What Is Bio-Inspired Computing?

This book is the author's systematic research in the field of biological heuristic computing. The book systematically and deeply introduces the origin, model, theory and application fields of bio-inspired computing. Based on the ideas and theories of biological evolution, symbiotic evolution, and complex adaptive systems, it proposes three levels based on individuals, groups, and communities. A unified method of biologically inspired computing for biological behavior evolution models, and taking several new implementation models as examples, the algorithm design, modeling simulation, and optimization problem solving based on a unified framework are made to make the concept of biologically inspired computing from macro to micro to The macro, from unity to diversity to unity have been systematically demonstrated. The publication of this book can provide new ideas and methods for research and applications in the field of bio-inspired computing.

Biologically Inspired Computing: Individual, Group, and Community Evolution Models and Methods

This book can be used as a reference for graduate students, teachers, scientific researchers, and engineering and technical personnel in related fields such as intelligent science, automation, computing science, and electronic information. ^[1]

Chapter 1 Introduction

1.1 From Bionics to Biologically Inspired Computing

1.2 Bio-inspired computing and complex adaptive systems

1.3 The main research branches of bio-inspired computing

1.4 Limitations of Bioinspired Computing Research

references

Chapter 2 Review of Bioinspired Computing Research

introduction

2.1 Overview of Implementation Models for Bio-inspired Computing

2.2 Overview of bio-inspired computing models

2.2.1 Genetic Algorithm

2.2.2 Particle Swarm Optimization

2.2.3 Ant colony optimization

2.2.4 Artificial Bee Colony Algorithm

2.2.5 Bacterial Foraging Algorithm

2.2.6 Group Search Algorithm

2.2.7 DNA Computing

2.2.8 Self-organizing Migration Algorithm

2.2.9 Membrane calculation

2.2.10 Cellular Automata

2.2.11 Neural Network

2.2.12 Artificial immune system

2.3 Applications and Development Trends of Bio-inspired Computing

2.3.1 Overview of Natural Computing Applications

2.3.2 Application Analysis and Outlook

references

Chapter 3 Theoretical Basis and Unified Model

introduction

3.1 Biological evolution and optimization

3.1.1 Survival of the fittest-survival competition

3.1.2 Adaptation and Efficiency

3.2 Symbiotic evolution and population dynamics

3.2.1 Symbiosis and Co-evolution

3.2.2 Dynamic description of co-evolution of multiple groups

3.3 Complex systems and emergence

3.3.1 Complex adaptive systems and biological evolution

3.3.2 Modeling Complex Systems of Biological Evolution

3.4 A unified framework model for bio-inspired computing

3.4.1 The unified framework concept of biologically inspired computing model

3.4.2 Individual-group-community: a general formal description of the biologically-inspired computing model

3.4.3 Environment

references

Chapter 4 Biological Individual Behavior Patterns and Adaptive Optimization Methods

introduction

4.1 Individual behavior patterns in natural evolution

4.1.1 Classification of foraging behavior of biological individuals

4.1.2 Adaptive Subject

4.1.3 Efficiency and Optimal Foraging Theory

4.2 Design of Computing Models Based on Biological Individual Behavior

4.2.1 Unified optimization framework based on biological individual behavior

4.2.2 Basic operations based on biological individual behavior

4.3 Biological individual modeling and simulation analysis

4.3.1 Formal definition of individuals in biological systems

4.3.2 Modeling and Simulation Analysis of Typical Biological Individual Behavior

4.3.3 Interaction description and rule model of individual environment

4.4 Bacterial adaptive foraging optimization algorithm

4.4.1 Basic idea and process of algorithm

4.4.2 Formal description of the algorithm

4.4.3 ABFO algorithm implementation steps

4.4.4 Algorithm Performance Analysis

4.5 Plant root adaptive growth optimization algorithm

4.5.1 Basic idea of the algorithm

4.5.2 Formal description of the algorithm

4.5.3 Algorithm Flow

4.5.4 Algorithm Performance Analysis

references

Chapter 5 Biological Population Information Exchange Models and Life Cycle Group Search Strategies

introduction

5.1 Information exchange and collaboration models within a single species group in nature

5.1.1 Biological population

5.1.2 Information exchange

5.1.3 Collaboration and Distributed Control

5.2 Design of calculation models based on the behavior of biological groups

5.2.1 A unified optimization framework based on the behavior of biological groups

5.2.2 Basic operations based on biological group behavior

5.3 Biological population modeling and simulation analysis

5.3.1 Formal definition of biological system population

5.3.2 Individual communication model within the population

5.3.3 Task Division

5.3.4 Population evolution model

5.4 Bacterial foraging algorithm and its performance analysis based on life cycle and social learning

5.4.1 Basic idea and process of algorithm

5.4.2 Formal description of the algorithm

5.4.3 Algorithm Performance Analysis

5.5 Life Cycle Group Search Optimization Algorithm and Performance Analysis

5.5.1 Basic idea and process of algorithm

5.5.2 Formal description of the algorithm

5.5.3 Experimental settings

5.5.4 Algorithm Performance Analysis: Unconstrained Function

5.5.5 Algorithm Performance Analysis: Constrained Function

references

Chapter 6 Biological Community Evolution Models and Optimization Algorithms

introduction

6.1 Population evolution patterns in biological community evolution

6.1.1 Hierarchical information network topology of biological communities

6.1.2 Polymorphism of population symbiosis patterns in biological communities

6.1.3 Population growth, migration and extinction patterns in biological communities

6.2 Design of Computational Models Based on Biological Community Evolution

6.2.1 A unified optimization framework based on the evolution of biological communities

6.2.2 Basic operations based on biological community evolution

6.3 Biological community modeling and simulation analysis

6.3.1 Formal definition of biological system community

6.3.2 Formal Definition of Community Topology

6.3.3 Modeling and simulation of biological community evolution based on different population relationships

6.4 Optimization Model and Algorithm Example Design Based on Biological Community Evolution

6.4.1 Development Status of Co-evolutionary Algorithms

6.4.2 Unified Model of Multi-Group Co-evolution

6.4.3 Multi-group symbiosis co-evolution particle swarm optimization algorithm

6.4.4 Algorithm Performance Analysis

6.4.5 Scheduling of RFID Network Reader Based on MSPSO

6.5 Multi-Group Multi-Objective Artificial Bee Colony Algorithm

6.5.1 Basic idea and process of algorithm

6.5.2 Formal description of the algorithm

6.5.3 Algorithm Performance Analysis

references

Chapter 7 Commentary and Outlook

introduction

7.1 Theoretical Basic Research Prospects

7.1.1 Research on the Effectiveness of Bio-inspired Computing

7.1.2 Convergence Research on Biologically Inspired Computing

7.1.3 Evaluation criteria for bio-inspired calculation methods

7.2 Research Prospects on Algorithm Design

7.2.1 Design of relevant algorithms at the niche level

7.2.2 Design of related algorithms at dynamic environment level

7.3 Prospects for the Application of Biological Heuristic Computing

7.3.1 Artificial brain

7.3.2 Evolving Hardware

7.3.3 Nanomolecular Biology

7.3.4 Virtual creatures

7.3.5 Cloud Computing

references

Appendix A Standard Test Functions

A.1 Unconstrained single target

A.2 Single target has constraints

A.3 Unconstrained multi-target

A.4 Constraints on multiple targets

Further reading ^[1]