Preface

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‐ ing these limited resources.

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 to solve complex planning problems in wartime operations.

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 the number of independent variables of an optimization problem.

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 nonlinear optimization problems.

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‐ proved.

This book presents some real-world applications of PSO. We intend to give a perspective to the readers who are planning to use PSO to solve a real-life problem.

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

PSO with a bio-inspired aging model is introduced in Chapter 2. This model is proposed for alleviating the premature convergence problem.

"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‐ viding proper overcurrent relay settings in the grid. The second case study system, the eco‐ nomic dispatch of a 15-unit system, is solved with PSO.

In Chapter 4, "Stochastic Greedy-Based PSO for Workflow Application in Grid" is presented.

In Chapter 5, "Performance Comparison of PSO and Its New Variants in the Context of VLSI Global Routing" is presented.

In Chapter 6, one of the most studied problems in electrical engineering "Combined Eco‐ nomic Emission Dispatch Problem with Valve-Point Effect Using Hybrid NSGA II-MOPSO" is presented.

I hope this book will be helpful to the readers.

**Pakize Erdoğmuş** Duzce University Turkey **Chapter 1**

**Provisional chapter**

**Introductory Chapter: Swarm Intelligence and Particle**

**Introductory Chapter: Swarm Intelligence and Particle** 

In order to survive, the main objective of all creatures is foraging. Foraging behavior is cooperative in the same species. Each agent in the swarm communicates with others in such a way to find the food in the shortest time and way. This capability of all lively beings gives inspiration to the human being in order to find solutions to the optimization problems. Collective

Most of the animals live as social groups in order to find foods easily and protect from the enemies to survive. Each individual lives in their habitat. Looking for food, they use their own experiences called cognitive movements as well as the experience of their leaders called

Optimization is to find the best solution to a given problem under some constraints. All disciplines use optimization for finding the best solution for their problems. Optimization is the first and foremost objective for engineers too. So especially in the future engineering applica-

Optimization is everywhere. In the production of a new device, in a new artificial intelligence technique, in a big data application or in a deep learning network, optimization is the most important part of the application. To design a device with optimum sizes using minimum energy, to train a network, to minimize the error between the desired output and real output

Because of the difficulties of classical optimization algorithms, scientists have started to find an easy way to solve their problems in the last 1960s. The development of the computers made the efforts of the scientists easy, and completely new problem solution techniques are

foraging behaviors of the lively beings are called swarm intelligence.

tions, optimization will be an indispensable part of the product.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

DOI: 10.5772/intechopen.74076

**Swarm Optimization**

**Swarm Optimization**

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.74076

Pakize Erdogmus

Pakize Erdogmus

**1. Introduction**

social movements.

values, optimization is required.

#### **Introductory Chapter: Swarm Intelligence and Particle Swarm Optimization Introductory Chapter: Swarm Intelligence and Particle Swarm Optimization**

DOI: 10.5772/intechopen.74076

Pakize Erdogmus

viding proper overcurrent relay settings in the grid. The second case study system, the eco‐

In Chapter 4, "Stochastic Greedy-Based PSO for Workflow Application in Grid" is presented. In Chapter 5, "Performance Comparison of PSO and Its New Variants in the Context of VLSI

In Chapter 6, one of the most studied problems in electrical engineering "Combined Eco‐ nomic Emission Dispatch Problem with Valve-Point Effect Using Hybrid NSGA II-MOPSO"

> **Pakize Erdoğmuş** Duzce University

> > Turkey

nomic dispatch of a 15-unit system, is solved with PSO.

I hope this book will be helpful to the readers.

Global Routing" is presented.

is presented.

VIII Preface

Additional information is available at the end of the chapter Pakize ErdogmusAdditional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.74076

**1. Introduction**

In order to survive, the main objective of all creatures is foraging. Foraging behavior is cooperative in the same species. Each agent in the swarm communicates with others in such a way to find the food in the shortest time and way. This capability of all lively beings gives inspiration to the human being in order to find solutions to the optimization problems. Collective foraging behaviors of the lively beings are called swarm intelligence.

Most of the animals live as social groups in order to find foods easily and protect from the enemies to survive. Each individual lives in their habitat. Looking for food, they use their own experiences called cognitive movements as well as the experience of their leaders called social movements.

