LEADER 05562nam 2200637Ia 450 001 9910971364703321 005 20251116221438.0 010 $a1-61728-813-6 035 $a(CKB)2560000000081141 035 $a(EBL)3018118 035 $a(SSID)ssj0000687013 035 $a(PQKBManifestationID)12347881 035 $a(PQKBTitleCode)TC0000687013 035 $a(PQKBWorkID)10734815 035 $a(PQKB)11353425 035 $a(MiAaPQ)EBC3018118 035 $a(Au-PeEL)EBL3018118 035 $a(CaPaEBR)ebr10659040 035 $a(OCoLC)923657235 035 $a(BIP)30661839 035 $a(EXLCZ)992560000000081141 100 $a20100512d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aApplications of swarm intelligence /$fLouis P. Walters, editor 205 $a1st ed. 210 $a[Hauppauge], N.Y. $cNova Science Publishers$dc2011 215 $a1 online resource (234 p.) 225 0 $aEngineering tools, techniques and tables 300 $aDescription based upon print version of record. 311 08$a1-61728-602-8 320 $aIncludes bibliographical references and index. 327 $a""APPLICATIONS OF SWARM INTELLIGENCE""; ""APPLICATIONS OF SWARM INTELLIGENCE""; ""CONTENTS ""; ""PREFACE ""; ""SWARM INTELLIGENCE AND FUZZY SYSTEMS ""; ""Abstract ""; ""1. Optimizing the Parameters of Fuzzy Systems Using Swarm Intelligence Algorithms ""; ""1.1. Fuzzy Systems ""; ""1.1.1. Membership Functions ""; ""1.1.2. Fuzzy Rules ""; ""1.2. Designing a Fuzzy Classifier Using Particle Swarm Optimization Algorithm (PSO) ""; ""1.2.1. Integer-Valued Particle Swarm Optimization with Constriction Coefficient ""; ""1.2.2. Particle Representation""; ""1.2.3. Fitness Function Definition "" 327 $a""1.3. Experimental Results """"1.4. Other Related Researches ""; ""2- Intelligently Controlling the Multi-objective Swarm Intelligence Parameters Using Fuzzy Systems ""; ""2.1. A Review on the Past Researches on Multi-objective PSO ""; ""2.2. Fuzzy-MOPSO Algorithm ""; ""2.2.1. Integer-Valued MOPSO with Constriction Coefficient ""; ""2.2.2. Designing Fuzzy-Controller for MOPSO ""; ""2.2.2.1. Metrics of Performance""; ""a) Minimal spacing ""; ""b) Aggregation factor ""; ""2.2.2.2. Fuzzy Parameters ""; ""a) Inputs of fuzzy controller ""; ""b) Outputs of fuzzy controller ""; ""c) Fuzzy rules "" 327 $a""2.3. Space Allocation (Problem Description and Formulation) """"2.4. Implementation and Experimental Results ""; ""2.4.1. Application on Well-Known Benchmarks ""; ""2.4.2. Application of Fuzzy-MOPSO on Space Allocation ""; ""a) Particle Representation ""; ""b) Experimental and Comparative Results ""; ""3. Conclusion ""; ""References ""; ""EVOLUTIONARY STRATEGIES TO FIND PARETO FRONTS IN MULTIOBJECTIVE PROBLEMS ""; ""Abstract ""; ""1. Introduction ""; ""2. Pareto Optimality ""; ""3. Multi-objective Optimization with PSO""; ""A1. Algorithm for MOPSO ""; ""4. Movement Strategies "" 327 $a""4.1. Ms1: Pick a Global Guidance Located in the Least Crowded Areas """"A2. Algorithm for Ms1 ""; ""4.2. Ms2: Create the Perturbation with Differential Evolution Concept ""; ""A3. Algorithm for Ms2 ""; ""4.3. Ms3: Search the Unexplored Space in the Non-Dominated Front ""; ""A4. Algorithm for Ms3 ""; ""4.4. Ms4: Combination of Ms1 and Ms2 ""; ""4.5. Ms5: Explore Solution Space with Mixed Particles ""; ""4.6. Ms6: Adaptive Weight Approach ""; ""5. Design of Experiments ""; ""6. Results and Discussions ""; ""7. Conclusions ""; ""Acknowledgment ""; ""References "" 327 $a""PARTICLE SWARM OPTIMIZATION APPLIED TO REAL-WORLD COMBINATORIAL PROBLEMS: THE CASE STUDY OF THE IN-CORE FUEL MANAGEMENT OPTIMIZATION """"Abstract ""; ""1. Introduction ""; ""2. Particle Swarm Optimization ""; ""3. Models of Particle Swarm Optimization for Combinatorial Problems ""; ""4. Particle Swarm Optimization with Random Keys ""; ""4.1. Random Keys ""; ""4.2. Particle Swarm Optimization with Random Keys ""; ""5. Optimization of Real-World Problems: The Case Study of the in-Core Fuel Management Optimization ""; ""5.1. The Traveling Salesman Problem "" 327 $a""5.2. The In-Core Fuel Management Optimization "" 330 $aSwarm Intelligence (SI) describes the evolving collective intelligence of population/groups of autonomous agents with a low level of intelligence. The population of agents interacts with each other or their environment locally using decentralised and self-organisational aspects in their decision making. SI and related sub-methods that follow its principles are used for problem solving in a variety of areas, such as robotics and forecasting. This book discusses swarm intelligence techniques and fuzzy logic as useful tools for solving practical engineering problems and the utilisation of a swarm intelligence algorithm to obtain the optimum neural network structure. Also explored is the application of Particle Swarm Optimization (PSO) methods to inverse heat radiation problems and PSO as a technique in computational electromagnetism problems. 410 0$aEngineering Tools, Techniques and Tables 606 $aProblem solving 606 $aSwarm intelligence 615 0$aProblem solving. 615 0$aSwarm intelligence. 676 $a006.3 701 $aWalters$b Louis P$01871444 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910971364703321 996 $aApplications of swarm intelligence$94480280 997 $aUNINA