【中文摘要】随着计算机科学与技术的迅速发展,人类生存空间的扩大以及认识与改造世界范围的拓宽,人们对科学技术提出了新的和更高的要求,其中高效的优化技术和智能计算的要求日益迫切。蛙跳算法是一种新兴的群智能优化算法,概念简单,易于实现。自从2003年Eusufr和Lansey首次应用该算法,之后在一些领域获得了成功应用。蛙跳算法具有较强的全局搜索能力,但同时对于一些复杂的问题依然存在着收敛速度不是很快、易于陷入局部极值的缺点,并且传统的蛙跳算法模型适合于解决连续优化问题,不适合解决离散的组合优化问题。为此,根据蛙跳算法的优化机理,提出了一种新的离散化蛙跳求解算法,并结合简化邻域搜索算法给出了三种改进策略,对新算法进行了改进。同时进行了仿真实验研究。主要研究成果和内容如下。首先,分析了蛙跳算法的优化机理,提出了适合求解复杂组合优化问题的离散蛙跳算法,新算法通过采用新的个体产生方法扩展了传统蛙跳算法的求解模型。其次,为了提高离散蛙跳算法的收敛速度,根据算法中群体对局部极值和全局极值的依赖性较大的特点,结合邻域搜索算法,提出了改进算法。接着,通过增加扰动策略,扩大局部极值和全局极值的搜索范围,从而进一步提高算法的收敛速度。再次,模拟退火算法具有较强的局部搜索能力,并能避免陷入局部最优解,但它的搜索能力不强。为此将离散蛙跳算法结合模拟退火的思想,对算法进一步改进,降低算法运行中陷入局部最优的概率。对旅行商问题、零空闲流水线调度问题这两种受约束的、离散的组合优化问题,采用离散蛙跳算法和改进算法分别进行了应用研究。仿真实验表明了所提算法及改进策略的有效性。');
【Abstract】Withtherapiddevelopmentofthecomputerscienceandtechnology,people\'slivingspacehasbeenenlarged,andthefieldinwhichpeoplerecognizeandchangetheworldhasbeenbroaden.Thedemandforscienceandtechnologyisinincrease.Thereforehigh-performanceoptimizingtechnologyandintelligenceoptimizationisinurgentneed.Theshuffledfrogleapingalgorithm(SFLA)isakindofrisingswarmintelligenceoptimizer.It\'sconceptissimpleanditiseasytobeimplemented.AfterbeingpresentedbyEusuffandLanseyin2003,Ithasbeensuccessfullyappliedtosomefields.Theshuffledfrogleapingalgorithmhasastrongabilitytoachievethemostoptimisticresult.Meanwhileithasadisadvantagesofarasitslocalminimumisconcernedwhenusedonsomecomplicatedcombinatorialoptimizationproblems.AndthetraditionalmodelofSFLAisisappliedtocontinousoptimizationproblems,itisnotfitfordiscretecombinatorialoptimizationproblems.SobasedonthestudyofthemechanismofSFLA,discreteshuffledfrogleapingalgorithm(DSFLA)ispresented,andemploysthesimpleneighborhoodsearchtopresentthreeimprovementstrategies.Theexperimentalhasbeendone.Theprimarycontentsandresultsarefollowing.First,basedontheanalysisoftheSFLA\'soptimizationmechanism,discreteshuffledfrogalgorithmforcomplexcombinationaloptimizationproblemswasputforward.ThenewalgorithmadoptsanewmethodofindividualproductiontoextendthetraditionalmodelofSFLA.Secondly,theswarmdependsextremelyonthelocalbestresultandthegroupbestresult.Basedonthischaracter,theimprovedalgorithmwasproposedemploysthesimpleneighborhoodsearch,anditcanimprovetheconvergencerateofDSFLA.Then,accordingtoincreasingthedisturbancestrategycanexpandthesearchscopeoflocalandglobalextremeresult,thealgorithm\'sconvergenceratewasfurtherenhanced.Finally,Simulatedannealingalgorithmhasastrongablitytoachievethelocaloptimisticresult.Anditcanavoidlocalminimum.Butontheotherhanditsabilitytoachievetheglobaloptimisticresultisquiteweak.SoDSFLAintegratesthemethodofSAcanfurtherimprovethealgorithm,andreducetheprobabilityoflocalminimums.Travellingsalesmanproblem(TSP)andnoidleflowshopproblem(NIFS)whichareconstrainedanddiscreteproblemsarediscussedinthepaperusingDSFLAandthreeimprovementstrategies.Theexperimentalresultsshowthattheproposedalgorithmanditsimprovementsareeffectiveandefficientfordifferentscalebenchmarksproblems.
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