Contents

**Preface XI**



Preface

ing these limited resources.

the nonlinear optimization problems.

proved.

plications in the literature.

alleviating the premature convergence problem.

Mankind lives in an environment where resources are limited. Therefore, since the day he existed, he has been trying to find the best solution to the problems he has encountered us‐

Like most of the inventions, optimization studies took a long way in the World War II. Line‐ ar programming was developed as a discipline in the 1940s, motivated initially by the need

With the developments of computers and computer programming, complex optimization problems encountered in the industry have been solved scientifically through modelling and optimization. These efforts have used classical mathematical solution methods and al‐ gorithms. But it has been seen that the solution time has been increased exponentially with

Instead of finding the exact solution of a given problem in a long time, finding near-optimal solution of a given problem in a reasonable solution time has been accepted. So, this type of solution simulates animal behaviours and intelligence. Since most creatures behave optimal‐ ly using their swarm intelligence, scientific studies on optimization have turned their way to these algorithms. Starting with ant colony optimization and particle swarm optimization, optimization algorithms simulating swarm intelligence have increased very much. Today, nearly all living beings' behaviours and their intelligence have been simulated for solving

The intent of this book is to give readers some insights into particle swarm optimization (PSO), which is one of the most used and cited nature-inspired optimization algorithms. PSO developed by Kennedy and Eberhart is both fast convergent and a very simple algo‐ rithm requiring very few parameters. So, since 1995, PSO has been popular and preferred especially in the dynamic optimization. Several hybrid versions of PSO have been im‐

This book presents some real-world applications of PSO. We intend to give a perspective to

Chapter 1 briefs the origins of PSO, improvements in the algorithm, hybrid studies and ap‐

PSO with a bio-inspired aging model is introduced in Chapter 2. This model is proposed for

"PSO Solution for Power System Operation Problems" is presented in Chapter 3. Two case studies were handled. The first case study system investigates applicability of PSO on pro‐

to solve complex planning problems in wartime operations.

the number of independent variables of an optimization problem.

the readers who are planning to use PSO to solve a real-life problem.
