Test Task Method for Multi-Spacecraft Based on Multi-level Coding Genetic Algorithm

In this paper, a multi-level method has been adopted to optimize the holes machining process with genetic algorithm (GA). Based on the analyzing of the features of the part with multi-holes, the local optimal processing route for the holes with the same processing feature is obtained with GA, then try to obtain the global optimal route with GA by considering the obtained local optimal route and the holes with different features. That is what the multi-level method means. The optimal route means the minimum moving length of the cutting tool and the minimum changing times of the cutting tool. The experiment is carried out to verify the algorithm and the proposed method, and result indicates that with GA and using the multi-level method the optimal holes machining route can be achieved efficiently.

Download Full-text

Fluid Genetic Algorithm (FGA)

Journal of Computational Design and Engineering ◽

10.1016/j.jcde.2017.03.001 ◽

2017 ◽

Vol 4 (2) ◽

pp. 158-167 ◽

Cited By ~ 12

Author(s):

Ruholla Jafari-Marandi ◽

Brian K. Smith

Keyword(s):

Genetic Algorithm ◽

Success Rate ◽

Optimization Problems ◽

Engineering Problem ◽

Review Of The Literature ◽

Learning Rates ◽

New Concepts ◽

Convergence Control ◽

Multi Level ◽

Paper Method

Abstract Genetic Algorithm (GA) has been one of the most popular methods for many challenging optimization problems when exact approaches are too computationally expensive. A review of the literature shows extensive research attempting to adapt and develop the standard GA. Nevertheless, the essence of GA which consists of concepts such as chromosomes, individuals, crossover, mutation, and others rarely has been the focus of recent researchers. In this paper method, Fluid Genetic Algorithm (FGA), some of these concepts are changed, removed, and furthermore, new concepts are introduced. The performance of GA and FGA are compared through seven benchmark functions. FGA not only shows a better success rate and better convergence control, but it can be applied to a wider range of problems including multi-objective and multi-level problems. Also, the application of FGA for a real engineering problem, Quadric Assignment Problem (AQP), is shown and experienced. Highlights This work presents a novel Genetic Algorithm alteration. Chromosome concept and structure in FGA is more similar to the real genetic world. FGA comprises global and individual learning rates. We show FGA enjoys higher success rate, and better convergence control.

Download Full-text

Behaviour of Multi-Level Selection Genetic Algorithm (MLSGA) using different individual-level selection mechanisms

Swarm and Evolutionary Computation ◽

10.1016/j.swevo.2018.09.005 ◽

2019 ◽

Vol 44 ◽

pp. 852-862 ◽

Cited By ~ 2

Author(s):

Przemyslaw A. Grudniewski ◽

Adam J. Sobey

Keyword(s):

Genetic Algorithm ◽

Individual Level ◽

Multi Level ◽

Selection Mechanisms

Download Full-text

Design optimisation for cold-formed steel residential roof truss using genetic algorithm

World Journal of Engineering ◽

10.1108/wje-10-2017-0322 ◽

2018 ◽

Vol 15 (5) ◽

pp. 575-583

Author(s):

Ka Yee Kok ◽

Hieng Ho Lau ◽

Thanh Duoc Phan ◽

TIina Chui Huon Ting

Keyword(s):

Genetic Algorithm ◽

Industrial Application ◽

Steel Structures ◽

Single Type ◽

Efficient Design ◽

Design Optimisation ◽

Content Type ◽

Roof Truss ◽

Cold Formed Steel ◽

Multi Level

Purpose This paper aims to present the design optimisation using genetic algorithm (GA) to achieve the highest strength to weight (S/W) ratio, for cold-formed steel residential roof truss. Design/methodology/approach The GA developed in this research simultaneously optimises roof pitch, truss configurations, joint coordinates and applied loading of typical dual-pitched symmetrical residential roof truss. The residential roof truss was considered with incremental uniform distributed loading, in both gravitational and uplift directions. The structural analyses of trusses were executed in this GA using finite element toolbox. The ultimate strength and serviceability of trusses were checked through the design formulation implemented in GA, according to the Australian standard, AS/NZS 4600 Cold-formed Steel Structures. Findings An optimum double-Fink roof truss which possess highest S/W ratio using GA was determined, with optimum roof pitch of 15Â°. The optimised roof truss is suitable for industrial application with its higher S/W ratio and cost-effectiveness. The combined methodology of multi-level optimisation and simultaneous optimisation developed in this research could determine optimum roof truss with consistent S/W ratio, although with huge GA search space. Research limitations/implications The sizing of roof truss member is not optimised in this paper. Only single type of cold-formed steel section is used throughout the whole optimisation. The design of truss connection is not considered in this paper. The corresponding connection costs are not included in the proposed optimisation. Practical implications The optimum roof truss presented in this paper is suitable for industrial application with higher S/W ratio and lower cost, in either gravitational or uplift loading configurations. Originality/value This research demonstrates the approaches in combining multi-level optimisation and simultaneous optimisation to handle large number of variables and hence executed an efficient design optimisation. The GA designed in this research determines the optimum residential roof truss with highest S/W ratio, instead of lightest truss weight in previous studies.

Download Full-text