基于对数惯性权重的改进蚁狮优化算法

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中图分类号：TN911.73 文献标识码：A 文章编号：1006-8228（2026）01-18-05

Abstract：InordertosolvetheproblemsthattheconvergencespeedofAntLionOptimization（ALO）isnotfastenoughinthelater staganditiseasytofallintolocaloptimathispaperproposestheimprovedAntLionOptiizationbasedonlogarithmicinertia weight（LALO）.LALOusesthecharacteristicsoflogarithmicfunctiontorealizethenonlinearadjustmentofinertiaweight，thus beterbalancingtheglobalexplorationandlocalexploitationcapabiliesofthealgorithm.Atthesametime，thelogarithmiciertia weightstrategyisintroducedintothelocationupdateofthealgorithmtooptimizethelocationupdateprocessofindividualant lions，whichreducesthepossbilityoftealgoritmfallngintolocalconvergenceandacceleratestheconvergenespeedThree clasictestfunctionsareusedtotesttheoptimizationperformanceofLALO.Comparedwiththeexistingswarminteligence algorithms，LALOaceleratestheconvergencespeedand improvestheconvergenceauracyandstabilityof thealgorithm.

Keywords：LogariticFunction;InertiaWeight;AntLionOptiizationAlgorith;ConvergenceSped；Stability;Convergenceccuracy

0引言

近年来，随着工业、农业、能源、通信等领域的快速发展，实际工程中的优化问题在规模和复杂度上呈现出指数级增长。（剩余4516字）