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Robustness analysis and identification for an enzyme-catalytic complex metabolic network in batch culture

BIOPROCESS AND BIOSYSTEMS ENGINEERING 2021年第7期44卷 1511-1524页

作者： Yang, Qi Chen, Qunbin Niu, Teng Feng, Enmin Yuan, Jinlong Guilin Univ Elect Technol Sch Math & Comp Sci Guilin 541004 Guangxi Peoples R China Dalian Univ Technol Sch Math Sci Dalian 116024 Liaoning Peoples R China Dalian Maritime Univ Sch Sci Dept Math Dalian 116026 Liaoning Peoples R China

Bioconversion of glycerol to 1,3-propanediol is a promising way to mitigate the shortage of energy. To maximize the production of 1,3-propanediol, it needs to control precisely microbial fermentation process. However, it might consume lots of human and material resources when conducting experimental tests many times. In this study, a nonlinear enzyme-catalytic dynamical system is developed to describe the bioconversion process of glycerol to 1,3-propanediol, especially continuous piecewise linear functions are used as identification parameters. The existence, uniqueness and continuity of solutions are also discussed. Then, considering the fact that the concentration of intracellular substances is difficult to measure in experiments, a new quantitative definition of biological robustness is introduced as a performance index to determine the identification parameters related to intracellular substances. Meanwhile, a two-phase optimization algorithm is constructed to solve the identification model. By comparison with the experimental data, it can be found that the present nonlinear dynamical system can describe the fermentation process very well. Finally, the present nonlinear dynamical system and the corresponding optimal identification parameters might be useful in future studies on the batch culture of glycerol to 1,3-propanediol.

关键词： Complex metabolic network Biological robustness Batch culture System identification optimization algorithm

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Modified Adaptive Bats Sonar algorithm with Doppler Effect Mechanism for Solving Single Objective Unconstrained optimization Problems 15

Modified Adaptive Bats Sonar Algorithm with Doppler Effect M...

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IEEE 15th International Colloquium on Signal Processing and its Applications (CSPA)

作者： Azlan, N. A. Yahya, N. M. Univ Malaysia Pahang Fac Mfg Engn Cybernat & Syst Engn Lab Pekan Malaysia

ISBN: (纸本)9781538675632

A modified adaptive bats sonar algorithm with Doppler Effect (MABSA-DE) is a new algorithm with an element of Doppler Effect theory that helped the transmitted bats' beam towards a superior position. The performances of the proposed algorithm are validated on a several well-known single objective unconstrained benchmark test functions. The obtained results show that the algorithm can perform well to find an optimum solution. The statistical results of MABSA-DE to solve all the test functions also has been compared with the results from the original MABSA on similar test functions. The comparative study has shown that MABSA-DE outperforms the original algorithm, and thus, it can be an efficient alternative method in solving single objective unconstrained optimization problems.

关键词： Bat algorithm Doppler Effect Metaheuristic optimization algorithm Single Objective optimization

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E-TDGO: An encrypted trust-based dolphin glowworm optimization for secure routing in mobile ad hoc network

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2020年第7期33卷

作者： Mukhedkar, Moresh Madhukar Kolekar, Uttam Ramrao Adik Inst Technol Dept Elect & Telecommun Engn DY Patil Campus Nerul Navi Mumbai India AP Shah Inst Technol Dept Elect & Telecommun Engn Mumbai Maharashtra India

Mobile ad hoc networks (MANETs) are independent networks, where mobile nodes communicate with other nodes through wireless links by multihop transmission. Security is still an issue to be fixed in MANETs. Hence, a routing protocol named encrypted trust-based dolphin glowworm optimization (DGO) (E-TDGO) is designed using Advanced Encryption Standard-128 (AES-128) and trust-based optimization model for secure routing in MANET. The proposed E-TDGO protocol includes three phases, namely, k-path discovery, optimal path selection, and communication. At first, k paths are discovered based on the distance and the trust level of the nodes. From the k paths discovered, the optimal path is selected using a novel algorithm, DGO, which is developed by combining glowworm swarm optimization (GSO) algorithm and dolphin echolocation algorithm (DEA). Once the optimal path is selected, communication begins in the network such that E-TDGO protocol ensures security. The routing messages are encrypted using AES-128 with shared code and key to offer security. The experimental results show that the proposed E-TDGO could attain throughput of 0.11, delay of 0.01 second, packet drop of 0.44, and detection rate of 0.99, at the maximum number of rounds considered in the network of 75 nodes with attack consideration.

关键词： AES-128 mobility optimization algorithm route discovery routing security trust

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A High-Performance Self-learning Antelopes Migration algorithm (SAMA) for Global optimization 17

A High-Performance Self-learning Antelopes Migration Algorit...

