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[null,"1.","Record Nr.",null,"UNINA9910971364703321"]
[null,null,"Titolo",null,"Applications of swarm intelligence / / Louis P. Walters, editor"]
[null,null,"Pubbl/distr/stampa",null,"[Hauppauge], N.Y., : Nova Science Publishers, c2011"]
[null,null,"ISBN",null,"1-61728-813-6"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (234 p.)"]
[null,null,"Collana",null,"Engineering tools, techniques and tables"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Problem solving"]
[{"$nulls":4},"Swarm intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"\"\"APPLICATIONS OF SWARM INTELLIGENCE\"\"; \"\"APPLICATIONS OF SWARM INTELLIGENCE\"\"; \"\"CONTENTS \"\"; \"\"PREFACE \"\"; \"\"SWARM INTELLIGENCE  AND FUZZY SYSTEMS \"\"; \"\"Abstract \"\"; \"\"1. Optimizing the Parameters of Fuzzy Systems Using Swarm Intelligence Algorithms \"\"; \"\"1.1. Fuzzy Systems \"\"; \"\"1.1.1. Membership Functions \"\"; \"\"1.1.2. Fuzzy Rules \"\"; \"\"1.2. Designing a Fuzzy Classifier Using Particle Swarm Optimization Algorithm (PSO) \"\"; \"\"1.2.1. Integer-Valued Particle Swarm Optimization with Constriction Coefficient \"\"; \"\"1.2.2. Particle Representation\"\"; \"\"1.2.3. Fitness Function Definition \"\""]
[{"$nulls":4},"\"\"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 \"\""]
[{"$nulls":4},"\"\"2.3. Space Allocation (Problem Description and Formulation) \"\"\"\"2.4. Implementation and Experimental Results \"\"; \"\"2.4.1. Application on Well-Known Benchmarks \"\"; \"\"2.4.2. Application of Fuzzy-MOPSO on Space Allocation \"\"; \"\"a) Particle Representation \"\"; \"\"b) Experimental and Comparative Results \"\"; \"\"3. Conclusion \"\"; \"\"References \"\"; \"\"EVOLUTIONARY STRATEGIES TO FIND PARETO FRONTS IN MULTIOBJECTIVE PROBLEMS \"\"; \"\"Abstract \"\"; \"\"1. Introduction \"\"; \"\"2. Pareto Optimality \"\"; \"\"3. Multi-objective Optimization with PSO\"\"; \"\"A1. "]
[{"$nulls":4},"Algorithm for MOPSO \"\"; \"\"4. Movement Strategies \"\""]
[{"$nulls":4},"\"\"4.1. Ms1: Pick a Global Guidance Located in the Least Crowded Areas \"\"\"\"A2. Algorithm for Ms1 \"\"; \"\"4.2. Ms2: Create the Perturbation with Differential Evolution Concept \"\"; \"\"A3. Algorithm for Ms2 \"\"; \"\"4.3. Ms3: Search the Unexplored Space in the Non-Dominated Front \"\"; \"\"A4. Algorithm for Ms3 \"\"; \"\"4.4. Ms4: Combination of Ms1 and Ms2 \"\"; \"\"4.5. Ms5: Explore Solution Space with Mixed Particles \"\"; \"\"4.6. Ms6: Adaptive Weight Approach \"\"; \"\"5. Design of Experiments \"\"; \"\"6. Results and Discussions \"\"; \"\"7. Conclusions \"\"; \"\"Acknowledgment \"\"; \"\"References \"\""]
[{"$nulls":4},"\"\"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 \"\""]
[{"$nulls":4},"\"\"5.2. The In-Core Fuel Management Optimization \"\""]
[null,null,null,"Sommario/riassunto","Swarm 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."]