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BL
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| Record Number
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889368
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| Main Entry
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Rakshit, Pratyusha
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| Title & Author
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Principles in noisy optimization : : applied to multi-agent coordination /\ Pratyusha Rakshit, Amit Konar.
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| Publication Statement
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Singapore :: Springer,, 2018.
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| Series Statement
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Cognitive intelligence and robotics,
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| Page. NO
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1 online resource (xvi, 367 pages) :: illustrations (some color)
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| ISBN
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9789811086427
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: 9811086427
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9789811086410
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9811086419
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Intro; Preface; Contents; About the Authors; 1 Foundation in Evolutionary Optimization; 1.1 Optimization Problem-A Formal Definition; 1.2 Optimization Problems with and Without Constraints; 1.2.1 Handling Equality Constraints; 1.2.2 Handling Inequality Constraints; 1.3 Traditional Calculus-Based Optimization Techniques; 1.3.1 Gradient Descent Algorithm; 1.3.2 Steepest Descent Algorithm; 1.3.3 Newton's Method; 1.3.4 Quasi-Newton's Method; 1.4 Optimization of Discontinuous Function Using Evolutionary Algorithms; 1.4.1 Limitations of Derivative-Based Techniques
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1.4.2 Emergence of Evolutionary Algorithms1.5 Selective Evolutionary Algorithms; 1.5.1 Genetic Algorithm; 1.5.2 Differential Evolution; 1.5.3 Particle Swarm Optimization; 1.6 Constraint Handling in Evolutionary Optimization; 1.7 Handling Multiple Objectives in Evolutionary Optimization; 1.7.1 Weighted Sum Approach; 1.7.2 Pareto Dominance Criteria; 1.7.3 Non-dominated Sorting Genetic Algorithm-II; 1.8 Performance Analysis of Evolutionary Algorithms; 1.8.1 Benchmark Functions and Evaluation Metrics for Single-Objective Evolutionary Algorithms
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1.8.2 Benchmark Functions and Evaluation Metrics for Multi-objective Evolutionary Algorithms1.9 Applications of Evolutionary Optimization Algorithms; 1.10 Summary; References; 2 Agents and Multi-agent Coordination; 2.1 Defining Agent; 2.2 Agent Perception; 2.3 Performance Measure of Agent; 2.4 Agent Environment; 2.5 Agent Architecture; 2.5.1 Logic-based Architecture; 2.5.2 Subsumption Architecture; 2.5.3 Belief-Desire-Intention Architecture; 2.5.4 Layered Architecture; 2.6 Agent Classes; 2.6.1 Simple Reflex Agent; 2.6.2 Model-based Reflex Agent; 2.6.3 Goal-based Agent
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2.6.4 Utility-based Agent2.6.5 Learning Agent; 2.7 Multi-agent System; 2.8 Multi-agent Coordination; 2.9 Multi-agent Planning; 2.10 Multi-agent Learning; 2.11 Evolutionary Optimization Approach to Multi-agent Robotics; 2.12 Evolutionary Optimization Approach to Multi-agent Robotics in the Presence of Measurement Noise; 2.13 Summary; References; 3 Recent Advances in Evolutionary Optimization in Noisy Environment- A Comprehensive Survey; 3.1 Introduction; 3.2 Noisy Optimization Using Explicit Averaging; 3.2.1 Time-Based Sampling; 3.2.2 Domination Strength-Based Sampling
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3.2.3 Rank-Based Sampling3.2.4 Standard Error Dynamic Resampling (SEDR); 3.2.5 m-Level Dynamic Resampling (mLDR); 3.2.6 Fitness-Based Dynamic Resampling (FBDR); 3.2.7 Hybrid Sampling; 3.2.8 Sampling Based on Fitness Variance in Local Neighborhood; 3.2.9 Progress-Based Dynamic Sampling; 3.2.10 Distance-Based Dynamic Sampling; 3.2.11 Confidence-Based Dynamic Resampling (CDR); 3.2.12 Noise Analysis Selection; 3.2.13 Optimal Computing Budget Allocation (OCBA); 3.3 Effective Fitness Estimation; 3.3.1 Expected Fitness Estimation Using Uniform Fitness Interval
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| Abstract
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Noisy optimization is a topic of growing interest for researchers working on mainstream optimization problems. Although several techniques for dealing with stochastic noise in optimization problems are covered in journals and conference proceedings, today there are virtually no books that approach noisy optimization from a layman's perspective; this book remedies that gap. Beginning with the foundations of evolutionary optimization, the book subsequently explores the principles of noisy optimization in single and multi-objective settings, and presents detailed illustrations of the principles developed for application in real-world multi-agent coordination problems. Special emphasis is given to the design of intelligent algorithms for noisy optimization in real-time applications. The book is unique in terms of its content, writing style and above all its simplicity, which will appeal to readers with a broad range of backgrounds. The book is divided into 7 chapters, the first of which provides an introduction to Swarm and Evolutionary Optimization algorithms. Chapter 2 includes a thorough review of agent architectures for multi-agent coordination. In turn, Chapter 3 provides an extensive review of noisy optimization, while Chapter 4 addresses issues of noise handling in the context of single-objective optimization problems. An illustrative case study on multi-robot path-planning in the presence of measurement noise is also highlighted in this chapter. Chapter 5 deals with noisy multi-objective optimization and includes a case study on noisy multi-robot box-pushing. In Chapter 6, the authors examine the scope of various algorithms in noisy optimization problems. Lastly, Chapter 7 summarizes the main results obtained in the previous chapters and elaborates on the book's potential with regard to real-world noisy optimization problems.
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| Subject
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Mathematical optimization.
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| Subject
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Multiagent systems.
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| Subject
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Mathematical optimization.
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| Subject
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Multiagent systems.
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| Dewey Classification
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519.6
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| LC Classification
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QA402.5
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| Added Entry
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Konar, Amit
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