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IEEE Int Conf on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom)

作者： Lin, Na Fu, Luwei Zhao, Liang Liu, Lu Min, Geyong Shenyang Aerosp Univ Sch Comp Sci Shenyang Peoples R China Univ Leicester Dept Informat Leicester Leics England Univ Exeter Dept Comp Sci Exeter Devon England

ISBN: (纸本)9781728143286

In global optimization, Nature-Inspired Computing (NIC) has attracted increasing attention in recent years. However, a common predicament for most optimization algorithms is the poor tradeoff between global exploration and local exploitation, which results in premature convergence. Moreover, the complex mechanism of algorithms makes them inconvenient to use for actual users, who are not specialists in computational intelligence (CI). To handle these defects, this paper proposes a succinct high-performance nature-inspired algorithm for global optimization, namely, Self-learning Antelopes Migration algorithm (SAMA), which imitates the migration behavior of Tibetan antelopes on the HohXil grassland of China. The antelope herd always migrates to the most fertile place they have found, where ordinary members graze about here and some vigorous members selected as scouts to explore farther to seek a better place. The interesting behaviors of different antelopes guarantee the development and best state of the herd. They also have learning capability which adaptively corrects their searching strategy through the past migration experience. To verify the high performance of the proposed SAMA, we compare our proposed algorithm with five advanced nature-inspired algorithms. A series of benchmark tests demonstrate that our SAMA has remarkable superiority on both optimization accuracy and speed.

关键词： Nature-Inspired Computing (NIC) Self-learning Antelopes Migration algorithm (SAMA) global search optimization algorithm

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Subdivided Error Correction Method for Photoelectric Axis Angular Displacement Encoder Based on Particle Swarm optimization

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2020年第10期69卷 8372-8382页

作者： Gao, Xu Li, Shuhang Ma, Qinglin Changchun Univ Sci & Technol Sch Photoelect Engn Changchun 130022 Peoples R China

To improve the subdivision accuracy of the absolute photoelectric shaft encoder, a particle swarm optimization (PSO) algorithm is proposed for error correction. The error is generated by the subdivision of the grating Moire fringe signal, and a Moire fringe photoelectric signal data acquisition system is built based on a digital signal processor. First, based on the principle of measuring the angular displacement with grating diffraction interference, a mathematical model for the Moire fringe photoelectric signal is established, and then the influence of signal deviation on the subdivision error is noted. Second, the compensation model of the signal subdivision error is established. Then, the PSO algorithm is introduced, and the inertial weight and speed factor in the algorithm are optimized. Seven undetermined parameters in the signal model are identified using an optimization algorithm. Finally, according to the subdivision error compensation model, the data processing system can automatically correct the subdivision error caused by the direct current (dc) offset, orthogonality error, and sinusoidal error. Taking the 23-bit photoelectric encoder as the experimental object, the actual static subdivision error can be reduced by at least 0.30-0.46". In the encoder operation, if the correction preprocessing is performed on all the grating Moire signals, then the subdivision accuracy of the encoder can be improved, and its spatial adaptability and angular reliability will also be improved.

关键词： Gratings Signal processing algorithms Shafts Harmonic analysis Error correction Particle swarm optimization Displacement measurement Error correction grating fringe signal optimization algorithm photoelectric encoder subdivision precision

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optimization of a Supersonic Rocket-Based Combined Cycle Inlet Using Differential Evolution

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JOURNAL OF SPACECRAFT AND ROCKETS 2020年第3期57卷 496-509页

作者： Jee, Craig Etele, J. Carleton Univ Ottawa ON K4M 1B4 Canada Carleton Univ Dept Mech & Aerosp Engn Ottawa ON K4M 1B4 Canada

A differential evolution optimization algorithm is proposed for an airbreathing rocket inlet called the exchange inlet at supersonic flight conditions. A five-parameter fitness function is used, which includes variables representing the ingested air mass flow, total pressure drop through the inlet, and shear layer area. Using a differential weight of 0.85, a population size of 75, and a crossover probability of 0.3, it is shown that the algorithm yields a design with a genome within 10% of the most likely global optimum 93% of the time. Single-point optimization is performed at flight Mach numbers of 1.5, 2.5, and 3.5 to establish a Pareto front of optimal designs. From these Pareto fronts a single optimum is chosen and evaluated over a range of off-design flight Mach numbers from 1.3 to 4.0. In terms of air mass flow and total pressure, the Mach 2.5 optimal design is shown to outperform the other designs between Mach 2.0 and 3.2, while yielding an air mass flow within 12% of the others at all other flight conditions considered.

关键词： Rocket Based Combined Cycle Freestream Mach Number Rockets Mass Flow Shear Layers Supersonic Flight optimization algorithm Genetic algorithm Reynolds Averaged Navier Stokes Turbine Based Combined Cycle

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An Improved Gravitational Search algorithm for optimization Problems 31

An Improved Gravitational Search Algorithm for Optimization ...

