scholarly journals Integrated Optimization of Pipe Routing and Clamp Layout for Aeroengine Using Improved MOALO

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qiang Liu ◽  
Zhi Tang ◽  
Huijuan Liu ◽  
Jiapeng Yu ◽  
Hui Ma ◽  
...  
Keyword(s):  
Optimization Method ◽  
Search Performance ◽  
Pareto Solution ◽  
Integrated Optimization ◽  
Decision Variables ◽  
Pipe Routing ◽  
Constraint Problem ◽  
Complex Engineering ◽  
Ant Lion ◽  
Assembly Constraints

Pipe routing and clamp layout for aeroengine are NP-hard computational problems and complex engineering design processes. Besides space constraints and engineering rules, there are assembly constraints between pipes and clamps, which usually lead to repeatedly modifications between pipe routing and clamp layout designs. In order to solve the problems of assembly constraints and design coupling between them, an integrated optimization method for pipe routing and clamp layout is proposed. To this end, the MOALO (multiobjective ant lion optimizer) algorithm is modified by introducing the levy flight strategy to improve the global search performance and convergence speed, and it is further used as a basic computation tool. The integrated optimization method takes pipe and clamp as a whole system and then solves the Pareto solution set of pipe-clamp layouts by using improved MOALO, where the pipe path, clamp position, and rotation angle are selected as decision variables and are further optimized. Inspired by engineering experience, a clamp-based pipe path mechanism considering regular nodes is established to deal with assembly constraint problem. The proposed method comprehensively considers engineering rules of pipe routing and clamp layout and realizes the overall layout optimization of pipe-clamp system while guaranteeing the assembly constraints between pipes and clamps. Finally, some numerical computations and routing examples are conducted to demonstrate the feasibility and effectiveness of the proposed method.

scholarly journals Improved Backstepping Control of a DFIG based Wind Energy Conversion System using Ant Lion Optimizer Algorithm

2021 ◽  
Author(s):  
Zoubir Zeghdi ◽  
Linda Barazane ◽  
Youcef Bekakra ◽  
Abdelkader Larabi
Keyword(s):  
Fitness Function ◽  
Harmonic Distortion ◽  
Absolute Error ◽  
Optimization Method ◽  
Backstepping Control ◽  
Ant Lion Optimizer ◽  
Integral Time ◽  
Distortion Minimization ◽  
Ant Lion ◽  
Doubly Fed

In this paper, an improved Backstepping control based on a recent optimization method called Ant Lion Optimizer (ALO) algorithm for a Doubly Fed Induction Generator (DFIG) driven by a wind turbine is designed and presented. ALO algorithm is applied for obtaining optimum Backstepping control (BCS) parameters that are able to make the drive more robust with a faster dynamic response, higher accuracy and steady performance. The fitness function of the ALO algorithm to be minimized is designed using some indexes criterion like Integral Time Absolute Error (ITAE) and Integral Time Square Error (ITSE). Simulation tests are carried out in MATLAB/Simulink environment to validate the effectiveness of the proposed BCS-ALO and compared to the conventional BCS control. The results prove that the objectives of this paper were accomplished in terms of robustness, better dynamic efficiency, reduced harmonic distortion, minimization of stator powers ripples and performing well in solving the problem of uncertainty of the model parameter.

PMP-Based Optimization Method for Determining Airline Fleet Composition

2014 ◽  
Vol 505-506 ◽  
pp. 645-649
Author(s):  
Yu Wang
Keyword(s):  
Network Effects ◽  
Optimization Method ◽  
Numerical Example ◽  
Fleet Planning ◽  
Decision Variables ◽  
Proposed Model ◽  
Fleet Composition ◽  
Aircraft Type ◽  
Airline Fleet ◽  
The Impact

Traditional methods for determining airline fleet composition could not reflect the impact of network effects on fleet composition. To solve this problem for airlines operating in the mode of Hub & Spoke network, the passenger mix problem was incorporated into the model of determining airline fleet composition. The purchasing number of aircrafts in each fleet type, the frequencies of each aircraft type flying on legs and the spilling number of passengers from each itinerary were treated as decision variables. The limitations including maximum flying frequencies on each leg, available flying time each fleet type can provide and maximum passengers spilled from each flight leg were considered as constraints. A model to minimize the fleet planning cost was constructed. The numerical example shows that the fleet planning cost derived from this proposed model is 46266381.64 Yuan and reduces by 3914969.70 Yuan compared to the result from the traditional leg-based model. In hence, this proposed model is effective and feasible.

