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001431612 001__ 1431612
001431612 003__ OCoLC
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001431612 019__ $$a1158085940$$a1162535857$$a1163831274$$a1164673960$$a1175704495$$a1182529366$$a1183408697
001431612 020__ $$a9789811550973$$q(electronic bk.)
001431612 020__ $$a9811550972$$q(electronic bk.)
001431612 020__ $$z9811550964
001431612 020__ $$z9789811550966
001431612 0248_ $$a10.1007/978-981-15-5
001431612 035__ $$aSP(OCoLC)1158218508
001431612 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dYDX$$dGW5XE$$dLQU$$dOCLCF$$dOCLCO$$dOCLCQ$$dOCLCO$$dOCLCQ
001431612 049__ $$aISEA
001431612 050_4 $$aQA402.5
001431612 08204 $$a519.6$$223
001431612 24500 $$aApplications of Bat algorithm and its variants /$$cNilanjan Dey, V. Rajinikanth, editors.
001431612 260__ $$aSingapore :$$bSpringer,$$c[2021]
001431612 300__ $$a1 online resource (182 pages)
001431612 336__ $$atext$$btxt$$2rdacontent
001431612 337__ $$acomputer$$bc$$2rdamedia
001431612 338__ $$aonline resource$$bcr$$2rdacarrier
001431612 4901_ $$aSpringer tracts in nature-inspired computing
001431612 5050_ $$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 Kapurs 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.
001431612 506__ $$aAccess limited to authorized users.
001431612 520__ $$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 BAs 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.
001431612 588__ $$aDescription based on print version record.
001431612 650_0 $$aMathematical optimization.
001431612 650_0 $$aSwarm intelligence.
001431612 650_6 $$aOptimisation mathématique.
001431612 655_0 $$aElectronic books.
001431612 7001_ $$aDey, Nilanjan,$$d1984-
001431612 7001_ $$aRajinikanth, V.
001431612 77608 $$iPrint version:$$aDey, Nilanjan.$$tApplications of Bat Algorithm and Its Variants.$$dSingapore : Springer, ©2020$$z9789811550966
001431612 830_0 $$aSpringer tracts in nature-inspired computing.
001431612 852__ $$bebk
001431612 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-15-5097-3$$zOnline Access$$91397441.1
001431612 909CO $$ooai:library.usi.edu:1431612$$pGLOBAL_SET
001431612 980__ $$aBIB
001431612 980__ $$aEBOOK
001431612 982__ $$aEbook
001431612 983__ $$aOnline
001431612 994__ $$a92$$bISE