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Optimizing a Transportation System Using metaheuristics Approaches (EGD/GA/ACO): A Forest Vehicle Routing Case Study

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World Journal of Engineering and Technology 2024年第1期12卷 141-157页

作者： Hossein Havaeji Thien-My Dao Tony Wong Mechanical Engineering Department école de technologie supérieure Montreal Canada

The large-scale optimization problem requires some optimization techniques, and the metaheuristics approach is highly useful for solving difficult optimization problems in practice. The purpose of the research is to optimize the transportation system with the help of this approach. We selected forest vehicle routing data as the case study to minimize the total cost and the distance of the forest transportation system. Matlab software helps us find the best solution for this case by applying three algorithms of metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Extended Great Deluge (EGD). The results show that GA, compared to ACO and EGD, provides the best solution for the cost and the length of our case study. EGD is the second preferred approach, and ACO offers the last solution.

关键词： metaheuristics algorithms Transportation Costs Optimization Approach Cost Minimisation

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Simulation and Application of algorithms CVRP to Optimize the Transport of Minerals Metallic and Nonmetallic by Rail for Export 8th

Simulation and Application of Algorithms CVRP to Optimize th...

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8th International Conference on Swarm Intelligence (ICSI)

作者： Margain, Lourdes Cruz, Edna Ochoa, Alberto Hernandez, Alberto Ramos Landeros, Jacqueline Univ Politecn Aguascalientes Aguascalientes Mexico Inst Tecnol Super Naranjos Naranjos Mexico UACJ Ciudad Juarez Mexico Univ Autonoma Estado Morelos FCAeI Cuernavaca Morelos Mexico

ISBN: (纸本)9783319618333;9783319618326

Metallic and nonmetallic minerals produced by the State of Puebla, for convenience, can be transported in containers by train to a seaport cargo exported to other countries;for transporting minerals by railways, it must analyze what the most optimal route to bring the product, taking into account various factors involved through each route in order to get a greater benefit for the transportation of minerals by train. This article comparing two metaheuristic algorithms applied to Capacitated Vehicle Routing Problem (CVRP), in order to determine which algorithm gives better optimization solutions that help you make the best route for the transfer. As a final result the implementation of an optimization algorithm Ant Colony was more successful in the runtime that on Genetic Algorithm;because it is slow to find an optimal solution among all generations.

关键词： Metallic and nonmetallic Export Container train Sea port metaheuristics algorithms Capacitated Vehicle Routing Problem (CVRP)

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The Inverse Kinematics solutions for Robot Manipulators based on Firefly Algorithm

The Inverse Kinematics solutions for Robot Manipulators base...

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5th IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Hernandez-Barragan, Jesus Lopez-Franco, Carlos Antonio-Gopar, Cecilia Alanis, Alma Y. Arana-Daniel, Nancy Univ Guadalajara CUCEI Dept Comp Sci Guadalajara Jalisco Mexico

ISBN: (纸本)9781538646267

The solution of the inverse kinematics of robot manipulators may have multiple joints configurations that reach the same end effector pose. These redundant solutions may provide a collision free joint configuration for a safe inverse kinematics task. In this work, we propose to solve the inverse kinematics problem for robot manipulators using the Firefly Algorithm. The proposed method is able to provide one or more solutions for the given inverse kinematics task. Since the method does not required the inversion of any Jacobian matrix, it avoids singularities configurations. Applicability of the proposed approach is illustrated in simulations using a five degree of freedom arm manipulator.

关键词： Denavit-Hartenberg Inverse kinematics metaheuristics algorithms Multimodal optimization

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Steam players count prediction using GRU optimized by modified PSO 32

Steam players count prediction using GRU optimized by modifi...

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32nd Telecommunications Forum, TELFOR 2024

作者： Jovanovic, Luka Zivkovic, Miodrag Zivkovic, Tamara Bacanin, Nebojsa Dubljanin, Dobrivoje Malisic, Snezana Singidunum University Faculty of Technical Sciences Belgrade Serbia Singidunum University Faculty of Informatics and Computing Belgrade Serbia University of Belgrade School of Electrical Engineering Belgrade Serbia University of Belgrade Technical Faculty in Bor Bor Serbia

ISBN: (纸本)9798350391053

Accurate prediction of Steam players count is vital for efficient resource management, informed decision-making, and ability to maximize the potential of each game over its entire lifecycle. From the business point of view, this can directly affect profitability, player retention and strategic planning. This paper examines potential of GRU tuned by an improved version of renowned particle swarm optimization algorithm. The proposed model was placed into comparative simulations with other potent optimization techniques, and obtained experimental outcomes clearly pointed out the supreme performance of the suggested technique. © 2024 IEEE.

