An Efficient Hybrid Evolution Strategy Algorithm with Direct Search Method for Global Optimization

Themainpurposeofthisarticleistodemonstratehowevolutionstrategyoptimizerscanbeimproved byincorporatinganefficienthybridizationschemewithrestartstrategyinordertojumpoutoflocal solutionregions.Theauthorsproposeahybrid(μ,λ)ES-NMalgorithmbasedontheNelder-Mead (NM)simplexsearchmethodandevolutionstrategyalgorithm(ES)forunconstrainedoptimization. Atfirst,amodifiedNM,calledAdaptiveNelder-Mead(ANM)isusedthatexhibitsbetterproperties thanstandardNMandself-adaptiveevolutionstrategyalgorithmisappliedforbetterperformance,in additiontoanewcontractioncriterionisproposedinthiswork.(μ,λ)ES-NMisbalancingbetween theglobalexplorationoftheevolutionstrategyalgorithmandthedeepexploitationoftheNelderMeadmethod.Theexperimentresultsshowtheefficiencyofthenewalgorithmanditsabilityto solveoptimizationproblemsintheperformanceofaccuracy,robustness,andadaptability. KEywORDS Artificial Intelligence, Complex Continuous Functions, Contraction Criterion, Evolutionary Algorithm, Hybrid Algorithm, Local Search, Nelder-Mead Method, Unconstrained Optimization

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