Genetic algorithms using multi-objectives(10)

发布时间:2021-06-07

We are interested in a job-shop scheduling problem corresponding to an industrial problem. Gantt diagram’s optimization can be considered as an NP-difficult problem. Determining an optimal solution is almost impossible, but trying to improve the current s

188A.Cardonetal./RoboticsandAutonomousSystems33(2000)

179–190

Fig.8.Evolutionoftheeconomicfunctionaccordingtothenumberofagentsandthenumberofgenetic

operations.

causedbythepossibleexposuretoexternaleventsoriginatingintheenvironmentandduringthegeneticcodereplicationphase.

Ifwealsointroducethenotionofmotivatedbe-haviorforagents[5],wegodeeplyintoarti ciallifeproblematics.Thegeneticautonomyandthenotionofmotivationforanagentmayleadtoadrasticallynewkindofemergencephenomenon[1](differentso-cialbehavior,auto-referringevaluationprocess,etc.)inself-organizingMASs.Itiscertainlyadif culttaskbutitmaysowtheseedsofaproli capproachcon-cerningarti ciallife.10.Conclusion

Determininganoptimalsolutionisalmostimpos-sible,buttryingtoimproveanexistingsolutionisthewaytoimprovetaskallocation.Duringthesimulationprocess,agentsgranularityappearswiththemutationbehaviorintroducedbyGA[38].Attheendofthesimulation,communicationsbetweenglobalandlocal

Fig.9.MASandGAintheenvironment.

Genetic algorithms using multi-objectives(10).doc 将本文的Word文档下载到电脑

精彩图片

热门精选

大家正在看

× 游客快捷下载通道(下载后可以自由复制和排版)

限时特价:7 元/份 原价:20元

支付方式:

开通VIP包月会员 特价:29元/月

注:下载文档有可能“只有目录或者内容不全”等情况,请下载之前注意辨别,如果您已付费且无法下载或内容有问题,请联系我们协助你处理。
微信:fanwen365 QQ:370150219