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Grasshoppera* optimization algorithm for Multi-objective optimization of Multi-pass Face Milling of Polyamide (PA6)

SAE INTERNATIONAL JOURNAL OF MATERIALS AND MANUFACTURING 2023年第4期16卷 379-386页

作者： Laouissi, Aissa Abderazek, Hammoudi Nouioua, Mourad Sadiq, M. Sait Mech Res Ctr CRM Constantine Algeria King Fahd Univ Petr & Minerals Dept Comp Engn Dhahran Saudi Arabia Mech Res Ctr CRM BP 73B Ain El Bey 25021 Constantine Algeria

Milling is a prevalent machining technique employed in various industries for the production of metallic and non-metallic components. This article focuses on the optimization of cutting parameters for polyamide (PA6) using carbide tools, utilizing a recently developed multi-objective, nature -inspired metaheuristic algorithm known as the Multi-Objective Grasshoppera* optimization algorithm (MOGOA). This optimization process's primary objectives are minimizing surface roughness and maximizing the material removal rate. By employing the MOGOA algorithm, the study demonstrates its efficacy in successfully optimizing the cutting parameters. This research's findings highlight the MOGOA algorithm's capability to effectively fine-tune cutting parameters during PA6 machining, leading to improved outcomes in terms of surface roughness reduction and enhanced material removal rate.

关键词： Machining Multi-pass face milling Metaheuristic algorithms Grasshopper optimization algorithm

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Mobile robot path planning method combined improved artificial potential field witha* optimization algorithm

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High Technology Letters 2011年第2期17卷 160-165页

作者：赵杰 Yu Zhenzhong Yan Jihong Gao Yongsheng Chen Zhifeng State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150080 P. R. China

To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method combining the IAPFM witha* optimization algorithm （trust region algorithm） is proposed. Attractive force between the robot and the target location, and repulsive force between the robot and the obstacles are both converted to the potential field intensity; and filled potential field is used to guide the robot to go out of the local minimum points ; on this basis, the effect of dynamic obstacles velocity and the robot＇s velocity is consid thers and the IAPFM is established, then both the expressions of the attractive potential field and the repulsive potential field are obtained. The trust region algorithm is used to search the minimum value of the sum of all the potential field inten- sities within the movement scope which the robot can arrive in a sampling period. Connecting of all the points which hare the minimum intensity in every sampling period constitutes the global optimization path. Experiment result shows that the method can meet the real-time requirement, and is able to execute the mobile robot path planning task effectively in the dynamic environment.

关键词： trust region optimization algorithm path planning artificial potential field mobile robot potential field intensity

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An adaptive heuristica* optimization algorithm based on politics

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2017年第1期11卷 127-136页

作者： Saif, Seyed Mojtaba Islamic Azad Univ Safashahr Branch Dept Comp Engn Safashar Iran

Heuristic optimization is used to tune parameters in various scientific fields. Therefore, a successfula* optimization algorithm that must be able to evolve in a parsimonious manner in many situations is necessary. Often, heuristica* optimization algorithms are inspired by nature but imperialist competitive algorithm, inspired by the laws and policies governing human society, was presented in recent decade. Imperialist competitive algorithm was applied in various fields. This algorithm had a very good performance compared to othera* optimization algorithms. For this reason, this study tries to modify imperialist competitive algorithm for improve the accuracy and performance of the algorithm. Assimilation operator of the imperialist competitive algorithm is modified. The main motivation of this work is to introduce a powerful heuristica* optimization algorithm. A comparison between the proposed imperialist competitive algorithm framework and several versions imperialist competitive algorithm on 6 standard numerical benchmarks and four famousa* optimization algorithm indicate that the proposed algorithm has a good performance on a wide variety of problems.

关键词： Evolutionary algorithm optimization algorithm imperialist competitive algorithm assimilation policy

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Predicting density log from well log using machine learning techniques and heuristica* optimization algorithm:A comparative study

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Petroleum Research 2024年第2期9卷 176-192页

作者： Mehdi Rahmati Ghasem Zargar Abbas Ayatizadeh Tanha National Iranian Drilling Company Well Logging DepartmentAhwazIran Faculty Member of Petroleum University of Technology Iran Department of Petroleum Engineering Tarbiat Modares UniversityTehranIran

In the petroleum industry,the analysis of petrophysical parameters is critical for efficient reservoir management,production optimization,development strategies,and accurate hydrocarbon reserve *** recent years,the integration of machine learning methodologies has revolutionized the field,addressing challenges in geology,geophysics,and petroleum engineering,even when confronted with limited or imperfect *** study focuses on the prediction of density logs,a pivotal factor in evaluating reservoir hydrocarbon *** is important to note that during well logging operations,log data for specific depths of interest may be missing or incorrect,presenting a significant *** tackle this issue,we employed the Adaptive Neuro-Fuzzy Inference System(ANFIS)and Artificial Neural Networks(ANN)in combination with advanceda* optimization algorithms,including Particle Swarm optimization(PSO),Imperialist Competitive algorithms(ICA),and Genetic algorithms(GA).These methods exhibit promising performance in predicting density logs from gamma-ray,neutron,sonic,and photoelectric log ***,our results highlight that the Genetic algorithms-based Artificial Neural Network(GA-ANN)approach outperforms all other methods,achieving an impressive Mean Squared Error(MSE)of *** comparison,ANFIS records an MSE of 0.0015,ICA-ANN 0.0090,PSO-ANN 0.0093,and ANN 0.0183.

