hLCGA: A Hybrid Competitive Coevolutionary Genetic Algorithm

2006 ◽  
Author(s):  
Gregoire Danoy ◽  
Pascal Bouvry ◽  
Tomy Martins
Keyword(s):  
Genetic Algorithm ◽  
Coevolutionary Genetic Algorithm

Double Elite Coevolutionary Genetic Algorithm

2012 ◽  
Vol 23 (4) ◽  
pp. 765-775 ◽  
Author(s):  
Quan LIU ◽  
Xiao-Yan WANG ◽  
Qi-Ming FU ◽  
Yong-Gang ZHANG ◽  
Xiao-Fang ZHANG
Keyword(s):  
Genetic Algorithm ◽  
Coevolutionary Genetic Algorithm

scholarly journals Vibration-Based Damage Detection in Beams by Cooperative Coevolutionary Genetic Algorithm

2014 ◽  
Vol 6 ◽  
pp. 624949 ◽  
Author(s):  
Kittipong Boonlong
Keyword(s):  
Genetic Algorithm ◽  
Damage Detection ◽  
Cantilever Beam ◽  
Cross Section ◽  
Random Noise ◽  
Vibration Characteristics ◽  
Mode Shapes ◽  
Test Problems ◽  
Uniform Cross Section ◽  
Coevolutionary Genetic Algorithm

Vibration-based damage detection, a nondestructive method, is based on the fact that vibration characteristics such as natural frequencies and mode shapes of structures are changed when the damage happens. This paper presents cooperative coevolutionary genetic algorithm (CCGA), which is capable for an optimization problem with a large number of decision variables, as the optimizer for the vibration-based damage detection in beams. In the CCGA, a minimized objective function is a numerical indicator of differences between vibration characteristics of the actual damage and those of the anticipated damage. The damage detection in a uniform cross-section cantilever beam, a uniform strength cantilever beam, and a uniform cross-section simply supported beam is used as the test problems. Random noise in the vibration characteristics is also considered in the damage detection. In the simulation analysis, the CCGA provides the superior solutions to those that use standard genetic algorithms presented in previous works, although it uses less numbers of the generated solutions in solution search. The simulation results reveal that the CCGA can efficiently identify the occurred damage in beams for all test problems including the damage detection in a beam with a large number of divided elements such as 300 elements.

scholarly journals Optimization of DP-M-QAM Transmitter Using Cooperative Coevolutionary Genetic Algorithm

2018 ◽  
Vol 36 (12) ◽  
pp. 2450-2462 ◽  
Author(s):  
Julio Cesar Medeiros Diniz ◽  
Francesco Da Ros ◽  
Edson Porto da Silva ◽  
Rasmus Thomas Jones ◽  
Darko Zibar
Keyword(s):  
Genetic Algorithm ◽  
Coevolutionary Genetic Algorithm

Nesting Scheduling in Sheet Metal Processing Based on Coevolutionary Genetic Algorithm in Different Environments

2018 ◽  
Vol 12 (5) ◽  
pp. 730-738 ◽  
Author(s):  
Tatsuhiko Sakaguchi ◽  
◽  
Kohki Matsumoto ◽  
Naoki Uchiyama
Keyword(s):  
Genetic Algorithm ◽  
Sheet Metal ◽  
Processing Time ◽  
Factors Affecting ◽  
Fitness Value ◽  
Evolutionary Genetic ◽  
Scheduling Method ◽  
Grouping Method ◽  
Metal Processing ◽  
Coevolutionary Genetic Algorithm

In sheet metal processing, nesting and scheduling are important factors affecting the efficiency and agility of manufacturing. The objective of nesting is to minimize the waste of material, while that of scheduling is to optimize the processing sequence. As the relation between them often becomes a trade-off, they should be considered simultaneously for the efficiency of the total manufacturing process. In this study, we propose a co-evolutionary genetic algorithm-based nesting scheduling method. We first define a cost function as a fitness value, and then we propose a grouping method that forms gene groups based on the processing layout and processing time. Finally, we validate the effectiveness of the proposed method through computational experiments.

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