Preface

Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting today the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. Undoubtedly, the main influences behind the conception of this stream are the classical Ant Colony Optimization and Particle Swarm Optimization. These algorithms started the interest in this field, being the origin and main inspiration for subsequent research. Today, a myriad of novel methods has been proposed, considering many different inspirational sources, such as the behavioral patterns of animals such as bats, fireflies, bees, or cuckoos; social and political behaviors such as the imperialism or hierarchical societies; or physical processes such as optics systems, electromagnetic theory, or gravitational dynamics. This book focuses on the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This material unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence.

> **Javier Del Ser**  TECNALIA Research and Innovation, Derio, Bizkaia University of the Basque Country (UPV/EHU), Bilbao, Bizkaia

> > **Esther Villar and Eneko Osaba**  TECNALIA Research and Innovation, Derio, Bizkaia

Chapter 1

Introductory Chapter: Swarm

New Perspectives, and

Eneko Osaba, Esther Villar and Javier Del Ser

main advantages of swarm intelligence-based meta-heuristics.

2. Brief history of swarm intelligence

Applications

1. Introduction

in the community.

1

Intelligence - Recent Advances,

Swarm intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting today the most high-growing stream on bioinspired computation community [1]. A clear trend can be deduced by analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has been in crescendo at a notable pace in the last years [2]. Undoubtedly, the main influences behind the conception of this stream are the extraordinarily famous particle swarm optimization (PSO, [3]) and ant colony optimization (ACO, [4]) algorithms. These meta-heuristic lighted the fuse of the success of this knowledge area, being the origin and principal inspiration of their subsequent research. Such remarkable success has led to the proposal of a myriad of novel methods, each one based on a different inspirational source such as the behavioral patterns of animals, social and political behaviors, or physical processes. The constant proposal of new methods showcases the capability and adaptability of this sort of solvers to reach a near-optimal performance over a wide range of high-demanding academic and real-world problems, being this fact one of the

The consolidation of swarm intelligence paradigm came after years of hard and successful scientific work and as a result of the proposal of several groundbreaking and incremental studies, as well as the establishment of some cornerstone concepts

In this regard, two decisive milestones can be highlighted in swarm intelligence history. First of these breakthrough landmarks can be contextualized on horseback between the 1960s and 1970s. Back then, influential researchers such as Schwefel, Fogel, and Rechenberg revealed their first theoretical and practical works related to evolving strategies (ES) and evolutionary programming (EP) [5–7]. An additional innovative notion came to the fore some years later from John H. Holland's hand. This concept is the genetic algorithm (GA, [8]), which was born in 1975 sowing the seed of the knowledge field today known as bioinspired computation. All the three outlined streams (i.e., ES, EP, and GA) coexisted in a separated fashion until the

## Chapter 1