关键词： GRU metaheuristics algorithms players count prediction PSO Steam

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Engineering Optimization 1

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2010年

作者： Yang Xin-She

ISBN: (数字)9780470640418

ISBN: (纸本)9780470582466

An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences From engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms. The author introduces all major metaheuristic algorithms and their applications in optimization through a presentation that is organized into three succinct parts: Foundations of Optimization and algorithms provides a brief introduction to the underlying nature of optimization and the common approaches to optimization problems, random number generation, the Monte Carlo method, and the Markov chain Monte Carlo method Metaheuristic algorithms presents common metaheuristic algorithms in detail, including genetic algorithms, simulated annealing, ant algorithms, bee algorithms, particle swarm optimization, firefly algorithms, and harmony search Applications outlines a wide range of applications that use metaheuristic algorithms to solve challenging optimization problems with detailed implementation while also introducing various modifications used for multi-objective optimization Throughout the book, the author presents worked-out examples and real-world applications that illustrate the modern relevance of the topic. A detailed appendix features important and popular algorithms using MATLAB® and Octave software packages, and a rel

关键词： optimization techniques modern optimization techniques metaheuristics metaheuristics algorithms optimization problems solve optimization problems how to solve optimization problems evolutionary optimization metaheuristics and mulitobjective optimization particle swarm optimization Markov chain Monte Carlo method biological inspired computing adaptive and natural computing algorithms evolutionary computation

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Optimization of laser-texturing process parameters of Ti6Al4V alloys using metaheuristic algorithms

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2024年

作者： Urgun, Satilmis Yigit, Halil Fidan, Sinan Sinmazcelik, Tamer Canel, Timur Kocaeli Univ Fac Aeronaut & Astronaut Dept Aviat Elect & Elect Kocaeli Turkiye Kocaeli Univ Fac Technol Dept Informat Syst Engn Kocaeli Turkiye Kocaeli Univ Fac Aeronaut & Astronaut Dept Airframe & Powerplant Maintenance Kocaeli Turkiye Kocaeli Univ Fac Engn Dept Mech Engn Kocaeli Turkiye Kocaeli Univ Fac Arts & Sci Dept Phys Kocaeli Turkiye

Manufacturing microscale textures can reduce friction and wear compared with smooth surfaces. In laser surface texturing, the resulting texture geometry is directly affected by laser parameters such as laser power, scanning speed and overlap rate. Therefore, finding the optimal parameter settings to improve the process performance is crucial. One of the biggest challenges encountered during the optimization stage is accurately determining the functional relationship between surface quality and process parameters. Another significant problem is solving the nonlinear equation system. This study aims to investigate the effect of laser-textured surfaces on the tribological behavior of titanium Ti6Al4V. Optimization of laser texture process parameters using metaheuristic algorithms is a hot topic. The first contribution of this study is the modeling of the contact angle, kurtosis and skewness quality parameters of Ti6Al4V titanium alloy based on the polynomial curve fitting method as a function of laser power, laser speed and overlap rate. Another significant contribution is to optimize the proposed nonlinear regression models using metaheuristic approaches such as genetic algorithm, particle swarm optimization, whale optimization algorithm, gray wolf optimization, equilibrium optimizer, teaching-learning-based optimization, student psychology-based optimization (SPBO), slime mould algorithm and social network search. The reason for using many metaheuristics is to test the effectiveness of different metaheuristic methods in solving this problem. Analysis of variance was performed to evaluate experimental data and compare the success of optimization methods. The results revealed that the proposed metaheuristic methods can be applied to the related problem. It has been observed that the methods give identical results. Among these methods, the SPBO method provided the best performance in the optimization of all laser output parameters.

关键词： Ti6Al4V alloy texture CO2 laser optimization metaheuristics algorithms

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Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

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SENSORS 2022年第15期22卷 5750-5750页

作者： Ismail, Leila Buyya, Rajkumar United Arab Emirates Univ Coll Informat Technol Dept Comp Sci & Software Engn Intelligent Distributed Comp & Syst INDUCE Res La Abu Dhabi 15551 U Arab Emirates United Arab Emirates Univ Natl Water & Energy Ctr Abu Dhabi 15551 U Arab Emirates Univ Melbourne Sch Comp & Informat Syst Cloud Comp & Distributed Syst CLOUDS Lab Parkville Vic 3010 Australia

The recent upsurge of smart cities' applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged the design of the upcoming 6G network generation, due to their stringent requirements in terms of the quality of services (QoS), availability, and dependability to satisfy a Service-Level-Agreement (SLA) for the end users. Industries and academia have started to design 6G networks and propose the use of AI in its protocols and operations. Published papers on the topic discuss either the requirements of applications via a top-down approach or the network requirements in terms of agility, performance, and energy saving using a down-top perspective. In contrast, this paper adopts a holistic outlook, considering the applications, the middleware, the underlying technologies, and the 6G network systems towards an intelligent and integrated computing, communication, coordination, and decision-making ecosystem. In particular, we discuss the temporal evolution of the wireless network generations' development to capture the applications, middleware, and technological requirements that led to the development of the network generation systems from 1G to AI-enabled 6G and its employed self-learning models. We provide a taxonomy of the technology-enabled smart city applications' systems and present insights into those systems for the realization of a trustworthy and efficient smart city ecosystem. We propose future research directions in 6G networks for smart city applications.

