03254nam a22004815i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050000900172072001600181072002300197072001500220100008000235245013100315250001800446264006900464300006700533336002600600337002600626338003600652347002400688490006200712505035600774520108301130650003902213650003802252650003902290650002502329700008902354710003402443773002602477776003602503776003602539776003602575830006902611856004602680912001402726912001502740999001702755978-3-031-48918-1DE-He21320251030111406.0cr nn 008mamaa240129s2024    sz |    s    |||| 0|eng d  a97830314891819978-3-031-48918-17 a10.1007/978-3-031-48918-12doi 4aQ342 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema1 aKaveh, Ali.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut97008210aChaotic Meta-heuristic Algorithms for Optimal Design of Structuresh[electronic resource] /cby Ali Kaveh, Hossein Yousefpoor.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXII, 342 p. 153 illus., 149 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aStudies in Computational Intelligence,x1860-9503 ;v11290 aIntroduction -- Chaotic Maps and Meta-Heuristic Algorithms -- Chaotic Cyclical Parthenogenesis Algorithm -- Chaotic Teaching Learning-Based Optimization -- Chaotic Biogeography Based Optimization -- Chaotic Differential Evolution -- Chaotic Water Evaporation Optimization -- Chaotic Artificial Bees Colony -- Chaotic Imperialist Competitive Algorithm.  aIn this book, various chaos maps are embedded in eleven efficient and well-known metaheuristics and a significant improvement in the optimization results is achieved. The two basic steps of metaheuristic algorithms consist of exploration and exploitation. The imbalance between these stages causes serious problems for metaheuristic algorithms, which are immature convergence and stopping in local optima. Chaos maps with chaotic jumps can save algorithms from being trapped in local optima and lead to convergence toward global optima. Embedding these maps in the exploration phase, exploitation phase, or both simultaneously corresponds to three efficient and useful scenarios. By creating competition between different modes and increasing diversity in the search space and creating sudden jumps in the search phase, improvements are achieved for chaotic algorithms. Four Chaotic Algorithms, including Chaotic Cyclical Parthenogenesis Algorithm, Chaotic Water Evaporation Optimization, Chaotic Tug-of-War Optimization, and Chaotic Thermal Exchange Optimization are developed. 0aComputational intelligence.964052 0aMathematical optimization.96552214aComputational Intelligence.96405224aOptimization.9655281 aYousefpoor, Hossein.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9700832 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303148917408iPrinted edition:z978303148919808iPrinted edition:z9783031489204 0aStudies in Computational Intelligence,x1860-9503 ;v112996885440uhttps://doi.org/10.1007/978-3-031-48918-1  aZDB-2-INR  aZDB-2-SXIT  c57352d57352