**Structure of the book**

This book discusses four ACO technic applications in different industries. Practical examples and scientific details support all the information. The chapters contain enough information for beginners to familiarize themselves with the high technology and science application to solve business problems and more detailed technical information for advanced readers.

Chapter 1 provides a background of ACO analytical model application to help industries make better decisions to optimize processes and reduce costs.

Chapter 2 discusses the application of ACO for the integrated design of Hybrid Electric Vehicles (HEVs). It examines the actual application of continuous ACO for integrated sizing and control design of HEVs to minimize drivetrain cost fuel consumption and address control objectives. The chapter provides valuable information for designers and automotive engineers related to incorporating soft computing, modeling, and simulation concepts into the optimization-based design of HEVs.

The authors of Chapter 3 are members of a research group at the Department of Information Technology Convergence Engineering, School of Electronic Engineering, Kumoh National Institute of Technology, Korea. The researchers for multi-robot systems investigated merging grid maps with ACO. Multi-robot systems have recently come into the spotlight due to their efficiency in performing tasks in a collaborative environment. However, if there is no map in the working environment, each robot must complete SLAM, which is a process that simultaneously performs localization and mapping of the surrounding environment. To operate the multi-robot systems

efficiently, the individual maps must be merged into a collective map that is accurate and complete. When the initial correspondences between the robots are unknown or uncertain, the map merging task becomes more challenging to complete. This chapter describes a novel approach to successfully and efficiently conducting grid-map merging with ACO, one of the well-known sampling-based optimization algorithms. This method was tested with one of the existing grid maps combining algorithms. The results showed that the ACO increased the accuracy of grid-map merging by approximately 20 percent.

In Chapter 4, the authors focus on ACO application for routing in wireless multi-hop networks. Wireless Mesh Networks (WMNs) and Mobile Ad-Hoc Networks (MANETs) are applied in situations where there is no predefined network structure consisting of routers and a base station or where the network is dynamic due to a growing number of nodes or mobile nodes moving into areas that a base station has not previously covered. This chapter introduces Wireless Multi-Hop Networks, their specific challenges, and an overview of the ACO application for routing in such networks.

Chapter 5 is written by an IT manager from IBM Singapore who has worked in advanced analytics applications in different industries for many years. The chapter focuses on preventive, predictive maintenance using ACO. The presented study results of using ACO to reduce maintenance costs in the mining industry can be a compelling case for the researchers who are thinking about a successful example of using advanced analytics to reduce maintenance costs.

We hope this book helps readers, including industry professionals and researchers, better understand ACO model applications in different areas. The chapters in this book present the state of the art of critical topics in ACO. Furthermore, each section's breadth of coverage and depth make it a helpful resource for all managers and engineers interested in the new generation of data analytics applications. Above all, the editor hopes that this volume will spur further discussions on all aspects of ACO application in different industries.

> **Dr. Ali Soofastaei** Artificial Intelligence Center, Vale, Brisbane, Australia
