LEADER 04187nam 22005655 450 001 9910767528403321 005 20200702010350.0 010 $a981-15-5097-2 024 7 $a10.1007/978-981-15-5097-3 035 $a(CKB)5280000000218464 035 $a(MiAaPQ)EBC6224949 035 $a(DE-He213)978-981-15-5097-3 035 $a(PPN)248593676 035 $a(EXLCZ)995280000000218464 100 $a20200609d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplications of Bat Algorithm and its Variants /$fedited by Nilanjan Dey, V. Rajinikanth 205 $a1st ed. 2021. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2021. 215 $a1 online resource (182 pages) 225 1 $aSpringer Tracts in Nature-Inspired Computing,$x2524-552X 311 $a981-15-5096-4 320 $aIncludes bibliographical references. 327 $aChapter 1. A New Hybrid Binary Algorithm of Bat Algorithm and Differential Evolution for Feature Selection and Classification -- Chapter 2. Multi-objective Optimization of Engineering Design Problems through Pareto-Based Bat Algorithm -- Chapter 3. A Study on the Bat Algorithm Technique To Evaluate The Skin Melanoma Images -- Chapter 4. Multi-Thresholding with Kapur?s Entropy ? A Study Using Bat Algorithm with Different Search Operators -- Chapter 5. Application of BAT Inspired Computing Algorithm and Its Variants In Search of Near Optimal Golomb Rulers For WDM Systems: A Comparative Study -- Chapter 6. Levy Flight Opposition Embed Bat Algorithm for Model Order Reduction -- Chapter 7. Application of BAT Algorithm for Detecting Malignant Brain Tumors -- Chapter 8. Bat Algorithm with Applications to Signal, speech and Image Processing- A Review -- Chapter 9. Bat Algorithm Aided System to Extract Tumor in Flair/T2 Modality Brain MRI Slices. . 330 $aThis book highlights essential concepts in connection with the traditional bat algorithm and its recent variants, as well as its application to find optimal solutions for a variety of real-world engineering and medical problems. Today, swarm intelligence-based meta-heuristic algorithms are extensively being used to address a wide range of real-world optimization problems due to their adaptability and robustness. Developed in 2009, the bat algorithm (BA) is one of the most successful swarm intelligence procedures, and has been used to tackle optimization tasks for more than a decade. The BA?s mathematical model is quite straightforward and easy to understand and enhance, compared to other swarm approaches. Hence, it has attracted the attention of researchers who are working to find optimal solutions in a diverse range of domains, such as N-dimensional numerical optimization, constrained/unconstrained optimization and linear/nonlinear optimization problems. Along with the traditional BA, its enhanced versions are now also being used to solve optimization problems in science, engineering and medical applications around the globe. 410 0$aSpringer Tracts in Nature-Inspired Computing,$x2524-552X 606 $aComputational intelligence 606 $aAlgorithms 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aAlgorithms$3https://scigraph.springernature.com/ontologies/product-market-codes/M14018 615 0$aComputational intelligence. 615 0$aAlgorithms. 615 14$aComputational Intelligence. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aAlgorithms. 676 $a519.3 702 $aDey$b Nilanjan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRajinikanth$b V$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910767528403321 996 $aApplications of Bat Algorithm and its Variants$93655975 997 $aUNINA