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31st Chinese Control And Decision Conference (CCDC)

作者： Li, Wei Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian 710048 Peoples R China

ISBN: (纸本)9781728101057

Although GSA is an effective optimization algorithm, the best fitness found by GSA cannot be improved in every generation. In order to improve the performance of GSA, this paper proposed an improved gravitational search algorithm (IGSA). In IGSA, a crossover operation is introduced in IGSA so that each solution can inherit some useful information from the global best solution. The exploitation capability of the algorithm can be greatly enhanced. The linearly decreasing weight is used to balance the global and local search abilities. To verify the effectiveness of IGSA, numerical experiments are carried on ten benchmark problems from CEC2014. The experimental results show that IGSA is competitive with respect to other compared algorithms for solving optimization problems.

关键词： Gravitational Search algorithm Crossover Weight optimization algorithm

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Teaching-Learning-Based optimization Enhanced With Multiobjective Sorting Based and Cooperative Learning

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IEEE ACCESS 2020年 8卷 65923-65937页

作者： Li, Wei Fan, Yaochi Xu, Qingzheng Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian 710048 Peoples R China Natl Univ Def Technol Coll Informat & Commun Xian 710106 Peoples R China

Teaching-learning-based optimization (TLBO) algorithm has been shown to be an effective optimization algorithm. However, it is easily trapped into local optima when the global optimal solution of the function to be optimized is at the original dot or around the original dot. This paper presents a novel TLBO variant by incorporating multiobjective sorting-based mechanism and cooperative learning strategy to alleviate this problem. Taking advantages of multiobjective optimization in maintaining good population diversity, several teachers are selected based on non-dominated sorting, so as to guide learners to learn more effectively. In addition, the proposed algorithm adopts cooperative learning, including learning within and between groups, to improve the search ability of the algorithm. Experimental and statistical analyses are performed on CEC2014 benchmark functions. The experimental results demonstrate the effectiveness of the proposed algorithm in comparison with other variants of TLBO and other state-of-the-art optimization algorithms.

关键词： Teaching-learning-based optimization non-dominated sorting cooperative learning optimization algorithm learning strategy

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optimization Design of a Rain-Power Utilization System Based on a Siphon and Its Application in a High-Rise Building

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ENERGIES 2020年第18期13卷

作者： Yu, Jiaxin Wang, Jun Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China

Rain falling from the sky is viewed as a clean energy source with a great potential, owing to the large amount of it and its zero pollution nature, the fact that it has scattered raindrops, and its characteristic rainfall concentration that promotes extensive research on harvesting and utilization. Here, we introduce a new approach to harvest rainwater on rooftops called the Rain-Power Utilization System, which is composed of an initial rainwater disposal system and multistage energy conversion system. Initial rainwater is discharged into a split-flow pipe due to its poor quality and impurities. Additionally, clean rainwater is accumulated in a storage pipe until the water level reaches a specified height, triggering siphonage for energy conversion. The same process is repeated in other storage pipes connected in series. Function relations among physical and dimension parameters have been established for further studies. A kind of simplified optimization algorithm has been proposed considering the maximum instantaneous power under the constraint of a permitted vacuum and maximum energy generation per unit length to find the model with an optimal height combination (h(u),h(d)). The experimental prototype developed in proportion is used to verify theoretical research and conduct error analysis to establish an equation of annual energy generation for a high-rise building. Without building extra tanks, this paper presents an innovative approach to maximizing the use of energy in rain for high-rise buildings based on a siphon.

关键词： rainwater energy siphon rain-power utilization system vacuum optimization algorithm experimental validation

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An efficient method for energy management optimization control: Minimizing fuel consumption for hybrid vehicle applications

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第1期42卷 69-80页

作者： Hmidi, Mohamed Elhedi Ben Salem, Ines El Amraoui, Lilia SEICT LR I8ES44 Res Lab Smart Elect & ICT Tunis Tunisia Univ Carthage Natl Engn Sch Carthage ENICarthage BP 45 Rue Entrepreneurs Ariana 2035 Charguia Ii Tunisia

One of the main research areas of the automotive sector is energy consumption and the reduction of pollutant emissions. Therefore, the hybrid electrical vehicle Vehicle electrical hybrid (VEH) is a possible approach to improve the vehicle components or its architecture to maximize system efficiency. This vehicle benefits from levels of consumption and emission of pollutants lower than the thermal vehicle. The problem is then to find the right split power between the two hybrid sources, the internal combustion engine (ICE) and the electrical machines (EM) to minimize fuel consumption. These energy management strategies are the algorithms that achieve instant split power between the two sources in the hybrid electric vehicle (HEV) while controlling the battery state of charge (SOC), in order to improve the fuel economy and optimize the performance HEV. In this research, a parametric study on the impact of optimization of the energy management strategy on the optimal operation of the vehicle is presented. A hybrid vehicle model is described and rules-based energy management strategy is presented. Later these rules are optimized with an optimization algorithm based on Grey Wolf optimizer (GWO). It is a new meta-heuristic based on the population. The results of the simulation make it possible to deduce the impact of this optimal strategy of energy management on fuel consumption and CO2 emissions. The obtained results show that the optimal operation of the vehicle varies according to the best energy management strategies.

关键词： Vehicle electric hybrid VEH fuel consumption hybrid vehicle model rules-based energy management optimization algorithm Grey wolf optimizer

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