LEADER 04931nam 2200565 450 001 9910816157603321 005 20200520144314.0 010 $a1-119-38706-X 010 $a1-119-38707-8 010 $a1-119-38705-1 035 $a(CKB)4100000000641136 035 $a(Au-PeEL)EBL5015534 035 $a(CaPaEBR)ebr11430731 035 $a(CaONFJC)MIL1033389 035 $a(OCoLC)988749666 035 $a(CaSebORM)9781119386995 035 $a(MiAaPQ)EBC5015534 035 $a(EXLCZ)994100000000641136 100 $a20170926h20172017 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aMeta-heuristic and evolutionary algorithms for engineering optimization /$fOmid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loaiciga 205 $a1st edition 210 1$aHoboken, New Jersey :$cWiley,$d2017. 210 4$dİ2017 215 $a1 online resource (281 pages) $cillustrations 225 1 $aWiley Series in Operations Research and Management Science 311 $a1-119-38699-3 320 $aIncludes bibliographical references and index. 327 $aOverview of optimization -- Introduction to meta-heuristic and evolutionary algorithms -- Pattern search -- Genetic algorithm -- Simulated annealing -- Tabu search -- Ant colony optimization -- Particle swarm optimization -- Differential evolution -- Harmony search -- Shuffled frog-leaping algorithm -- Honey-bee mating optimization -- Invasive weed optimization -- Central force optimization -- Biogeography-based optimization -- Firefly algorithm -- Gravity search algorithm -- Bat algorithm -- Plant propagation algorithm -- Water cycle algorithm -- Symbiotic organisms search -- Comprehensive evolutionary algorithm. 330 $aA detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique. Chapter 1 of Meta-heuristic and Evolutionary Algorithms for Engineering Optimization provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm? and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book: Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization; Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner; Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms; Discusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering; Relates optimization algorithms to engineering problems employing a unifying approach. Meta-heuristic and Evolutionary Algorithms for Engineering Optimization is a reference intended for students, engineers, researchers, and instructors in the fields of industrial engineering, operations research, optimization/mathematics, engineering optimization, and computer science. OMID BOZORG-HADDAD, PhD, is Professor in the Department of Irriga... 410 0$aWiley series in operations research and management science. 606 $aMathematical optimization 606 $aEngineering design$xMathematics 615 0$aMathematical optimization. 615 0$aEngineering design$xMathematics. 676 $a620/.0042015196 700 $aBozorg-Haddad$b Omid$f1974-$01051360 702 $aSolgi$b Mohammad 702 $aLoaiciga$b Hugo A. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910816157603321 996 $aMeta-heuristic and evolutionary algorithms for engineering optimization$94088511 997 $aUNINA