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Global coverage path planner in 2.5 dimensions for nonholonomic vehicles

INTERNATIONAL JOURNAL OF FOREST ENGINEERING 2025年第2期36卷 201-212页

作者： Arvidsson, Erik Karlberg, Magnus Lideskog, Hakan Lindback, Torbjorn LTU Dept Engn Sci & Math Laboratorievagen 14 SE-97187 Lulea Sweden

This study presents a metaheuristic approach to coverage path planning for ground-based forest operations, focusing on minimizing path lengths for forest vehicles while considering terrain characteristics and vehicle parameters. Forest vehicles can usually tolerate higher pitch than roll angles, which makes them vulnerable to rollover. To mitigate that, this method utilizes a genetic algorithm to optimize the sequence of nodes, which are scattered over the site with equal spacing. The coverage path planner then calculates the Dubins path distance between every node in the fitness function, together with penalties for exceeding pitch, roll and soil moisture constraints for the vehicle. This ensures that the path planner tries to make the most traversable path as possible, while trying to minimize the driving distance. Two synthetic test sites resembling primitive challenging terrains, and one real site were utilized to theoretically evaluate the proposed method. The results show that aligning the node patterns with the critical slope headings, instead of having a straight pattern, had little effect on the path length. However, square grids can yield shorter paths across multiple runs, while triangular grids ensure consistent results in single runs. A two-hectare site took 43 minutes to calculate on average. This suggests that further development of the path planner could lead to significant improvements, enabling the management of sites larger than a few hundred nodes. However, the calculation time is justified for the reduced path length during deployment. The study presents a methodology that supports manual operators and establishes foundations for full autonomy.

关键词： Unmanned ground vehicle UGV genetic algorithm ga coverage path planning CPP precision forestry autonomous forest vehicle

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Multi-Objective Optimisation of Cortical Spiking Neural Networks With genetic algorithms 32

Multi-Objective Optimisation of Cortical Spiking Neural Netw...

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32nd Irish Signals and Systems Conference (ISSC)

作者： Fitzgerald, James Wong-Lin, KongFatt Ulster Univ Intelligent Syst Res Ctr Sch Comp Engn & Intelligent Syst Magee Campus Derry Londonderry North Ireland

ISBN: (纸本)9781665434294

Spiking neural networks (SNNs) communicate through the all-or-none spiking activity of neurons. However, fitting the large number of SNN model parameters to observed neural activity patterns, for example, in biological experiments, remains a challenge. Previous work using genetic algorithm (ga) optimisation on a specific efficient SNN model, using the Izhikevich neuronal model, was limited to a single parameter and objective. This work applied a version of ga, called non-dominated sorting ga (NSga-III), to demonstrate the feasibility of performing multi-objective optimisation on the same SNN, focusing on searching network connectivity parameters to achieve target firing rates of excitatory and inhibitory neuronal types, including across different network connectivity sparsity. We showed that NSga-III could readily optimise for various firing rates. Notably, when the excitatory neural firing rates were higher than or equal to that of inhibitory neurons, the errors were small. Moreover, when connectivity sparsity was considered as a parameter to be optimised, the optimal solutions required sparse network connectivity. We also found that for excitatory neural firing rates lower than that of inhibitory neurons, the errors were generally larger. Overall, we have successfully demonstrated the feasibility of implementing multi-objective ga optimisation on network parameters of recurrent and sparse SNN.

关键词： Multi-objective parameter optimisation genetic algorithm ga NSga-III recurrent spiking neuronal network model Izhikevich neuronal model

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Voice recognition enhancement by genetic algorithm

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Heliyon 2024年第23期10卷 e38845页

作者： Salhi, Mohamed Salah Alenezi, Ali Hamdan Research Laboratory of Signal Image and Information Technology LR-SITI University of Tunis El Manar National Engineering School of Tunis Tunis Tunisia Remote Sensing Unit Electrical Engineering Department. Northern Border University Arar Saudi Arabia

This research paper investigates the application of genetic algorithms (ga) in optimizing Artificial Neural Networks (ANN) for phoneme recognition. The study examines the formalism of ga, their parameters, and operators, and describes the genetic strategy adopted for phoneme recognition using the TIMIT sound database. The paper presents the outcomes of experiments conducted on the phonemes of the test base and the DR1 dialect learning base of the TIMIT base, and compares the recognition rates obtained by learning and testing with those guided by Self-Organizing Map (SOM) experiments. The findings suggest that the ga algorithm provides improved recognition rates and extends the search space to a global optimum, but can sometimes produce unacceptable results due to rapid and premature convergence and the overfitting problem. The study proposes a hybrid model between SOM and ga to take advantage of each of their properties and improve recognition rates. The results show that the proposed approach achieves an average recognition rate of over 95 %. © 2024

关键词： genetic algorithm ga Hybrid model Neural map SOM Phonemes recognition

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Energy Trading in the Smart Grid: Poly-sellers Decision based on game Theory 11

Energy Trading in the Smart Grid: Poly-sellers Decision base...