关键词： Density log Machine learning approaches Artificial neural networks(ANN) Adaptive neuro-fuzzy inference system(ANFIS) optimization algorithm

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Flight Conflict Resolution Simulation Study Based on the Improved Fruit Flya* optimization algorithm

IEEE JOURNAL ON MINIATURIZATION FOR AIR AND SPACE SYSTEMS

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IEEE JOURNAL ON MINIATURIZATION FOR AIR AND SPACE SYSTEMS 2024年第3期5卷 200-209页

作者： Sun, Yulong Ding, Guoshen Zhao, Yandong Zhang, Renchi Wang, Wenjun North Automat Control Technol Inst Software Dept Taiyuan 030006 Peoples R China

Due to the increasingly widespread application of unmanned aerial vehicle (UAV), the study of flight conflict resolution can effectively avoid the collision of different UAVs. First, describe flight conflict resolution as an optimization problem. Second, the improved fruit flya* optimization algorithm (IFOA) is proposed. The smell concentration judgment is equal to the coordinate instead of the reciprocal of the distance in order to make the variable accessible to be negative and occur with equal probability in the defined domain. Next, introduce the limited number of searches of the Artificial Bee Colony algorithm to avoid falling into the local optimum. Meanwhile, generate a direction and distance of the fruit fly individual through roulette. Finally, the effectiveness of the algorithm is demonstrated by computational experiments on 18 benchmark functions and the simulation of the flight conflict resolution of two and four UAVs. The results show that compared with the standard fruit flya* optimization algorithm, the IFOA has superior global convergence ability and effectively reduces the delay distance, which has important potential in flight conflict resolution.

关键词： optimization Autonomous aerial vehicles Statistics Sociology Standards Safety Military aircraft Flight conflict resolution fruit flya* optimization algorithm (FOA) optimization algorithm path planning unmanned aerial vehicle (UAV)

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Detection and parameter estimation of R peaks in ECG signal usinga* optimization algorithm 6

Detection and parameter estimation of R peaks in ECG signal ...

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6th International Symposium on Computing and Networking (CANDAR) - Across Practical Development and Theoretical Research

作者： Hsieh, Sheng-Ta Lin, Chun-Ling Oriental Inst Technol Dept Commun Engn New Taipei Taiwan Ming Chi Univ Technol Dept Elect Engn New Taipei Taiwan

ISBN: (纸本)9781538691847

Accurate R-peak detection is very important for arrhythmia diagnosis. Our previous effective R detection algorithm consisted of three strategies: band-pass filter, adaptive definition of interesting block and dynamic threshold. Then, it adopted thea* optimization algorithm to replace the knowledge-based theory and found out the suitable parameters (El, F2, N, WI, W2, beta and mu) in R detection algorithm quickly and obtained the high performance of detecting R peaks (99.77%). In order to improve the performance of the previous study, this study proposes to add the median filter in the algorithm to correct baseline wander components of electrocardiography (ECG) signals. It is necessary to defined two parameters (Ti and T2) in median filter. Therefore, this study adopts particle swarm optimization (PSO) to find the suitable parameters (Ti, T2, F1, F2, N, W1, W2, beta and mu) in the proposed method. The proposed method is applied to MIT-BIH arrhythmia database. The results show that PSO can find out the suitable parameters in R detection algorithm and have a higher accuracy (99.95%) than one of the previous study.

关键词： R-peak detection arrhythmia diagnosis optimization algorithm knowledge-based theory electrocardiography (ECG) particle swarm optimization (PSO) MIT-BIH arrhythmia database

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A Metaheuristica* optimization algorithm for Flight Assignment to the North Atlantic Organized Track System

A Metaheuristic Optimization Algorithm for Flight Assignment...

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AIAA Aviation Forum and Exposition

作者： Izadi, Arman Trani, Antonio A. Virginia Polytech Inst & State Univ Dept Civil & Environm Engn 200 Patton Hall Blacksburg VA 24061 USA Virginia Polytech Inst & State Univ Dept Civil & Environm Engn 200 Patton Hall Blacksburg VA 24061 USA

ISBN: (数字)9781624106101

ISBN: (纸本)9781624106101

The North Atlantic region is the busiest oceanic airspace controlled by the United States. To better serve the two traffic flows between Europe and North America, two Organized Track Systems (OTS) are created on a daily basis to accommodate oceanic flights. This paper studies the potential benefits of a novel method for assigning flights to the OTS in the North Atlantic airspace. A mathematical model for the group assignment procedure is provided in this study, and a metaheuristica* optimization algorithm is proposed for the solution methodology. The proposed algorithm is validated with a small size problem verifying it can find the global optimal in reasonable computational time. The validateda* optimization algorithm is implemented inside a fast-time flight simulation tool, called Global Oceanic Model. This study used the real-scale flight traffic related to three consecutive days in June 2016. The simulation results show the operational benefits of the proposed assignment procedure in terms of efficiency, level of service, and airline equity.

