LEADER 05143nam 22005775 450 001 9910337595303321 005 20200703062659.0 010 $a3-030-12044-9 024 7 $a10.1007/978-3-030-12044-3 035 $a(CKB)4100000008280562 035 $a(MiAaPQ)EBC5780049 035 $a(DE-He213)978-3-030-12044-3 035 $a(PPN)236522574 035 $a(EXLCZ)994100000008280562 100 $a20190521d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aModern Music-Inspired Optimization Algorithms for Electric Power Systems $eModeling, Analysis and Practice /$fby Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (747 pages) 225 1 $aPower Systems,$x1612-1287 300 $aIncludes index. 311 $a3-030-12043-0 327 $aChapter1: Introduction to Meta-Heuristic Optimization Algorithms -- Chapter2: Introduction to Multi-Objective Optimization and Decision Making Analysis -- Chapter3: Music-Inspired Optimization Algorithms: From Past to Present -- Chapter4: Advances in Music-Inspired Optimization Algorithms -- Chapter5: Power Systems Operation -- Chapter6: Power Systems Planning -- Chapter7: Power Quality Planning. 330 $aIn today?s world, with an increase in the breadth and scope of real-world engineering optimization problems as well as with the advent of big data, improving the performance and efficiency of algorithms for solving such problems has become an indispensable need for specialists and researchers. In contrast to conventional books in the field that employ traditional single-stage computational, single-dimensional, and single-homogeneous optimization algorithms, this book addresses multiple newfound architectures for meta-heuristic music-inspired optimization algorithms. These proposed algorithms, with multi-stage computational, multi-dimensional, and multi-inhomogeneous structures, bring about a new direction in the architecture of meta-heuristic algorithms for solving complicated, real-world, large-scale, non-convex, non-smooth engineering optimization problems having a non-linear, mixed-integer nature with big data. The architectures of these new algorithms may also be appropriate for finding an optimal solution or a Pareto-optimal solution set with higher accuracy and speed in comparison to other optimization algorithms, when feasible regions of the solution space and/or dimensions of the optimization problem increase. This book, unlike conventional books on power systems problems that only consider simple and impractical models, deals with complicated, techno-economic, real-world, large-scale models of power systems operation and planning. Innovative applicable ideas in these models make this book a precious resource for specialists and researchers with a background in power systems operation and planning. Provides an understanding of the optimization problems and algorithms, particularly meta-heuristic optimization algorithms, found in fields such as engineering, economics, management, and operations research; Enhances existing architectures and develops innovative architectures for meta-heuristic music-inspired optimization algorithms in order to deal with complicated, real-world, large-scale, non-convex, non-smooth engineering optimization problems having a non-linear, mixed-integer nature with big data; Addresses innovative multi-level, techno-economic, real-world, large-scale, computational-logical frameworks for power systems operation and planning, and illustrates practical training on implementation of the frameworks using the meta-heuristic music-inspired optimization algorithms. 410 0$aPower Systems,$x1612-1287 606 $aPower electronics 606 $aMathematical optimization 606 $aComputational intelligence 606 $aPower Electronics, Electrical Machines and Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24070 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 615 0$aPower electronics. 615 0$aMathematical optimization. 615 0$aComputational intelligence. 615 14$aPower Electronics, Electrical Machines and Networks. 615 24$aOptimization. 615 24$aComputational Intelligence. 676 $a621.31 676 $a621.310151 700 $aKiani-Moghaddam$b Mohammad$4aut$4http://id.loc.gov/vocabulary/relators/aut$0864685 702 $aShivaie$b Mojtaba$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aWeinsier$b Philip D$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910337595303321 996 $aModern Music-Inspired Optimization Algorithms for Electric Power Systems$91930037 997 $aUNINA