Algeria

This paper investigates hybridization of multi-objective memetic algorithm and artificial immune system (AIS) for the Frequency Assignment Problem (FAP) in cellular mobile networks. The considered objectives to minimize are the total interference, the maximal interference, and the number of used frequencies. The proposed approach integrates FAP-specific local search into the evolutionary process to overcome the shortcoming of the multi-objective genetic algorithm, as well as clonal selection and receptor editing, which aims to improve the algorithm exploration and exploitation abilities. Based on the hypervolume metric, the proposed hybrid multi-objective algorithm produces high quality solutions as proved by the tests performed over COST259 instances and corroborated by the comparisons with the most frequently referred algorithms in the related literature. Furthermore, the effect and the behaviour of the main parameters of our algorithm and the interaction between them are analysed using the Design of Experiment (DOE).

关键词： multi-objective genetic algorithm Memetic algorithm Local search Clonal selection Receptor editing Frequency assignment problem

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COSMO: A dynamic programming algorithm for multicriteria codon optimization

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2020年 18卷 1811-1818页

作者： Taneda, Akito Asai, Kiyoshi Hirosaki Univ Grad Sch Sci & Technol Hirosaki Aomori 0368561 Japan Univ Tokyo Grad Sch Frontier Sci Kashiwa Chiba 2778562 Japan

Codon optimization in protein-coding sequences (CDSs) is a widely used technique to promote the heterologous expression of target genes. In codon optimization, a combinatorial space of nucleotide sequences that code a given amino acid sequence and take into account user-prescribed forbidden sequence motifs is explored to optimize multiple criteria. Although evolutionary algorithms have been used to tackle such complex codon optimization problems, evolutionary codon optimization tools do not provide guarantees to find the optimal solutions for these multicriteria codon optimization problems. We have developed a novel multicriteria dynamic programming algorithm, COSMO. By using this algorithm, we can obtain all Pareto-optimal solutions for the multiple features of CDS, which include codon usage, codon context, and the number of hidden stop codons. User-prescribed forbidden sequence motifs are rigorously excluded from the Pareto-optimal solutions. To accelerate CDS design by COSMO, we introduced constraints that reduce the number of Pareto-optimal solutions to be processed in a branch-andbound manner. We benchmarked COSMO for run-time and the number of generated solutions by adapting selected human genes to yeast codon usage frequencies, and found that the constraints effectively reduce the run-time. In addition to the benchmarking of COSMO, a multi-objective genetic algorithm (MOGA) for CDS design was also benchmarked for the same two aspects and their performances were compared. In this comparison, (i) MOGA identified significantly fewer Pareto-optimal solutions than COSMO, and (ii) the MOGA solutions did not achieve the same mean hypervolume values as those provided by COSMO. These results suggest that generating the whole set of the Pareto-optimal solutions of the codon optimization problems is a difficult task for MOGA. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

关键词： Constraints Codon adaptation index Codon pair bias Hidden stop codon multi-objective genetic algorithm Codon optimization/deoptimization

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Radial basis function surrogate model-based optimization of guardrail post embedment depth in different soil conditions

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2020年第2-3期234卷 739-761页

作者： Ozcanan, Sedat Atahan, Ali Osman Istanbul Tech Univ Istanbul Turkey

For guardrail designers, it is essential to achieve a crashworthy and optimal system design. One of the most critical parameters for an optimal road restraint system is the post embedment depth or the post-to-soil interaction. This study aims to assess the optimum post embedment depth values of three different guardrail posts embedded in soil with varying density. Posts were subjected to dynamic impact loads in the field while a detailed finite element study was performed to construct accurate models for the post-soil interaction. It is well-known that experimental tests and simulations are costly and time-consuming. Therefore, to reduce the computational cost of optimization, radial basis function-based metamodeling methodology was employed to create surrogate models that were used to replace the expensive three-dimensional finite element models. In order to establish the radial basis function model, samples were derived using the full factorial design. Afterward, radial basis function-based metamodels were generated from the derived data and objective functions performed using finite element analysis. The accuracy of the metamodels were validated by k-fold cross-validation, then optimized using multi-objective genetic algorithm. After optimum embedment depths were obtained, finite element simulations of the results were compared with full-scale crash test results. In comparison with the actual post embedment depths, optimal post embedment depths provided significant economic advantages without compromising safety and crashworthiness. It is concluded that the optimum post embedment depths provide an economic advantage of up to 17.89%, 36.75%, and 43.09% for C, S, and H types of post, respectively, when compared to actual post embedment depths.

