Advances in swarm intelligence : 11th International Conference, ICSI 2020, Belgrade, Serbia, July 14-20, 2020, Proceedings

Advances in swarm intelligence : 11th International Conference, ICSI 2020, Belgrade, Serbia, July 14-20, 2020, Proceedings / Ying Tan, Yuhui Shi, Milan Tuba (eds.).

This book constitutes the proceedings of the 11th International Conference on Advances in Swarm Intelligence, ICSI 2020, held in July 2020 in Belgrade, Serbia. Due to the COVID-19 pandemic the conference was held virtually. The 63 papers included in this volume were carefully reviewed and selected f...

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Bibliographic Details
Corporate Author: ICSI (Conference) Online)
Other Authors: Tan, Ying, 1964-, Shi, Yuhui, Tuba, Milan, 1952-
Format: eBook
Language:English
Published: Cham : Springer, 2020.
Series:Lecture notes in computer science ; 12145.
LNCS sublibrary. Theoretical computer science and general issues.
Subjects:
Swarm intelligence > Congresses.
Computer networking & communications.
Information technology: general issues.
Computer science.
Computers > Networking > General.
Computers > Data Processing.
Computers > Social Aspects > Human-Computer Interaction.
Computers > Online Services > General.
Computers > Computer Science.
Swarm intelligence
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Online Access:Click for online access
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents
  • I Swarm Intelligence and Nature-Inspired Computing
  • Swarm Intelligence in Data Science: Applications, Opportunities and Challenges
  • 1 Introduction
  • 2 Swarm Intelligence Algorithms
  • 2.1 General Procedure of SI Algorithms
  • 2.2 Developments
  • 3 Theoretical Applications
  • 3.1 Dimensionality Reduction
  • 3.2 Classification and Clustering
  • 3.3 Automated Machine Learning
  • 4 Real-World Applications
  • 5 Opportunities and Challenges
  • 6 Conclusion
  • References
  • Synchronized Swarm Operation
  • 1 Introduction
  • 2 Petri-Markov Model of Synchronized Operation
  • 3 Transformation PMN to Complex Semi-Markov Process
  • 4 Effectiveness of Synchronization
  • 5 Conclusion
  • References
  • Prediction of Photovoltaic Power Using Nature-Inspired Computing
  • 1 Introduction
  • 2 Related Work
  • 3 Firefly Algorithm
  • 3.1 Movement Equation
  • 4 Methods of Prediction
  • 4.1 Weather Classes Discovery
  • 4.2 Use of Multiple Weather Classes for Prediction
  • 4.3 Bias Correction
  • 5 Data
  • 5.1 Data Preprocessing
  • 6 Experiments
  • 6.1 Evaluation Metrics
  • 6.2 Experiments with Settings of Firefly Algorithm
  • 6.3 Experiments with Hour Ahead Prediction
  • 6.4 Comparison with Existing Solution
  • 7 Conclusion
  • References
  • A Two-Step Approach to the Search of Minimum Energy Designs via Swarm Intelligence
  • 1 Introduction
  • 2 The Swarm Intelligence Based Method: A Review
  • 2.1 The Swarm Intelligence Based (SIB) Method
  • 3 The Two-Step Swarm Intelligence Based Method
  • 4 Demonstration: A Search of Minimum Energy Designs
  • 4.1 A Brief Introduction to Minimum Energy Design (MED)
  • 4.2 Implementation of the Two-Step SIB Method
  • 4.3 Result
  • 5 Discussion and Conclusion
  • References
  • On Assessing the Temporal Characteristics of Reaching the Milestone by a Swarm
  • 1 Introduction
  • 2 Dynamics of Swarm Unit Longitudinal Movement
  • 3 Reaching the Milestone by the Swarm Unit
  • 4 Reaching the Milestone by a Swarm
  • 5 Computer Experiment
  • 6 Conclusion
  • References
  • I Swarm-Based Computing Algorithms for Optimization
  • Learning Automata-Based Fireworks Algorithm on Adaptive Assigning Sparks
  • 1 Introduction
  • 2 Related Work
  • 2.1 Fireworks Algorithm
  • 2.2 Learning Automata
  • 3 Learning Automata-Based Fireworks Algorithm
  • 3.1 m-DPRI
  • 3.2 Assigning Sparks
  • 3.3 Learning Automata-Based Fireworks Algorithm
  • 4 Experimental Results and Comparisons
  • 4.1 Benchmark and Experimental Settings
  • 4.2 Experimental Results and Comparison
  • 5 Conclusion
  • References
  • Binary Pigeon-Inspired Optimization for Quadrotor Swarm Formation Control
  • 1 Introduction
  • 2 Dynamics Modeling and Control of a Quadrotor
  • 2.1 Coordinate System
  • 2.2 Dynamics Modeling
  • 2.3 The Strategy of Control Design
  • 3 The BPIO Algorithm
  • 3.1 Pigeon-Inspired Optimization
  • 3.2 Binary Pigeon-Inspired Optimization
  • 4 BPIO for the Quadrotor Swarm Formation Control

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