Swarm Intelligence, Focus on Ant and Particle Swarm Optimization

Swarm Intelligence, Focus on Ant and Particle Swarm Optimization

In the era globalisation the emerging technologies are governing engineering industries to a multifaceted state. The escalating complexity has demanded researchers to find the possible ways of easing the solution of the problems.


This has motivated the researchers to grasp ideas from the nature and implant it in the engineering sciences. This way of thinking led to emergence of many biologically inspired algorithms that have proven to be efficient in handling the computationally complex problems with competence such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), etc.

Motivated by the capability of the biologically inspired algorithms the present book on “Swarm Intelligence: Focus on Ant and Particle Swarm Optimization” aims to present recent developments and applications concerning optimization with swarm intelligence techniques. The papers selected for this book comprise a cross-section of topics that reflect a variety of perspectives and disciplinary backgrounds. In addition to the introduction of new concepts of swarm intelligence, this book also presented some selected representative case studies covering power plant maintenance scheduling; geotechnical engineering; design and machining tolerances; layout problems; manufacturing process plan; job-shop scheduling; structural design; environmental dispatching problems; wireless communication; water distribution systems; multi-plant supply chain; fault diagnosis of airplane engines; and process scheduling. I believe these 27 chapters presented in this book adequately reflect these topics.

Table of Contents

  • Chaotic Rough Particle Swarm Optimization Algorithms
  • Power Plant Maintenance Scheduling Using Ant Colony Optimization
  • Particle Swarm Optimization for Simultaneous Optimization of Design and Machining Tolerances
  • Hybrid Optimisation Method for the Facility Layout Problem
  • Selection of Best Alternative Process Plan in Automated Manufacturing Environment: An Approach Based on Particle Swarm Optimization
  • Job-shop Scheduling and Visibility Studies with a Hybrid ACO Algorithm
  • Particle Swarm Optimization in Structural Design
  • Reserve-Constrained Multiarea Environmental/Economic Dispatch Using Enhanced Particle Swarm Optimization
  • Hybrid Ant Colony Optimization for the Channel Assignment Problem in Wireless Communication
  • Case Study Based Convergence Behaviour Analysis of ACO Applied to Optimal Design of Water Distribution Systems
  • A CMPSO Algorithm Based Approach to Solve the Multi-plant Supply Chain Problem
  • Ant Colonies for Performance Optimization of Multi-components Systems Subject to Random Failures
  • Distributed Particle Swarm Optimization for Structural Bayesian Network Learning
  • CSV-PSO and Its Application in Geotechnical Engineering
  • Application of PSO to Design UPFC-based Stabilizers
  • Integration Method of Ant Colony Algorithm and Rough Set Theory for Simultaneous Real Value Attribute Discretization and Attribute Reduction
  • A New Ant Colony Optimization Approach for the Degree-Constrained Minimum Spanning Tree Problem Using Pruefer and Blob Codes Tree Coding
  • Robust PSO-Based Constrained Optimization by Perturbing the Particle’s Memory
  • Using Crowding Distance to Improve Multi-Objective PSO with Local Search
  • Simulation Optimization Using Swarm Intelligence as Tool for Cooperation Strategy Design in 3D Predator-Prey Game
  • Differential Meta-model and Particle Swarm Optimization
  • Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem
  • Finite Element Mesh Decomposition Using Evolving Ant Colony Optimization
  • Swarm Intelligence and Image Segmentation
  • Particle Swarm Optimization – Stochastic Trajectory Analysis and Parameter Selection
  • Stochastic Metaheuristics as Sampling Techniques using Swarm Intelligence
  • Artificial Ants in the Real World: Solving On-line Problems Using Ant Colony Optimization
  • Preface: Swarm Intelligence, Focus on Ant and Particle Swarm Optimization

Book Details

Author(s): Felix T.S. Chan and Manoj Kumar Tiwari
Format(s): PDF
File size: 9.89 MB
Number of pages: 532
Link: Download or read online.

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