LEADER 03825nam 22005895 450 001 9910591038103321 005 20251113191826.0 010 $a3-031-07516-1 024 7 $a10.1007/978-3-031-07516-2 035 $a(MiAaPQ)EBC7080270 035 $a(Au-PeEL)EBL7080270 035 $a(CKB)24779133800041 035 $a(PPN)264956699 035 $a(OCoLC)1344159780 035 $a(DE-He213)978-3-031-07516-2 035 $a(EXLCZ)9924779133800041 100 $a20220903d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHandbook of Nature-Inspired Optimization Algorithms: The State of the Art $eVolume II: Solving Constrained Single Objective Real-Parameter Optimization Problems /$fedited by Ali Wagdy Mohamed, Diego Oliva, Ponnuthurai Nagaratnam Suganthan 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (220 pages) 225 1 $aStudies in Systems, Decision and Control,$x2198-4190 ;$v213 311 08$aPrint version: Mohamed, Ali Wagdy Handbook of Nature-Inspired Optimization Algorithms: the State of the Art Cham : Springer International Publishing AG,c2022 9783031075155 320 $aIncludes bibliographical references. 327 $aParticle swarm optimization based optimization for in-dustry inspection -- Ant Algorithms: from Drawback Identification to Quality and Speed Improvement -- Fault location techniques based on traveling waves with application in the protection of distribution systems with renewable energy and particle swarm optimization -- Improved Particle Swarm Optimization and Non-Quadratic Penalty Method for Non-Linear Programming Problems with Equality Constraints -- Recent Trends in Face Recognition Using Metaheuristic Optimization. 330 $aThis book presents recent contributions and significant development, advanced issues, and challenges. In real-world problems and applications, most of the optimization problems involve different types of constraints. These problems are called constrained optimization problems (COPs). The optimization of the constrained optimization problems is considered a challenging task since the optimum solution(s) must be feasible. In their original design, evolutionary algorithms (EAs) are able to solve unconstrained optimization problems effectively. As a result, in the past decade, many researchers have developed a variety of constraint handling techniques, incorporated into (EAs) designs, to counter this deficiency. The main objective for this book is to make available a self-contained collection of modern research addressing the general constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects. 410 0$aStudies in Systems, Decision and Control,$x2198-4190 ;$v213 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence 606 $aArtificial Intelligence 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a519.3 676 $a519.6 702 $aMohamed$b Ali Wagdy$f1978- 702 $aOliva$b Diego 702 $aSuganthan$b Ponnuthurai Nagaratnam 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910591038103321 996 $aHandbook of Nature-Inspired Optimization Algorithms: The State of the Art$94462643 997 $aUNINA