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001476208 001__ 1476208
001476208 003__ OCoLC
001476208 005__ 20231003174637.0
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001476208 0247_ $$a10.1007/978-981-99-3970-1$$2doi
001476208 035__ $$aSP(OCoLC)1395180903
001476208 040__ $$aEBLCP$$beng$$cEBLCP$$dYDX$$dGW5XE$$dEBLCP
001476208 049__ $$aISEA
001476208 050_4 $$aQA402.5
001476208 08204 $$a519.6$$223/eng/20230830
001476208 24500 $$aBenchmarks and hybrid algorithms in optimization and applications /$$cXin-She Yang, editor.
001476208 260__ $$aSingapore :$$bSpringer,$$c2023.
001476208 300__ $$a1 online resource (viii, 246 pages) :$$billustrations (chiefly color).
001476208 4901_ $$aSpringer Tracts in Nature-Inspired Computing Series
001476208 500__ $$aDescription based upon print version of record.
001476208 5050_ $$aIntro -- Preface -- Contents -- 1 Nature-Inspired Algorithms in Optimization: Introduction, Hybridization, and Insights -- 1 Introduction -- 2 Optimization and Algorithms -- 2.1 Components of Optimization -- 2.2 Gradients and Optimization -- 3 Nature-Inspired Algorithms -- 3.1 Recent Nature-Inspired Algorithms -- 3.2 Other Nature-inspired Algorithms -- 4 Hybridization -- 4.1 Hybridization Schemes -- 4.2 Issues and Warnings -- 5 Insights and Recommendations -- References -- 2 Ten New Benchmarks for Optimization -- 1 Introduction -- 2 Role of Benchmarks -- 3 New Benchmark Functions
001476208 5058_ $$a3.1 Noisy Functions -- 3.2 Non-differentiable Functions -- 3.3 Functions with Isolated Domains -- 4 Benchmarks with Multiple Optimal Solutions -- 4.1 Function on a Hyperboloid -- 4.2 Non-smooth Multi-layered Functions -- 5 Parameter Estimation as Benchmarks -- 6 Integrals as Benchmarks -- 7 Benchmarks of Infinite Dimensions -- 7.1 Shortest Path Problem -- 7.2 Shape Optimization -- 8 Conclusions -- References -- 3 Review of Parameter Tuning Methods for Nature-Inspired Algorithms -- 1 Introduction -- 2 Parameter Tuning -- 2.1 Schematic Representation of Parameter Tuning
001476208 5058_ $$a2.2 Different Types of Optimality -- 2.3 Approaches to Parameter Tuning -- 3 Review of Parameter Tuning Methods -- 3.1 Generic Methods for Parameter Tuning -- 3.2 Online and Offline Tunings -- 3.3 Self-Parametrization and Fuzzy Methods -- 3.4 Machine Learning-Based Methods -- 4 Discussions and Recommendations -- References -- 4 QOPTLib: A Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems -- 1 Introduction -- 2 Description of the Problems -- 2.1 Traveling Salesman Problem -- 2.2 Vehicle Routing Problem -- 2.3 Bin Packing Problem -- 2.4 Maximum Cut Problem
001476208 5058_ $$a3 Introducing the Generated QOPTLib Benchmarks -- 4 Preliminary Experimentation -- 5 Conclusions and Further Work -- References -- 5 Benchmarking for Discrete Cuckoo Search: Three Case Studies -- 1 Introduction -- 2 COPs Statements -- 2.1 Studied COPs -- 2.2 Formal Definitions -- 3 DCS Common Resolution -- 3.1 General Algorithm -- 3.2 Main Functions -- 4 Studied Case Resolutions -- 4.1 Solutions -- 4.2 Moves -- 5 Experimental Tests -- 5.1 Parameters -- 5.2 Instances -- 5.3 Statistic Tests -- 6 Conclusion -- References
001476208 5058_ $$a6 Metaheuristics for Feature Selection: A Comprehensive Comparison Using Opytimizer -- 1 Introduction -- 2 Literature Review -- 3 Hands-on Opytimizer: A Python Implementation for Metaheuristic Optimization -- 4 Case Study: Feature Selection -- 4.1 Methodology -- 4.2 Experiments -- 5 Conclusions -- References -- 7 AL4SLEO: An Active Learning Solution for the Semantic Labelling of Earth Observation Satellite Images-Part 1 -- 1 Introduction -- 2 State of the Art -- 3 Data Set Description -- 4 Active Learning -- 5 Semantic Labelling -- 6 Conclusions -- References
001476208 506__ $$aAccess limited to authorized users.
001476208 520__ $$aThis book is specially focused on the latest developments and findings on hybrid algorithms and benchmarks in optimization and their applications in sciences, engineering, and industries. The book also provides some comprehensive reviews and surveys on implementations and coding aspects of benchmarks. The book is useful for Ph.D. students and researchers with a wide experience in the subject areas and also good reference for practitioners from academia and industrial applications.
001476208 650_0 $$aMathematical optimization.$$vCongresses$$0(DLC)sh2008107548
001476208 655_0 $$aElectronic books.
001476208 7001_ $$aYang, Xin-She.
001476208 77608 $$iPrint version:$$aYang, Xin-She$$tBenchmarks and Hybrid Algorithms in Optimization and Applications$$dSingapore : Springer,c2023$$z9789819939695
001476208 830_0 $$aSpringer Tracts in Nature-Inspired Computing.
001476208 852__ $$bebk
001476208 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-3970-1$$zOnline Access$$91397441.1
001476208 909CO $$ooai:library.usi.edu:1476208$$pGLOBAL_SET
001476208 980__ $$aBIB
001476208 980__ $$aEBOOK
001476208 982__ $$aEbook
001476208 983__ $$aOnline
001476208 994__ $$a92$$bISE