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Table of Contents
Preface; References; Contents; 1 Membrane Computing
Key Concepts and Definitions; 1.1 Introduction; 1.2 Origins of Membrane Computing; 1.3 Preliminary Concepts and Notations; 1.4 Membrane Computing Concepts; 1.5 Summary; References; 2 Fundamentals of Evolutionary Computation; 2.1 Introduction; 2.2 Genetic Algorithms; 2.3 Quantum-Inspired Evolutionary Algorithms; 2.4 Ant Colony Optimization; 2.5 Particle Swarm Optimization; 2.6 Differential Evolution; 2.7 Conclusions; References; 3 Membrane Algorithms; 3.1 Introduction; 3.2 Membrane Algorithms with Nested Membrane Structure; 3.2.1 Principle
3.2.2 Genetic Algorithm Based on P System3.3 Membrane Algorithms with One-Level Membrane Structure; 3.3.1 Principle; 3.3.2 Quantum-Inspired Evolutionary Algorithm Based on P Systems; 3.3.3 Ant Colony Optimization Based on P Systems; 3.3.4 Differential Evolution Based on P Systems; 3.4 Membrane Algorithms with Hybrid Hierarchical Membrane Structure; 3.5 Membrane Algorithms with Dynamic Hierarchical Membrane Structure; 3.5.1 Brief Introduction; 3.5.2 Approximate Algorithm Using P Systems with Active Membranes; 3.6 Membrane Algorithms with Static Network Structure; 3.6.1 Brief Introduction
3.6.2 A Hybrid Approach Based on Differential Evolution and Tissue P Systems3.7 Membrane Algorithms with Dynamic Network Structure; 3.7.1 Brief Introduction; 3.7.2 Population Membrane-System-Inspired Evolutionary Algorithm; 3.7.3 Multi-objective Membrane Algorithm Based on Population P Systems and DE; 3.8 P Systems Roles in Membrane Algorithms; 3.8.1 Population Diversity Analysis; 3.8.2 Convergence Analysis; 3.9 Conclusions; References; 4 Engineering Optimization with Membrane Algorithms; 4.1 Introduction; 4.2 Engineering Optimizations with Cell-Like P Systems; 4.2.1 Signal Analysis
4.2.2 Image Processing4.2.3 Controller Design; 4.2.4 Mobile Robot Path Planning; 4.2.5 Other Applications; 4.3 Engineering Optimization with Tissue-Like P Systems; 4.3.1 Manufacturing Parameter Optimization Problems; 4.3.2 Distribution Network Reconfiguration; 4.4 Engineering Optimization with Neural-Like P Systems; 4.5 Conclusions; References; 5 Electric Power System Fault Diagnosis with Membrane Systems; 5.1 Introduction; 5.2 Preliminaries; 5.2.1 Fuzzy Knowledge Representation and Reasoning; 5.2.2 Essentials of Electric Power System Fault Diagnosis
5.2.3 Principles of Model-Based Fault Diagnosis Methods5.3 Spiking Neural P Systems for Fault Diagnosis; 5.3.1 Models; 5.3.2 Algorithms; 5.4 Fault Diagnosis with Spiking Neural P Systems; 5.4.1 Transformer Fault Diagnosis with rFRSN P Systems; 5.4.2 Traction Power Supply Systems Fault Diagnosis with WFRSN P Systems; 5.4.3 Power Transmission Networks Fault Diagnosis with tFRSN P Systems; 5.5 Conclusions; References; 6 Robot Control with Membrane Systems; 6.1 Introduction; 6.2 Numerical P Systems; 6.2.1 NPS; 6.2.2 An Example for NPS; 6.2.3 ENPS; 6.3 Preliminaries of Mobile Robot Control
Key Concepts and Definitions; 1.1 Introduction; 1.2 Origins of Membrane Computing; 1.3 Preliminary Concepts and Notations; 1.4 Membrane Computing Concepts; 1.5 Summary; References; 2 Fundamentals of Evolutionary Computation; 2.1 Introduction; 2.2 Genetic Algorithms; 2.3 Quantum-Inspired Evolutionary Algorithms; 2.4 Ant Colony Optimization; 2.5 Particle Swarm Optimization; 2.6 Differential Evolution; 2.7 Conclusions; References; 3 Membrane Algorithms; 3.1 Introduction; 3.2 Membrane Algorithms with Nested Membrane Structure; 3.2.1 Principle
3.2.2 Genetic Algorithm Based on P System3.3 Membrane Algorithms with One-Level Membrane Structure; 3.3.1 Principle; 3.3.2 Quantum-Inspired Evolutionary Algorithm Based on P Systems; 3.3.3 Ant Colony Optimization Based on P Systems; 3.3.4 Differential Evolution Based on P Systems; 3.4 Membrane Algorithms with Hybrid Hierarchical Membrane Structure; 3.5 Membrane Algorithms with Dynamic Hierarchical Membrane Structure; 3.5.1 Brief Introduction; 3.5.2 Approximate Algorithm Using P Systems with Active Membranes; 3.6 Membrane Algorithms with Static Network Structure; 3.6.1 Brief Introduction
3.6.2 A Hybrid Approach Based on Differential Evolution and Tissue P Systems3.7 Membrane Algorithms with Dynamic Network Structure; 3.7.1 Brief Introduction; 3.7.2 Population Membrane-System-Inspired Evolutionary Algorithm; 3.7.3 Multi-objective Membrane Algorithm Based on Population P Systems and DE; 3.8 P Systems Roles in Membrane Algorithms; 3.8.1 Population Diversity Analysis; 3.8.2 Convergence Analysis; 3.9 Conclusions; References; 4 Engineering Optimization with Membrane Algorithms; 4.1 Introduction; 4.2 Engineering Optimizations with Cell-Like P Systems; 4.2.1 Signal Analysis
4.2.2 Image Processing4.2.3 Controller Design; 4.2.4 Mobile Robot Path Planning; 4.2.5 Other Applications; 4.3 Engineering Optimization with Tissue-Like P Systems; 4.3.1 Manufacturing Parameter Optimization Problems; 4.3.2 Distribution Network Reconfiguration; 4.4 Engineering Optimization with Neural-Like P Systems; 4.5 Conclusions; References; 5 Electric Power System Fault Diagnosis with Membrane Systems; 5.1 Introduction; 5.2 Preliminaries; 5.2.1 Fuzzy Knowledge Representation and Reasoning; 5.2.2 Essentials of Electric Power System Fault Diagnosis
5.2.3 Principles of Model-Based Fault Diagnosis Methods5.3 Spiking Neural P Systems for Fault Diagnosis; 5.3.1 Models; 5.3.2 Algorithms; 5.4 Fault Diagnosis with Spiking Neural P Systems; 5.4.1 Transformer Fault Diagnosis with rFRSN P Systems; 5.4.2 Traction Power Supply Systems Fault Diagnosis with WFRSN P Systems; 5.4.3 Power Transmission Networks Fault Diagnosis with tFRSN P Systems; 5.5 Conclusions; References; 6 Robot Control with Membrane Systems; 6.1 Introduction; 6.2 Numerical P Systems; 6.2.1 NPS; 6.2.2 An Example for NPS; 6.2.3 ENPS; 6.3 Preliminaries of Mobile Robot Control