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一种人工鱼群体行为动画的新方法
Abstract
Artificialfishswarmalgorithm(AFSA)isapopularswarmintelligencealgorithminspiredbythecollectivebehavioroffish.InAFSA,fishagentsinteractwitheachotherandtheenvironmenttofindoptimalsolutionstocomplexproblems.However,AFSAsuffersfromslowconvergenceandprematureconvergence,whichlimitsitsapplicationsinreal-worldscenarios.Inthispaper,weproposeanewmethodforgeneratingartificialfishswarmanimationsthatallowsustoobservetheswarmbehaviorandovercometheconvergenceissuesofAFSA.Ourmethodisbasedontheprinciplesofcomputergraphicsandinvolvesgeneratingrealisticfishmodels,simulatingthehydrodynamicsofwater,andvisualizingthemovementandinteractionoffishagents.Theresultinganimationprovidesarealisticandintuitiverepresentationofthecollectivebehavioroffish,whichcanbeusedforeducationalandresearchpurposes.
Introduction
Swarmintelligenceisabranchofartificialintelligencethatdealswiththecollectivebehaviorofdecentralizedsystems.Itisinspiredbythebehaviorofsocialanimalssuchasants,bees,andfish,whichexhibitemergentpropertiesthatarenotpresentintheindividualagents.Oneofthemostpopularswarmintelligencealgorithmsistheartificialfishswarmalgorithm(AFSA),whichisbasedonthecollectivebehavioroffish.AFSAhasbeenappliedtovariousoptimizationproblems,suchasfunctionoptimization,parametertuning,andimagesegmentation.
Despiteitspopularity,AFSAsuffersfromslowconvergenceandprematureconvergence,whichmeansthatthealgorithmtakesalongtimetoconvergetotheoptimalsolutionandoftengetsstuckinlocaloptima.Toovercometheseissues,researchershaveproposedvariousmodificationstothealgorithm,suchasadaptivestepsizecontrol,dynamicneighborhoodtopologies,andhybridizationwithothermetaheuristics.However,thesemodificationsmakethealgorithmmorecomplexandcomputationallyexpensive.
Inthispaper,weproposeanewmethodforgeneratingartificialfishswarmanimationsthatallowsustoobservetheswarmbehaviorandovercometheconvergenceissuesofAFSA.Ourmethodisbasedontheprinciplesofcomputergraphicsandinvolvesgeneratingrealisticfishmodels,simulatingthehydrodynami