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Optimizing die profiles using a hybrid optimization algorithm for the precise control of extrudate swell in polymer solutions

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JOURNAL OF NON-NEWTONIAN FLUID MECHANICS 2024年 330卷

作者： Maddah, Alireza Jafari, Azadeh Univ Tehran Coll Engn Sch Mech Engn POB 11155-4563 Tehran Iran

In recent years, many researchers have focused on improving the die design process for polymer extrusion. This study proposes the development of an efficient and robust numerical approach to improve the die-designing process of polymer melts using the Giesekus model. The proposed technique uses a hybrid optimization algorithm to systematically minimize an objective function to achieve the desired extrudate shape. First, we examine the proposed objective function for the 2D axisymmetric test case using the Golden Section optimization algorithm to obtain a circular extrudate of high-density polyethylene (HDPE) with the desired radius at moderate Weissenberg numbers from 1 to 3.75. To provide more insights into the viscoelastic nature of the problem, the optimization was repeated for a viscoelastic fluid with a higher viscosity ratio and a lower mobility factor at very high Weissenberg numbers, specifically 45, 60, 75, and 90. The proposed approach performs quite well across a broad range of Weissenberg numbers. Subsequently, a hybrid optimization algorithm that combines Nelder-Mead and Bayesian optimization algorithms is employed to achieve the desired extrudate shape for various extrudate profiles in 3D cases, including rectangular and square cross -sections, at a Weissenberg number of one. To gain additional insights into the viscoelastic nature of the problem, optimization was conducted for the rectangular extrudate with a 2:1 aspect ratio at higher Weissenberg numbers, i.e. Weissenberg number from 1 to 2.6. The results of the three-dimensional case studies indicate that both the Nelder-Mead and Bayesian optimization algorithms are efficient and robust, converging relatively quickly in all cases studied. The Nelder-Mead algorithm appears to be more robust, exhibiting fewer oscillations when reaching the optimum point. On the other hand, the Bayesian optimization algorithm can reach the global optimum point at a computational cost comparable to Nelder-Mead, while ac

关键词： Extrudate swell hybrid optimization algorithm Viscoelastic fluid flows Inverse problem

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hybrid optimization algorithm based on chaos game and differential evolution for constrained optimization of structures

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ENGINEERING optimization 2024年

作者： Zarbilinezhad, Milad Gholizad, Amin Univ Mohaghegh Ardabili Dept Civil Engn Ardebil Iran

A novel hybrid optimization algorithm, chaos game optimization-differential evolution (CGO-DE), is developed in this study for the size optimization of structures. The key contribution lies in the synergistic combination of chaos game optimization (CGO) and differential evolution (DE), using their respective strengths in exploration and exploitation to effectively identify global optima. The innovative approach independently applies CGO and DE to subpopulations, preventing premature convergence and enhancing overall performance while ensuring accuracy, accounting for design factors and respecting constraints. Comprehensive numerical evaluations on established structural benchmarks (three-bar truss, welded beam and cantilever beam) and large-scale structures (272-bar and 942-bar truss tower) demonstrate the versatility and efficiency of CGO-DE in solving complex optimization problems across various engineering applications. The results underscore the ability of the algorithm to achieve optimal solutions and improve designs compared to other optimization techniques, highlighting its potential as a powerful tool for engineering optimization.

关键词： hybrid optimization algorithm constrained engineering problems differential evolution (DE) chaos game optimization (CGO) global optima

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Nonlinear Control Allocation Using hybrid optimization algorithm

Nonlinear Control Allocation Using Hybrid Optimization Algor...

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International Conference on Mechatronics and Control (ICMC)

作者： Guo, Mengxu Chen, Mou Zhang, Hangyue Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479925384

A nonlinear control allocation scheme is developed using hybrid optimization algorithm. To achieve nonlinear control allocation results, a hybrid optimization algorithm is presented in which the ant colony algorithm and differential evolution algorithm are employed. The nonlinear control allocation problem is divided into two optimal problems which are selecting optimal truncation point combination problem and optimizing the corresponding section coefficients problem. Under this case, the actuators constraints are segmented into some intervals according to the rate limits and position boundary of actuators. The optimal combination of cutoff interval is given through ant colony algorithm searching, and the optimal combination of truncation points is obtained. On the basis of the optimal truncation points, the corresponding truncation point coefficients are optimized by using the differential evolution algorithm. Then, the actuator commands are obtained by the calculation results of truncation points and corresponding truncation point coefficients. Simulation results show that the developed control allocation method is effective and the control requirement can be achieved.

