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001438227 020__ $$a9783030788117$$q(electronic bk.)
001438227 020__ $$a3030788113$$q(electronic bk.)
001438227 020__ $$z9783030788100$$q(print)
001438227 0247_ $$a10.1007/978-3-030-78811-7$$2doi
001438227 035__ $$aSP(OCoLC)1260346159
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001438227 050_4 $$aQ337.3$$b.I28 2021eb
001438227 08204 $$a006.3/824$$223
001438227 1112_ $$aICSI (Conference)$$n(12th :$$d2021 :$$cQingdao, China)
001438227 24510 $$aAdvances in swarm intelligence :$$b12th International Conference, ICSI 2021, Qingdao, China, July 17-21, 2021, Proceedings.$$nPart II /$$cYing Tan, Yuhui Shi (eds.).
001438227 2463_ $$aICSI 2021
001438227 260__ $$aCham :$$bSpringer,$$c2021.
001438227 300__ $$a1 online resource (591 pages)
001438227 336__ $$atext$$btxt$$2rdacontent
001438227 337__ $$acomputer$$bc$$2rdamedia
001438227 338__ $$aonline resource$$bcr$$2rdacarrier
001438227 347__ $$atext file
001438227 347__ $$bPDF
001438227 4901_ $$aLecture notes in computer science ;$$v12690
001438227 4901_ $$aLNCS sublibrary, SL 1, Theoretical computer science and general issues
001438227 500__ $$a2.1 Multiparty Multiobjective Optimization Problems (MPMOPs).
001438227 500__ $$aIncludes author index.
001438227 5050_ $$aIntro -- Preface -- Organization -- Contents -- Part II -- Contents -- Part I -- Multi-objective Optimization -- A Multi-objective Evolutionary Algorithm Based on Second-Order Differential Operator -- 1 Introduction -- 2 MOP Problem -- 3 Second-Order Differential Evolution -- 4 MOEA/D-SODE Algorithm -- 4.1 General Framework of MOEA/D-SODE -- 4.2 A SODE-Best Second-Order Differential Operator -- 4.3 The Flow Chart of MOEA/D-SODE -- 5 Experimental Results and Analysis -- 5.1 Experimental Environment and Parameter Setting -- 5.2 Performance Metrics -- 5.3 Analysis of Experimental Results
001438227 5058_ $$a6 Conclusion -- References -- An Improved Evolutionary Multi-objective Optimization Algorithm Based on Multi-population and Dynamic Neighborhood -- 1 Introduction -- 2 Problem Statement and Related Methods -- 2.1 Problem Statement -- 2.2 Related Methods -- 3 Proposed Method -- 3.1 Framework of the Proposed Method -- 3.2 Multi-population Strategy -- 3.3 Dynamic Neighborhood -- 4 Experiment and Analysis -- 4.1 Settings -- 4.2 Results and Analysis -- 5 Conclusion -- References -- A Multiobjective Memetic Algorithm for Multiobjective Unconstrained Binary Quadratic Programming Problem
001438227 5058_ $$a1 Introduction -- 2 Background -- 2.1 Multiobjective Optimization -- 2.2 Formulation of mUBQP -- 3 Proposed Algorithm: MOMA -- 3.1 Framework of MOMA -- 3.2 Population Initialization and Stopping Criterion -- 3.3 Uniform Generation -- 3.4 Crossover Operator and Tabu Search -- 3.5 Archive and Weight Vector Updating -- 4 Computational Experiments -- 4.1 Experimental Settings and Performance Measures -- 4.2 Competitors -- 4.3 Comparing MOMA with the Competitors -- 5 Conclusion and Future Work -- References
001438227 5058_ $$aA Hybrid Algorithm for Multi-objective Permutation Flow Shop Scheduling Problem with Setup Times -- 1 Introduction -- 2 Problem Description -- 3 Proposed Hybrid Algorithm for Multi-objective PFSP with Setup Times -- 3.1 Encoding and Decoding of Chromosome -- 3.2 Initial Population Generation -- 3.3 Pareto Sorting -- 3.4 Selection and Crossover Operator -- 3.5 Mutation Operator -- 3.6 Neighborhood Structure Design -- 3.7 Flowchart of Proposed MOHGA -- 4 Experimental Results and Analysis -- 5 Conclusion and Future Work -- References
001438227 5058_ $$aDynamic Multi-objective Optimization via Sliding Time Window and Parallel Computing -- 1 Introduction -- 2 Background -- 2.1 Dynamic Multi-objective Optimization -- 2.2 Performance Metric -- 2.3 Sliding Time Window -- 3 Related Work -- 4 Sliding Time Window Based on Parallel Computing -- 5 Experimental Results and Analyses -- 5.1 Test the STW-PC Using MIGD -- 5.2 Experimental Analysis -- 6 Conclusions and Future Work -- References -- A New Evolutionary Approach to Multiparty Multiobjective Optimization -- 1 Introduction -- 2 Related Work
001438227 506__ $$aAccess limited to authorized users.
001438227 520__ $$aThis two-volume set LNCS 12689-12690 constitutes the refereed proceedings of the 12th International Conference on Advances in Swarm Intelligence, ICSI 2021, held in Qingdao, China, in July 2021. The 104 full papers presented in this volume were carefully reviewed and selected from 177 submissions. They cover topics such as: Swarm Intelligence and Nature-Inspired Computing; Swarm-based Computing Algorithms for Optimization; Particle Swarm Optimization; Ant Colony Optimization; Differential Evolution; Genetic Algorithm and Evolutionary Computation; Fireworks Algorithms; Brain Storm Optimization Algorithm; Bacterial Foraging Optimization Algorithm; DNA Computing Methods; Multi-Objective Optimization; Swarm Robotics and Multi-Agent System; UAV Cooperation and Control; Machine Learning; Data Mining; and Other Applications.
001438227 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed July 21, 2021).
001438227 650_0 $$aSwarm intelligence$$vCongresses.
001438227 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001438227 655_0 $$aElectronic books.
001438227 7001_ $$aTan, Ying,$$d1964-
001438227 7001_ $$aShi, Yuhui.
001438227 77608 $$iPrint version:$$aTan, Ying.$$tAdvances in Swarm Intelligence.$$dCham : Springer International Publishing AG, ©2021$$z9783030788100
001438227 830_0 $$aLecture notes in computer science ;$$v12690.
001438227 830_0 $$aLNCS sublibrary.$$nSL 1,$$pTheoretical computer science and general issues.
001438227 852__ $$bebk
001438227 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-78811-7$$zOnline Access$$91397441.1
001438227 909CO $$ooai:library.usi.edu:1438227$$pGLOBAL_SET
001438227 980__ $$aBIB
001438227 980__ $$aEBOOK
001438227 982__ $$aEbook
001438227 983__ $$aOnline
001438227 994__ $$a92$$bISE