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一种求解多维函数优化问题的改进组搜索优化算法.docx

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一种求解多维函数优化问题的改进组搜索优化算法 Abstract Optimizationproblemscanbeseeninmultipledomainssuchasfinance,engineering,andcomputerscience.Insomecases,theseproblemsarenottrivialandinvolvemultiplevariablesandconstraints.Traditionaloptimizationalgorithmsfailinsuchcasesastheyarenotefficientintermsofsearchingthroughthesolutionspace.Thisiswheremetaheuristicalgorithmscomeintoplay.Metaheuristicalgorithmssearchthroughthesolutionspaceinamorerobustmannerandcanavoidgettingstuckinasuboptimalsolution.TheGroupSearchOptimization(GSO)algorithmisonesuchmetaheuristicalgorithmthathasbeentestedonvariousoptimizationproblems.However,theGSOhassomelimitationsthatcanbeaddressedbytheImprovedGSO. ThispaperdiscussestheImprovedGSOalgorithm,whichisanextensionoftheGSO.TheImprovedGSOalgorithmaddsacomplexificationmechanismtotheGSOalgorithm.Thecomplexificationmechanismallowsthealgorithmtoconvergetotheoptimalsolutionmoreefficientlybytestingdifferentsolutionsandconsideringthecombinationsthatprovideimprovedresults.ThealgorithmimplementsthesamecharacteristicsoftheGSOalgorithmsuchastheuseofgroups,whichworktogethertofindanoptimizedsolution.TheImprovedGSOalgorithmhasbeentestedonvariousbenchmarkfunctionsandcomparedtootherpopularmetaheuristicalgorithmssuchastheParticleSwarmOptimizationalgorithmandtheGeneticAlgorithm.TheresultsshowthattheImprovedGSOalgorithmoutperformstheotheralgorithmsintermsofaccuracyandefficiency. Introduction Optimizationplaysanimportantroleinmanydomainssuchasfinance,engineering,andcomputerscience.Optimizationproblemsexistinvariousformssuchaslinearprogrammingproblems,nonlinearprogrammingproblems,anddynamicprogrammingproblems.Insomecases,theseproblemsarenottrivialandinvolvemultiplevariablesandconstraints,whichmakethemdifficulttosolveusingtraditionalalgorithms.Traditionaloptimizationtechniquesusemathematicalmodelstosolveoptimizationproblems.Thesealgorithmsarenotalwaysefficientwhentheproblemiscomplex,andtheyoftenfailtoprovideaglobaloptimumsolution. Efficienttechniquesarerequiredtosolvecomplexoptimizationpro