关键词： Artificial Intelligence (AI) beyond 5G blockchain Deep Learning Internet of Things (IoT) Machine Learning metaheuristics algorithms Sixth Generation (6G) wireless communication smart city

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Non-Linear Regression Models with Vibration Amplitude Optimization algorithms in a Microturbine

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SENSORS 2022年第1期22卷 130-130页

作者： Rodriguez-Abreo, Omar Rodriguez-Resendiz, Juvenal Montoya-Santiyanes, L. A. alvarez-Alvarado, Jose Manuel Univ Politecn Queretaro Ind Technol Div El Marques 76240 Mexico Red Invest OAC Optimizac Automatizac & Control El Marques 76240 Mexico Univ Autonoma Queretaro Fac Ingn Queretaro 76010 Mexico

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85-93%);the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

关键词： machine diagnosis mechanical sensors vibration non-linear model grey wolf optimizer (GWO) metaheuristics algorithms

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Optimizing the Scheduling of Teaching Activities in a Faculty

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APPLIED SCIENCES-BASEL 2024年第20期14卷 9554页

作者： Diallo, Francis Patrick Tudose, Catalin Natl Univ Sci & Technol POLITEHN Bucharest Fac Automat Control & Comp Bucharest 060042 Romania Luxoft Romania Bucharest 020335 Romania

To maximize resource usage, minimize disputes, and improve academic experience, professors must schedule teaching activities efficiently. This study provides an optimized automated schedule creation technique. The system generates schedules that aim to be conflict-free and efficient, utilizing evolutionary algorithms along with multi-objective optimization. Resource usage, scheduling problems, and faculty/student satisfaction are the goals of the research. The system optimizes scheduling based on room limitations, instructor availability, and student preferences. The project uses system design, model creation, algorithm implementation, and performance analysis to solve the difficult timetable-generating problem. This research should save administrators time, improve academic operations, and improve staff and student academic experiences. Scalability and flexibility allow the system to be used in multiple faculties and incorporate new limits and requirements. This paper presents a complete approach to faculty scheduling, including insights and recommendations for future study and application in educational institutions.

关键词： automatic timetable generation faculty scheduling metaheuristics algorithms resource utilization scheduling conflicts scalability Timefold

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Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm

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RESULTS IN ENGINEERING 2024年 24卷

作者： Islam, Muhammad Shafiqul Ahmad, Mohd Ashraf Univ Malaysia Pahang Al Sultan Abdullah Fac Elect & Elect Engn Technol Pekan 26600 Pahang Malaysia Univ Malaysia Pahang Al Sultan Abdullah Ctr Adv Ind Technol Pekan 26600 Pahang Malaysia

This research introduces the improved Archimedes optimization algorithm (IAOA) for data-driven modeling of continuous-time Hammerstein models with missing data. It addresses the limitations of the original Archimedes optimization algorithm (AOA) through two key modifications: rebalancing the exploration and exploitation phases and mitigating local optima trapping issues. The primary focus is on developing a novel data-driven approach for modeling continuous-time Hammerstein models, particularly in the presence of missing output data. Four levels of missing measurement data (5 %, 15 %, 35 %, and 50 %) were considered, with data points randomly replaced with zeros. Models were tested with both complete and missing output data to evaluate the robustness of the IAOA-based method. The proposed based method identified linear and nonlinear subsystem variables in a continuous-time Hammerstein model leveraging input and output data, validated through two practical experiments: a Twin Rotor System and an Electromechanical Positioning System. The performance was assessed by examining various factors, including the convergence curve of the fitness function and its statistical analysis, responses in the frequency and time domains, Wilcoxon's rank-sum test, and computational time. Across all experiments, the IAOA-based method demonstrated superior performance compared to AOA and other methods, including a hybrid approach combining the average multi-verse optimizer and sine cosine algorithm, particle swarm optimizer, the sine cosine algorithm, multi-verse optimizer and grey wolf optimizer. The findings showed that the proposed IAOA-based method delivered highly accurate and consistent solutions, proving it to be the most effective and reliable method compared to the others assessed.

关键词： Data-driven Hammerstein modeling metaheuristics algorithms Data-driven control with missing data Archimedes optimization algorithm Twin rotor system Electrical-mechanical positioning system

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