关键词： optimization algorithm North Atlantic Airspace Mathematical Models Flight Simulation Flight Level Controller Pilot Data Link Communications Mach Numbers Fuel Consumption Annealing Process Air Navigation Service Provider

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A Mixed Integer Efficient Globala* optimization algorithm with Multiple Infill Strategy - Applied to a Wing Topology optimization Problem

A Mixed Integer Efficient Global Optimization Algorithm with...

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AIAA Science and Technology (SciTech) Forum and Exposition

作者： Roy, Satadru Crossley, William A. Stanford, Bret K. Moore, Kenneth T. Gray, Justin S. Purdue Univ Aeronaut & Astronaut 701 W Stadium Ave W Lafayette IN 47907 USA NASA Langley Res Ctr Aeroelastic Branch 218 Dodd Blvd Hampton VA 23681 USA DB Consulting Grp Inc Cleveland OH 44135 USA NASA Glenn Res Ctr Cleveland OH 44135 USA

ISBN: (数字)9781624105784

ISBN: (纸本)9781624105784

With the advancement in high performance computing and numerical optimization techniques, engineering design optimization problems are becoming more complex, larger scale, higher fidelity, and computationally more demanding, requiring longer run times than ever before. There exists methodologies and techniques that can address some of these challenges but very few can address all, and most are limited in the extent that these concerns can be addressed. With the goal of addressing such challenging engineering problems, we developed a newa* optimization algorithm, named AMIEGO, that combines concepts from surrogate-based optimization approaches, gradient-based numerical methods, Partial Least Squares, evolutionary algorithms, and Branch-and-Bound, providing newer capabilities that were not previously perceived. The effort here builds upon this previously developeda* optimization algorithm to include multiple infill sampling capability that combines the concept of generalized expected improvement function, unsupervised learning, and multi-objective evolutionary technique. To demonstrate, AMIEGO with the multiple infill capability (called AMIEGO-MIMOS) solves a series of increasingly difficult engineering design optimization problems. The results reveal the performance of the new approach is problem dependent. When applied to a ten-bar truss problem, the newly proposed multiple infill strategy consistently leads to a better design solutions when compared to the existing CPTV method (implemented with the context of the AMIEGO algorithm). On the other hand, when applied to a mixed-integer high fidelity wing topology optimization problem - MIMOS, despite showing a steeper convergence at the start, eventually leads to an inferior solution as compared to CPTV approach. These results also reveal that a small number of starting points, in general, are sufficient to lead to a good overall solution.

关键词： optimization algorithm Numerical optimization Trusses Multi Objective Evolutionary algorithms High Performance Computing Surrogate Model Wingbox Kernel Functions Parallel Computing Aspect Ratio

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AOAB - Automateda* optimization algorithm Benchmarking 10

AOAB - Automated Optimization Algorithm Benchmarking

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12th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Weise, Thomas Niu, Li Tang, Ke Univ Sci & Technol China Nat Inspired Computat & Applicat Lab Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9781450300735

In this paper we present AOAB, the Automateda* optimization algorithm Benchmarking system. AOAB can be used to automatically conduct experiments with numericala* optimization algorithms by applying them to different benchmarks with different parameter settings. Based on the results, AOAB can automatically perform comparisons between different algorithms and settings. It can aid the researcher to identify trends for good parameter settings and to find which algorithms are suitable for which type of problem. We introduce the system structure of AOAB (the server and the graphical client interface), define the way in which optimizers and benchmark functions can be implemented for the use in AOAB, and conduct an illustrative example experiment with our system: a comparison between Random Search and two Hill Climbers.

关键词： AOAB Automated Benchmarking optimization algorithm Distribution Single-Objective optimization Numerical optimization

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A review on the recent application of Jaya* optimization algorithm 1

A review on the recent application of Jaya optimization algo...

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1st Annual International Conference on Information and Sciences (AiCIS)

作者： Alsajri, Mohammad Ismail, Mohd Arfian Salamn, Saba Abdul-baqi Univ Malaysia Pahang Fac Comp Syst & Software Engn Pahang Malaysia Al Iraqia Univ Dept Comp Sci Coll Educ Baghdad Iraq

ISBN: (纸本)9781538691885

Jaya is a new metaheuristic that in recent years, has been applied to numerous intractable optimization problems. The important difference between Jaya and othera* optimization algorithms is that Jaya does not require the tuning of its parameters (a process needed in the other algorithms to escape unwanted convergence). Another different is the efficiency of Jaya in always choosing the best solution. In this paper, a review of the recent application of Jaya algorithm in the field of optimization problem was reviewed.

关键词： Jaya optimization algorithm metaheuristic algorithm application of Jaya

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