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11th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Alsalloum, Hala Merghem-Boulahia, Leila Rahim-Amoud, Rana Lebanese Univ LTRM EDST Beirut Lebanon Lebanese Univ Fac Sci Beirut Lebanon Univ Technol Troyes ICD ERA Troyes France

ISBN: (纸本)9789897583506

An essential element in the smart grid is the existence of prosumers, i.e. the consumers who can also produce and sell the energy. They will become one of the stakeholders of the future grid. Their active behavior is helpful on different sides: the environmental, economical and social sides. In fact, integrating the prosumers will result in selling the surplus of energy to the grid or other consumers. However, the interactions between prosumers and the grid need to be defined in order to maximize the profit of each stakeholder. This paper proposes an energy-trading algorithm based on game theory and genetic optimization in order to optimize the satisfaction of prosumers. In our solution, buyers can afford their demands from different sellers taking into consideration the distance, the price and the amount of energy traded and needed. Simulation results indicate the effectiveness of our proposed approach in terms of minimization the total cost and maximization each prosumer satisfaction i.e. minimization the buyer's bills and maximization the seller's revenues.

关键词： Energy Trading Optimization Sellers Buyers Energy Management game Theory genetic algorithm ga Agents

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Data reconstruction of seasonal changes of amylolytic microorganisms amount in copper polluted soils 13

Data reconstruction of seasonal changes of amylolytic microo...

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13th IEEE International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT)

作者： Moroz, Olha Stepashko, Volodymyr NASU Int Res & Training Ctr Informat Technol & Syst Dept Informat Technol Induct Modelling Glushkov Ave 40 UA-03680 Kiev Ukraine MESU Glushkov Ave 40 UA-03680 Kiev Ukraine

The article presents the application results of the combinatorial-genetic algorithm COMBI-ga for reconstruction the missing decade data in the series of observations of amylolytic microorganisms quantity in a soil plo... 详细信息

ISBN: (纸本)9781538664636

关键词： inductive modelling GMDH combinatorial algorithm COMBI genetic algorithm ga hybrid algorithm COMBI-ga amylolytic microorganisms soil heavy metals data reconstruction

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Data reconstruction of seasonal changes of amylolytic microorganisms amount in copper polluted soils

Data reconstruction of seasonal changes of amylolytic microo...

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International Scientific and Technical Conference on Computer Science and Information Technologies

作者： Olha Moroz Volodymyr Stepashko Department for Information Technologies of Inductive Modelling Systems of the NASU and MESU Kyiv Ukraine

关键词： Inductive modelling GMDH Combinatorial algorithm COMBI genetic algorithm ga Hybrid algorithm COMBI-ga Amylolytic microorganisms Soil Heavy metals Data reconstruction

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A Review of Volt/Var Control Techniques in Passive and Active Power Distribution Networks 4

A Review of Volt/Var Control Techniques in Passive and Activ...

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4th IEEE International Conference on Smart Energy Grid Engineering (SEGE)

作者： Tahir, Monsef Nassar, Mohammed E. El-Shatshat, Ramadan Salama, M. M. A. Univ Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781509051113

Volt/Var control problem of distribution systems has been extensively investigated in literature. Many control models and algorithms have been proposed to achieve better system quality, security, reliability, efficiency, loadability, and cost effectiveness. Early distribution systems are built based on centralized power generation which is named passive distribution system (PDS), where power flow is unidirectional. Nowadays, the topology of the distribution system allows for bidirectional power flow which is named active distribution system (ADS) due to the presence of active resources, such as distributed generations (DGs). The complexity of controlling each system depends on the topology and size of the network, as well as the control devices used. However, in general there are mainly two main control strategies used to control power networks: centralized and decentralized. This paper provides a review for both control strategies in the distribution system based on Volt/Var control techniques. It introduces the most commonly used techniques and algorithms in the literature for passive and active distribution systems. Moreover, it provides the reader with a comprehensive review on the common optimization techniques and the different objective functions used in terms of loss minimization, voltage deviation, and minimum control variable operation.

