Multiobjective optimization of tibial locking screw design using a genetic algorithm: Evaluation of mechanical performance

2006 ◽  
Vol 24 (5) ◽  
pp. 908-916 ◽  
Author(s):  
Ching-Chi Hsu ◽  
Ching-Kong Chao ◽  
Jaw-Lin Wang ◽  
Jinn Lin
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Mechanical Performance ◽  
Screw Design ◽  
Algorithm Evaluation ◽  
Locking Screw

scholarly journals Multiobjective optimization design of the solar field and reverse osmosis system with preheating feed water using Genetic algorithm

2018 ◽  
Vol 6 (6) ◽  
pp. 624-642 ◽  
Author(s):  
Iman Ebrahimi Ghoujdi ◽  
Hasti Hadiannasab ◽  
Mokhtar Bidi ◽  
Abbas Naeimi ◽  
Mohammad Hossein Ahmadi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Reverse Osmosis ◽  
Optimization Design ◽  
Feed Water ◽  
Solar Field

The Optimization Design of the Cross-Sectional Layout of an Umbilical Based on the Hybrid Genetic Algorithm

2021 ◽  
Author(s):  
Xu Yin ◽  
Zhixun Yang ◽  
Dongyan Shi ◽  
Jun Yan ◽  
Lifu Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cross Sections ◽  
Optimization Design ◽  
Mechanical Performance ◽  
Fitness Function ◽  
Hybrid Genetic Algorithm ◽  
Optimization Strategy ◽  
Cross Sectional ◽  
Great Probability ◽  
The Cross

Abstract The umbilical which consists of hydraulic tubes, electrical cables and optical cables is a key equipment in the subsea production system. Each components perform different physical properties, so different cross-sections will present different geometrical characteristic, carrying capacities, the cost and the ease of manufacture. Therefore, the cross-sectional layout design of the umbilical is a typical multi-objective optimization problem. A mathematical model of the cross-sectional layout considering geometric and mechanical properties is proposed, and the genetic algorithm is introduced to copy with the optimization model in this paper. A steepest descent operator is embedded into the basic genetic algorithm, while the appropriate fitness function and the selection operator are advanced. The optimization strategy of the cross-sectional layout based on the hybrid genetic algorithm is proposed with the fast convergence and the great probability for global optimization. Finally, the cross-section of an umbilical case is performed to obtain the optimal the cross-sectional layout. The geometric and mechanical performance of results are compared with the initial design, which verify the feasibility of the proposed algorithm.

scholarly journals An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Xiangmin Guan ◽  
Xuejun Zhang ◽  
Yanbo Zhu ◽  
Dengfeng Sun ◽  
Jiaxing Lei
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Network Flow ◽  
Optimization Problems ◽  
Parallel Evolution ◽  
Dynamic Adjustment ◽  
Cooperative Coevolution ◽  
Solution Quality ◽  
Optimization Framework ◽  
Flow Assignment

Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology.

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