Landmark-Selection Optimization Method for Autonomous Optical Planetary Landing Navigation Systems Using a Relaxation Optimization Algorithm

2021 ◽  
pp. 528-540
Author(s):  
Bowen Hou ◽  
Dayi Wang ◽  
Chao Xu ◽  
Jiongqi Wang ◽  
Wenbo Li ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Method ◽  
Navigation Systems ◽  
Planetary Landing ◽  
Landmark Selection ◽  
Landing Navigation

An Optimization Method for Optimal Engineering Structure Design Combining Intelligent Optimization Algorithm and CAE Code

2011 ◽  
Vol 399-401 ◽  
pp. 2296-2300
Author(s):  
Wen Jie Peng ◽  
Rui Ge ◽  
Ming Kai Gu
Keyword(s):  
Analytical Solution ◽  
Normal Stress ◽  
Optimization Algorithm ◽  
Optimization Method ◽  
Structure Design ◽  
Stacking Sequence ◽  
Intelligent Optimization ◽  
Engineering Structure ◽  
Intelligent Optimization Algorithm ◽  
Computer Aided

This paper presents an optimization method for optimal engineering structure design. An interface procedure is essentially developed to combine the intelligent optimization algorithm and computer aided engineering (CAE) code. An optimization example is carried out to minimize the interlaminar normal stress of a laminate which affect the delamination failure of a laminate via arranging the stacking sequence. The analytical solution is calculated to validate the accuracy of optimization results.

A general trajectory optimization method for aircraft taxiing on flight deck of carrier

2018 ◽  
Vol 233 (4) ◽  
pp. 1340-1353 ◽  
Author(s):  
Yu Wu ◽  
Ning Hu ◽  
Xiangju Qu
Keyword(s):  
Optimization Algorithm ◽  
Trajectory Optimization ◽  
Fitness Function ◽  
Control Variable ◽  
Optimization Method ◽  
Operation Efficiency ◽  
Ground Motion Model ◽  
Aircraft System ◽  
Chicken Swarm Optimization ◽  
Flight Deck

Enhancing operation efficiency of flight deck has become a hotspot because it has an important impact on the fighting capacity of the carrier–aircraft system. To improve the operation efficiency, aircraft need taxi to the destination on deck with the optimal trajectory. In this paper, a general method is proposed to solve the trajectory optimization problem for aircraft taxiing on flight deck considering that the existing methods can only deal with the problem in some specific cases. Firstly, the ground motion model of aircraft, the collision detection strategy and the constraints are included in the mathematical model. Then the principles of the chicken swarm optimization algorithm and the generality of the proposed method are explained. In the trajectory optimization algorithm, several strategies, i.e. generation of collocation points, transformation of control variable, and setting of segmented fitness function, are developed to meet the terminal constraints easier and make the search efficient. Three groups of experiments with different environments are conducted. Aircraft with different initial states can reach the targets with the minimum taxiing time, and the taxiing trajectories meet all the constraints. The reason why the general trajectory optimization method is validated in all kinds of situations is also explained.

Derivative-Free Hybrid Optimization Method for Top Design of Satellite System

2011 ◽  
Vol 308-310 ◽  
pp. 2413-2417
Author(s):  
Ying Guo Chen ◽  
Shuai Lu ◽  
Xiao Lu Liu ◽  
Ying Wu Chen
Keyword(s):  
Optimization Algorithm ◽  
Gaussian Processes ◽  
Design Space ◽  
Optimization Method ◽  
Satellite System ◽  
Pattern Search ◽  
Hybrid Optimization ◽  
Hybrid Optimization Algorithm ◽  
Hybrid Optimization Method ◽  
Derivative Free

This paper combines a derivative-free hybrid optimization algorithm, generalize pattern search (GPS), with Treed Gaussian Processes (TGP) to create a new hybrid optimization algorithm. The goal is to use the method for top design of satellite system, in which the objective or constraint functions usually are computationally expensive black-box functions. TGP model partitions the design space into disjoint regions, and employs independent Gaussian Processes (GP) in each partition to represent the time consumption of true problem responses. Utilizing the TGP, we generate the new “promising” points, which are the combination of model-predicted values and estimated model errors. Then, these points are used to guide GPS search in the design space efficiently. The hybrid optimization method is applied to top design of multi-satellites cooperated observation. The results demonstrate that the proposed method can not only increase the chance of obtaining optimal solution but also cut down the cost of function evaluations.