关键词： on-load tap changer OLTC step voltage regulator SVR shunt or switched capacitor SC genetic algorithm ga particle swarm optimization PSO

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Student Grouping Using Adaptive genetic algorithm 18

Student Grouping Using Adaptive Genetic Algorithm

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18th IEEE International Electronics Symposium (IES)

作者： Ciptayani, Putu Indah Dewi, Kadek Cahya Sentana, I. Wayan Budi Politekn Negeri Bali Elect Engn Dept Badung Bali Indonesia Politekn Negeri Bali Business Dept Badung Bali Indonesia

ISBN: (纸本)9781509016402

Learning in groups is one of the important things in the learning process. By forming some groups of students, they are able to share and complement deficiency. The best combination of student in a group will give the best result in learning. But to form the groups of students, need much time, because it is a combinatorical problem. To get the best combination, it needs to evaluate all of the possible solutions. Adaptive genetic algorithm (Aga) was used in this paper to find the best group formation. The adaptive number of population, crossover probability, and mutation rate were applied in this paper based on the fitness achieved in each generation. This study used 25 students in Basic Programming class as a data testing and divided them into 5 groups. This paper shown how Aga find the solution of the problem eficiently in time and the fitness value. The maximum of fitness gain by Aga was 86.88% of the actual fitness in the case study.

关键词： adaptive ga genetic algorithm ga adaptive crossover probablity adaptive mutation rate adaptive population size student grouping

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Optimal Reactive Power Dispatch by Furnishing UPFC Using Multi-Objective Hybrid gaPSO Approach For Transmission Loss Minimisation and Voltage Stability

Optimal Reactive Power Dispatch by Furnishing UPFC Using Mul...

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International Conference on Nascent Technologies in the Engineering Field (ICNTE)

作者： Shrawane, Shilpa S. Diagavane, Manoj Bawane, Narendra GHRCE Dept Elect Engn Nagpur Maharashtra India VIT Nagpur Maharashtra India SBJIT Nagpur Maharashtra India

ISBN: (纸本)9781479972630

The goal of optimal reactive power dispatch is to make the transmission loss minimal in addition to control of voltage profile such that the voltage deviations at the load buses for various loading conditions. By furnishing Flexible AC Transmission System devices in the transmission system, the power control can be finely achieved. The paper describes Optimal Reactive Power Dispatch for the power loss minimisation using optimal location of Unified Power Flow Controller for loss minimisation. The power flow is first solved by Newton Raphson method without implementing or furnishing Unified Power Flow Controller and then by furnishing them randomly in the IEEE - 30 bus system. Unified Power Flow Controller allows control of real and reactive power both in addition to voltage magnitude control at various buses. In this paper, optimal reactive power dispatch is applied using the hybrid ga - PSO. Simulations are performed on IEEE-30 bus test system using MATLAB software package to ensure efficacy of the proposed algorithm. This paper aims at to find the optimum usage of Unified Power Flow Controller which means the finding of the optimal location where their influence would be more useful.

关键词： Unified Power flow controller UPFC genetic algorithm ga Particle swarm optimisation PSO Flexible AC transmission devices FACTs Optimal Reactive Power Dispatch ORPD

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Concoction of UPFC for Optimal Reactive Power Dispatch Using Hybrid gaPSO Approach for Power Loss Minimisation 6

Concoction of UPFC for Optimal Reactive Power Dispatch Using...

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6th IEEE Power India International Conference (PIICON)

作者： Shrawane, Shilpa S. Diagavane, Manoj Bawane, Narendra GHRCE Dept Elect Engn Nagpur Maharashtra India VIT Nagpur Maharashtra India

ISBN: (纸本)9781479960422

The main motive of OPRD is to minimise the transmission loss along with control of voltage profile so that the voltage deviations at the load buses for variations in the loading conditions. With the concoction of FACTS devices, the power control can be finely achieved. The paper describes ORPD for the power loss minimisation using optimal concoction of UPFC and the cost of UPFC for ORPD. The power flow is first solved by NR method without concocting UPFC and then with concoction of UPFC randomly placed in the IEEE -30 bus system. The UPFC allows control of real and reactive power both in addition to voltage magnitude control at various buses. In this paper, OPRD is applied using the hybrid ga -PSO. Simulations are performed on IEEE-30 bus test system using MATLAB software package to ensure efficacy of the proposed algorithm. This paper aims at to find the optimum usage of UPFC which means the finding of the optimal location where their influence would be more useful as well as to determine their cost.

关键词： Unified Power flow controller UPFC genetic algorithm ga Particle swarm optimisation PSO Flexible AC transmission devices FACTs

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