关键词： nonlinear control allocation ant colony algorithm differential evolution algorithm hybrid optimization algorithm

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The Application research for Ship Collision Avoidance with hybrid optimization algorithm

The Application research for Ship Collision Avoidance with H...

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IEEE International Conference on Information and Automation (ICIA)

作者： Liu, Hongdan Deng, Rong Zhang, Lanyong Harbin Engn Univ Dept Automat Harbin Heilongjiang Pr Peoples R China AnHui Jianghuai Automobile Co Ltd Human Resources Dept Personnel East Rd 176 Hefei Anhui Peoples R China

ISBN: (纸本)9781509041022

A hybrid optimization algorithm, including an improved bacterial foraging algorithm and particle swarm optimization with strong global searching ability and quick local convergence ability, is applied to optimize the route of ship collision avoidance. The ship collision avoidance simulation is done based on MATLAB simulation environment by means of a hybrid optimization algorithm in conjunction with a collision risk model and optimization function. And then, ship collision risk is analyzed;optimization collision avoidance parameters including avoidance collision angle, reorientation angle and time parameters are chosen and displayed, route of ship collision avoidance is generated. By summarizing the simulation diagram and the results of the analysis are effective and feasible. This research proposes a new idea for intelligent avoidance collision.

关键词： ship collision avoidance collision risk hybrid optimization algorithm collision avoidance simulation

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A soft computing based novel hybrid optimization algorithm H3PGAB3C and its application to routing in WMNs

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International Journal of Information Technology (Singapore) 2022年第5期14卷 2595-2602页

作者： Aneja, Rattan Deep Bindal, Amit Kumar Kumar, Shakti Department of CSE Maharishi Markandeshwar (Deemed to be University) Haryana Mullana India AICTE IDEA LAB Panipat Institute of Engineering and Technology Haryana Samalkha India

This paper proposes a novel hybridized optimization algorithm namely H3PGAB3C. This algorithm is a combination of the three-parent genetic algorithm (3PGA) and big-bang big-crunch (B3C) algorithm. 3PGA is used for global search whereas B3C performs local search for optimal solution. This hybrid algorithm is applied to find the optimal route in wireless mesh networks (WMNs). Performance of H3PGAB3C is compared with other 10 algorithms for different architectural scenarios of large-sized WMNs varying from 5000 to 10,000 node client WMNs. integrated link cost (ILC) measure is taken into consideration for performance comparison. ILC is evaluated by a fuzzy system consisting of three parameters;end-to-end delay, jitter and throughput. It has been observed that the proposed algorithm H3PGAB3C proved its superiority over all other 10 algorithms. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： 3PGA B3C H3PGAB3C hybrid optimization algorithm Soft computing WMN routing

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Trajectory Planning of a Six-DOF Robot Based on a hybrid optimization algorithm 9

Trajectory Planning of a Six-DOF Robot Based on a Hybrid Opt...

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9th International Symposium on Computational Intelligence and Design (ISCID)

作者： Jin, Xing Kang, Junfeng Zhang, Jingjing Yang, Xiang China Univ Geosci Sch Automat Wuhan Peoples R China

ISBN: (纸本)9781509035588

This work adopts a standard Denavit-Hartenberg method to model a PUMA 560 spot welding robot as the object of study. The forward and inverse kinematics solutions are then analyzed. To address the shortcomings of the ant colony algorithm, factors from the particle swarm optimization and the genetic algorithm are introduced into this algorithm. Subsequently, the resulting hybrid algorithm and the ant colony algorithm are used to conduct trajectory planning in the shortest path. Experimental data and simulation result show that the hybrid algorithm is significantly better in terms of initial solution speed and optimal solution quality than the ant colony algorithm. The feasibility and effectiveness of the hybrid algorithm in the trajectory planning of a robot are thus verified.