IFCIP: An integrated optimization method for planning filters in fluid power systems under uncertainty

2011 ◽  
Vol 43 (3) ◽  
pp. 329-348 ◽  
Author(s):  
S. L. Nie ◽  
Y. L. Zheng ◽  
Y. P. Li ◽  
S. Peng ◽  
G. H. Huang
Keyword(s):  
Power Systems ◽  
Optimization Method ◽  
Fluid Power ◽  
Integrated Optimization ◽  
Fluid Power Systems

Improved Integrated Optimization Method of Gasoline Blend Planning and Real-Time Blend Recipes

2016 ◽  
Vol 55 (16) ◽  
pp. 4632-4645 ◽  
Author(s):  
Kaixun He ◽  
Feng Qian ◽  
Hui Cheng ◽  
Wenli Du
Keyword(s):  
Real Time ◽  
Optimization Method ◽  
Integrated Optimization

scholarly journals Multi-objective Human-computer Co-operative Co-evolutionary Method Based on Non-dominated Sorting Strategy

2014 ◽  
Vol 8 (1) ◽  
pp. 213-217
Author(s):  
Huo Junzhou ◽  
Chen Jing ◽  
Zhou Jianjun ◽  
Wu Hanyang
Keyword(s):  
Numerical Simulation ◽  
Layout Problem ◽  
Pareto Solution ◽  
Trade Off ◽  
Multi Objective ◽  
Evolutionary Method ◽  
Complex Engineering ◽  
Cooperative Solutions ◽  
Simulation Results ◽  
The Individual

Based on the human-computer cooperation ideas, a Human-Computer Multi-Objective Cooperative Co-Evolutionary Method (HCMCCM) is developed to solve the complex engineering layout problem, in which the multiobjective optimization idea is integrated to avoid the "flooding" phenomenon that occurs during the combination of the artificial solutions and the algorithm solutions. In the proposed HCMCCM, the artificial solutions expressed by unified encoding strings are incorporated together with the algorithms solutions to create new cooperative solutions based on the non-dominated sorting strategies. This kind of cooperation can make the artificial solutions and the algorithm solutions on an equal basis and integrate the artificial individual with the individual algorithms into a multi-objective trade-off. The numerical simulation results of the satellite layout problem show that the proposed method can combine the artificial solutions and the algorithm solutions effectively and provide a Pareto solution set for engineers to choose from.

Integrated Optimization of Principal Dimensions Based on EEDI and Minimum Propulsion Power

2021 ◽  
Author(s):  
Yanxia Wang ◽  
Wenyu Sun ◽  
Qiang Zhao
Keyword(s):  
Energy Efficiency ◽  
Optimization Model ◽  
Evaluation Method ◽  
Science Research ◽  
Optimization Method ◽  
Power Reduction ◽  
Integrated Optimization ◽  
Calculation Results ◽  
Digital Pattern ◽  
Propulsion Power

Abstract An integrated optimization model of EEDI and minimum propulsion power has been established in this paper. EEDI optimization needs to meet IMO requirements for minimum propulsion power. Installed power reduction is one of the most effective way to optimize EEDI, but it will make the installed power lower than IMO requirements. From the view of security, it is not allowed. In order to coordinate the contradiction between the reduction of EEDI and the minimum propulsion power of the ship, this paper is devoted to the development of an effective and efficient EEDI optimization method under the minimum propulsion power constraints of the ship. The evaluation method of the objective function EEDI is a digital pattern of hydrodynamics performance for tanker series developed by the China Ship Science Research Center. In order to illustrate the method, the VLCC is selected as the research object, and Non-dominated Sorting Genetic Algorithms II is selected to optimize the EEDI. The calculation results show that energy efficiency has been optimized about 4%, so the EEDI and minimum propulsion power integrated optimization model are reasonable and effective.

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