Application of Parallel Chaos Optimization Algorithm for Plug-in Hybrid Electric Vehicle Design

2014 ◽  
Vol 24 (01) ◽  
pp. 1450001 ◽  
Author(s):  
Xiaolan Wu ◽  
Guifang Guo ◽  
Jun Xu ◽  
Binggang Cao
Keyword(s):  
Optimization Algorithm ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Optimization Problem ◽  
Optimization Method ◽  
Vehicle Performance ◽  
Chaos Optimization ◽  
Performance Requirements ◽  
Chaos Optimization Algorithm ◽  
Hybrid Electric

Plug-in hybrid electric vehicles (PHEVs) have been offered as alternatives that could greatly reduce fuel consumption relative to conventional vehicles. A successful PHEV design requires not only optimal component sizes but also proper control strategy. In this paper, a global optimization method, called parallel chaos optimization algorithm (PCOA), is used to optimize simultaneously the PHEV component sizes and control strategy. In order to minimize the cost, energy consumption (EC), and emissions, a multiobjective nonlinear optimization problem is formulated and recast as a single objective optimization problem by weighted aggregation. The driving performance requirements of the PHEV are considered as the constraints. In addition, to evaluate the objective function, the optimization process is performed over three typical driving cycles including Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), and New European Driving Cycle (NEDC). The simulation results show the effectiveness of the proposed approach for reducing the fuel cost, EC and emissions while ensuring that the vehicle performance has not been sacrificed.

Shuffled shepherd optimization method: a new Meta-heuristic algorithm

2020 ◽  
Vol 37 (7) ◽  
pp. 2357-2389 ◽  
Author(s):  
Ali Kaveh ◽  
Ataollah Zaerreza
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Algorithm ◽  
Design Methodology ◽  
Optimization Method ◽  
Optimization Process ◽  
Heuristic Optimization ◽  
Benchmark Problems ◽  
Content Type

Purpose This paper aims to present a new multi-community meta-heuristic optimization algorithm, which is called shuffled shepherd optimization algorithm (SSOA). In this algorithm. Design/methodology/approach The agents are first separated into multi-communities and the optimization process is then performed mimicking the behavior of a shepherd in nature operating on each community. Findings A new multi-community meta-heuristic optimization algorithm called a shuffled shepherd optimization algorithm is developed in this paper and applied to some attractive examples. Originality/value A new metaheuristic is presented and tested with some classic benchmark problems and some attractive structures are optimized.

scholarly journals An improved particle swarm optimization algorithm for dynamic analysis of chain drive based on multidisciplinary design optimization

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401982961
Author(s):  
Mengjiang Chai ◽  
Yongliang Yuan ◽  
Wenjuan Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Method ◽  
Drive System ◽  
Multidisciplinary Design ◽  
Chain Drive ◽  
Swarm Optimization

Chain drive is one of the most commonly used mechanical devices in the main equipment transmission system. In the past decade, scholars focused on basic performance research, but ignore its best performance. In this study, due to the large vibration of the chain drive in the transmission system, the vibration performance and optimization parameters are also considered as a new method to design the chain drive system to obtain the best performance of the chain drive system. This article proposes a new method and takes a chain drive design as a case based on the multidisciplinary design optimization. The system optimization objective and sub-systems are established by the multidisciplinary design optimization method. To obtain the best performance for the chain, the chain drive is executed by an improved particle swarm optimization algorithm. Dynamic characteristics of the chain drive system are simulated based on the multidisciplinary design optimization results. The impact force of the chain links, vibration displacement, and the vibration frequency are analyzed. The results show that the kinematics principle of the chain drive and the optimal parameter value are obtained based on the multidisciplinary design optimization method.

scholarly journals A New Optimization Algorithm Based on Search and Rescue Operations

2019 ◽  
Vol 2019 ◽  
pp. 1-23 ◽  
Author(s):  
Amir Shabani ◽  
Behrouz Asgarian ◽  
Saeed Asil Gharebaghi ◽  
Miguel A. Salido ◽  
Adriana Giret
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Search And Rescue ◽  
Rank Test ◽  
Engineering Design Problems ◽  
Continuous Optimization Problems

In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.

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