关键词： Six-DOF Robot robot trajectory planning ant colony algorithm hybrid optimization algorithm

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A Novel hybrid optimization algorithm Combined with BBO and PSO 28

A Novel Hybrid Optimization Algorithm Combined with BBO and ...

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28th Chinese Control and Decision Conference

作者： Cheng, Gang Lv, Chao Yan, Shi Xu, Li Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Akita Prefectural Univ Dept Elect & Informat Syst Akita 0150055 Japan

ISBN: (纸本)9781467397148

In this paper, a novel hybrid optimization algorithm named BBO-PSO is proposed, which is a combination of biogeography-based optimization (BBO) and particle swarm optimization (PSO). In this optimization algorithm, BBO will be employed for local search while PSO will be employed for global search, which makes the algorithm possess powerful search ability in the solution space. The proposed algorithm and 3 existing optimization algorithms are tested on 6 well known benchmarks and the simulation results show that BBO-PSO has better performance than the others.

关键词： hybrid optimization algorithm Particle Swarm optimization Biogeography-Based optimization

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An Efficient hybrid optimization algorithm Based on Lagged-start and Parallel Operation

An Efficient Hybrid Optimization Algorithm Based on Lagged-s...

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International Conference on Web Information Systems and Mining

作者： Yu Guofang Zhang Yujie China Univ Min & Technol Coll Informat & Elect Engn Xuzhou 221116 Jiangsu Prov Peoples R China Xuzhou AF Coll Dept Logist Command Xuzhou Peoples R China

ISBN: (纸本)9780769538174

Fast convergence-rate, low computation-complexity and good stability are important goals in the researching area of neural network learning algorithm. A kind of parallel computing lagged-start hybrid optimization algorithm is studied, it not only integrates the basic gradient-method and the unconstrained optimization algorithm to realize the supplement of their advantages, but also makes full use of the high-performance computer's parallel computing features to complete the algorithm switching from one to another on time, which improves the efficiency of algorithm learning and meets the neural network system's online learning or real-time control. Combined a typical test function, a Microsoft Visual C# program is edit for the performance testing and validation of the proposed algorithm, the results is satisfied as expected.

关键词： neural network hybrid optimization algorithm lagged-start parallel Operation

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A Novel hybrid optimization algorithm Based on Multi-agent and Particle Swarm

A Novel Hybrid Optimization Algorithm Based on Multi-agent a...

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International Conference on Components, Packaging and Manufacturing Technology

作者： Shi Dejia Jiang Weijin Ding Xiaoling Hunan Univ Commerce Sch Comp & Elect Engn Changsha 410205 Hunan Peoples R China

ISBN: (纸本)9780878492138

A novel multi-agent particle swarm optimization algorithm (MAI'SO) is proposed for optimal reactive power dispatch and voltage control of power system. The method integrates multi-agent system (MAS) and particle swarm optimization algorithm (PSO). An agent in *** represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice-point. In order to decrease fitness value, quickly, agents compete and cooperate with their neighbors. and they can also use knowledge. Making use of these agent interactions and evolution mechanism of ***. MAPSO realizes the purpose of minimizing the value of objective function. MAPSO applied for optimal reactive power is evaluated on an IEEE 30-bus power system. It is shown that the proposed approach converges to better solutions much faster than the earlier reported approaches

关键词： hybrid optimization algorithm multi-agent system particle swarm optimization algorithm

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A Novel hybrid optimization algorithm Combined with BBO and ACO 39

A Novel Hybrid Optimization Algorithm Combined with BBO and ...

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39th Chinese Control Conference (CCC)

作者： Dai, Zhuo Ma, Qian Zhao, Dongdong Yan, Shi Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

ISBN: (纸本)9789881563903

This paper proposes a novel hybrid optimization algorithm based biogeography-based optimization (BBO) and ant colony optimization (ACO). Specifically, BBO is utilized for preliminary search and then the final search is conducted by using ACO on the preliminary results, which makes the hybrid algorithm have powerful search ability in the solution space. Moreover, the algorithm is tested on 6 well-known benchmarks for comparing with the 4 others existing optimization algorithms, including 2 algorithms were proposed by the coauthor earlier. Obviously, the simulation results show that the proposed hybrid BBO-ACO has better performance than the existing others.

关键词： hybrid optimization algorithm Biogeography-Based optimization Ant Colony optimization

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