Optimization is to find the best solution to a given problem under some constraints. All disciplines use optimization for finding the best solution for their problems. Optimization is the first and foremost objective for engineers too. So especially in the future engineering applications, optimization will be an indispensable part of the product.

Optimization is everywhere. In the production of a new device, in a new artificial intelligence technique, in a big data application or in a deep learning network, optimization is the most important part of the application. To design a device with optimum sizes using minimum energy, to train a network, to minimize the error between the desired output and real output values, optimization is required.

Because of the difficulties of classical optimization algorithms, scientists have started to find an easy way to solve their problems in the last 1960s. The development of the computers made the efforts of the scientists easy, and completely new problem solution techniques are

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

studied. These techniques using heuristic information were derivative free, easy to implement, and shorten the solution time. The first product of these studies is genetic algorithm (GA) developed by Holland [1]. The evolutionary idea has been applied to the solution of the optimization problems. Instead of the evolving only one solution, a group of solutions called population has been used in the algorithm. Each solution is called individual. By this way, running such algorithms with multiple processors could be possible. After GA, simulated annealing [2] has been generally accepted as the second algorithm, inspired from the annealing process of physical materials. In high temperatures, particles move randomly in order to explore the solution space. While temperature is decreasing, particles try to create a perfect crystalline structure, only with local movements.

[15], image filtering [16] problems are solved with PSO. In Industrial Engineering, examination timetabling problems [17], traveling salesman problem [18], and job-shop scheduling problems [19] are solved with PSO. In Robotics, particle swarm optimization in coconut tree plucking robot is introduced [20], and path planning problem is solved with PSO [21]. In these studies, it has been proven PSO success in point of both performance and speed in most of the studies. After the main PSO algorithm was studied and evolved with some parameters, hybrid algorithms were designed and developed by researchers. The most prominent ones are hybrid PSO with genetic algorithm (GA) [22] and particle swarm optimization with chaos [23–25] and quantum chaotic PSO [26]. In the next section, some of the recent hybrid PSO algorithms

Introductory Chapter: Swarm Intelligence and Particle Swarm Optimization

http://dx.doi.org/10.5772/intechopen.74076

3

Hybrid algorithms are quite successful since they combine both algorithms' powerful sides. Since PSO is quite fast algorithm, nearly all newly developed algorithm combined with PSO. Some of the recent studies using swarm intelligence are crow search algorithm (CSA) [27], ant lion optimizer (ALO) [28], the whale optimization algorithm (WOA) [29], grey wolf optimizer (GWO) [30], monarch butterfly optimization (MBO) [31], moth flame optimization [32], selfish herd optimization (SHO) [33], and salp swarm optimization (SSO) [34]. Since the algorithms stated in the following paragraph are quite new, according to the Web of Science

Firefly algorithm (FFA) [35], bacterial foraging optimization algorithm (BFOA) [36], ant colony optimization (ACO) [37], artificial bee colony (ABC) [38], and cuckoo search (CS) [39] are some of the algorithms using swarm intelligence improved in the last decade. There are

FFA is improved by mimicking the flashing activity of fireflies. FFA is similar most of the swarm intelligence algorithm. Fireflies are located in a position in the solution space randomly initially. The fitness of the fireflies is calculated according to the light intensity. The next location of each firefly is calculated according to the current position, randomness and attractiveness. The hybrid algorithm combined PSO with firefly optimization [40] proposes a technique for the detection of Bundle Branch Block (BBB), one of the abnormal cardiac beat, using hybrid firefly and particle swarm optimization (FFPSO) technique in combination with

BFOA is inspired by the social foraging behavior of *Escherichia coli*. BFOA is an efficient algorithm in solving real-world optimization problems. Chemotaxis process simulates the movement of an *E. coli* cell through swimming and tumbling via flagella. *E. coli* cells like particles move in solution space and change their location with a formula-dependent previous

**3. Hybrid PSO algorithms using swarm intelligence**

records, there is not any hybrid study combining PSO with them.

Levenberg Marquardt Neural Network (LMNN) classifier.

**3.2. Bacterial foraging optimization algorithm**

hybrid versions of these algorithms with PSO.

**3.1. Firefly algorithm**

are presented.
