LEADER 03787nam 22005053 450 001 9910747590703321 005 20231011080222.0 010 $a3-031-42685-1 035 $a(MiAaPQ)EBC30775415 035 $a(Au-PeEL)EBL30775415 035 $a(PPN)272914843 035 $a(CKB)28477905100041 035 $a(Exl-AI)30775415 035 $a(EXLCZ)9928477905100041 100 $a20231011d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMetaheuristics and Optimization in Computer and Electrical Engineering $eVolume 2: Hybrid and Improved Algorithms 205 $a1st ed. 210 1$aCham :$cSpringer International Publishing AG,$d2023. 210 4$dİ2023. 215 $a1 online resource (491 pages) 225 1 $aLecture Notes in Electrical Engineering Series ;$vv.1077 311 08$aPrint version: Razmjooy, Navid Metaheuristics and Optimization in Computer and Electrical Engineering Cham : Springer International Publishing AG,c2023 9783031426841 327 $aPreface -- Contents -- A Comprehensive Survey of Meta-heuristic Algorithms -- 1 Introduction -- 2 Conception of the Optimization -- 3 Optimization Methods -- 3.1 Exact Optimization Methods -- 3.2 Intelligent (No Exact) Optimization Methods -- 4 The Concept of the Cost Function and Its Types -- 4.1 Single-Dimensional and Multi-dimensional Cost Functions -- 4.2 Dynamic and Static Cost Functions -- 4.3 Constrained and Un-constrained Cost Functions -- 4.4 Continuous and Discrete Cost Functions -- 4.5 Single Objective and Multi-objective Cost Functions -- 5 Meta-heuristic Algorithms -- 5.1 Particle Swarm Optimization Algorithm -- 5.2 Imperialist Competitive Algorithm -- 5.3 Invasive Weed Optimization Algorithm -- 5.4 Quantum Invasive Weed Optimization Algorithm -- 5.5 Firefly Algorithm -- 5.6 Artificial Bee Colony Algorithm -- 5.7 World Cup Optimization Algorithm -- 6 Benchmark Functions -- 7 Simulation Results for the Analyzed Algorithms -- 8 Discussions -- 9 Conclusions -- References -- Order Reduction of the Time-Independent Linear Systems Using the Firefly Algorithm with Neighbourhood Attraction -- 1 Introduction -- 2 Reducing the Order of the Model$7Generated by AI. 330 $aThis volume in the Lecture Notes in Electrical Engineering series explores advanced optimization techniques in computer and electrical engineering. It emphasizes hybrid and improved algorithms to solve complex engineering problems such as embedded systems, circuit design, robotics, and energy management. The book integrates concepts from artificial intelligence, control theory, and machine learning to develop efficient solutions. It covers evolutionary computation, swarm intelligence, and ant colony optimization, with applications in robotics, machine learning, and autonomous systems. Designed for researchers, engineers, and students, it provides a comprehensive overview of metaheuristic methods through examples and case studies.$7Generated by AI. 410 0$aLecture Notes in Electrical Engineering Series 606 $aOptimisation . . $7Generated by AI 606 $aArtificial intelligence$7Generated by AI 615 0$aOptimisation . . . 615 0$aArtificial intelligence 676 $a519.6 700 $aRazmjooy$b Navid$01431704 701 $aGhadimi$b Noradin$01431705 701 $aRajinikanth$b Venkatesan$01431706 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910747590703321 996 $aMetaheuristics and Optimization in Computer and Electrical Engineering$93574621 997 $aUNINA