关键词： Guardrail post embedment depth optimization numerical analysis radial basis function multi-objective genetic algorithm

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Direct method for uncertain multi-objective optimization based on interval non-dominated sorting

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STRUCTURAL AND multiDISCIPLINARY OPTIMIZATION 2020年第2期62卷 729-745页

作者： Liu, Guiping Liu, Sheng Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China

The interval optimization methods which describe the uncertainty by intervals are widely used in uncertain multi-objective optimization. Commonly, the interval values of the objective functions are substituted by approximate values, which will cause the loss of accuracy. To deal with the problem, a direct method is presented in this paper. In the framework of the multi-objective genetic algorithm, the upper and lower boundaries of the objective and constraint functions are found using the Monte Carlo stochastic simulation method and the constraints are handled by the degree of interval constraint violation;and then every two individuals from the combination population of the evolutionary population and the external elite population are compared using the interval comparison method. Afterwards, an interval non-dominated sorting based on the interval dominance relations is performed to sort the combination population into different non-dominated level. Furthermore, a general crowding distance is calculated for each individual to make individuals from the same non-dominated level comparable and also to maintain the diversity. The efficiency and accuracy of the proposed method is demonstrated by three test functions, the multi-objective structural optimization of the ten-bar truss, and the multi-objective structural optimization of a vehicle body for crashworthiness and lightweight.

关键词： Uncertain multi-objective optimization Interval non-dominated sorting General crowding distance multi-objective genetic algorithm Crashworthiness

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Optimal energy management of microgrid using advanced multi-objective particle swarm optimization

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ENGINEERING COMPUTATIONS 2020年第6期37卷 2085-2110页

作者： Anh, Ho Pham Huy Kien, Cao Van Ho Chi Minh City Univ Technol VNU HCM FEEE Ho Chi Minh City Vietnam Ind Univ Ho Chi Minh City Fac Elect Technol Ho Chi Minh City Vietnam

Purpose The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power isolated microgrid. The microgrid investigated combines renewable and conventional power generation. Design/methodology/approach Five bio-inspired optimization methods include an advanced proposed multi-objective particle swarm optimization (MOPSO) approach which is comparatively applied for OEM of the implemented microgrid with other bio-inspired optimization approaches via their comparative simulation results. Findings Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization methods. Moreover, the proposed MOPSO is successfully applied to perform 24-h OEM microgrid. The simulation results also display the merits of the real time optimization along with the arbitrary of users' selection as to satisfy their power requirement. Originality/value This paper focuses on the OEM of a designed microgrid using a newly proposed modified MOPSO algorithm. Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization approaches.

关键词： Optimal energy management Hybrid microgrid multi-objective genetic algorithm multi-objective particle swarm optimization algorithm 24-hour optimal energy management simulations Bio-inspired optimization methods

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Investigation of Tool Chatter Features at Higher Metal Removal Rate Using Sound Signals

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ACOUSTICS AUSTRALIA 2020年第1期48卷 141-148页

作者： Gupta, Pankaj Singh, Bhagat Jaypee Univ Engn & Technol Guna MP India

Chatter-free turning has been a matter of concern for engineers. Improper selection of cutting parameters leads to regenerative chatter and loss of productivity. In the present work, a methodology has been proposed to select an appropriate combination of input cutting parameters for stable turning with improved metal removal rate (MRR). Chatter signals generated during the turning of Al 6061 are acquired using a microphone and processed using local mean decomposition signal processing technique. Thereafter, these decomposed signals have been analyzed in order to extract tool chatter features. Prediction models of chatter indices and MRR have been developed using response surface methodology. Moreover, these prediction models have been optimized using multi-objective genetic algorithm for ascertaining the optimal range of cutting parameters for stable turning with higher MRR. Finally, obtained stable range has been validated by performing more experiments.

关键词： Tool chatter Local mean decomposition Chatter indices Nakagami distribution RSM multi-objective genetic algorithm

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