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**Swarm Intelligence and Bio-Inspired Computation**

1. Swarm Intelligence and Bio-Inspired Computation: An Overview

#### by **Xin-She Yang,Mehmet Karamanoglu**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128068876
- Language : En, Es, Fr & De

Swarm intelligence (SI) and bio-inspired computing in general have attracted great interest in almost every area of science, engineering, and industry over the last two decades. In this chapter, we provide an overview of some of the most widely used bio-inspired algorithms, especially those based on SI such as cuckoo search, firefly algorithm, and particle swarm optimization. We also analyze the essence of algorithms and their connections to self-organization. Furthermore, we highlight the main challenging issues associated with these metaheuristic algorithms with in-depth discussions. Finally, we provide some key, open problems that need to be addressed in the next decade.

**Swarm Intelligence and Bio-Inspired Computation**

Theory and Applications

#### by **Xin-She Yang,Zhihua Cui,Renbin Xiao,Amir Hossein Gandomi,Mehmet Karamanoglu**

- Publisher : Newnes
- Release : 2013-05-16
- Pages : 450
- ISBN : 0124051774
- Language : En, Es, Fr & De

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers. Focuses on the introduction and analysis of key algorithms Includes case studies for real-world applications Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book.

**Swarm Intelligence and Bio-Inspired Computation**

12. Bio-Inspired Models for Semantic Web

#### by **Priti Srinivas Sajja,Rajendra Akerkar**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128068981
- Language : En, Es, Fr & De

Bio-inspired models have taken inspiration from the nature to solve challenging problems in an intelligent manner. Major aims of such bio-inspired models of computation are to propose new unconventional computing architectures and novel problem solving paradigms. Computing models such as artificial neural network (ANN), genetic algorithm (GA), and swarm intelligence (SI) are major constituent models of the bio-inspired approach. Applications of these models are ubiquitous and hence proposed to be applied for Semantic Web. The chapter discusses fundamentals of these bio-inspired constituents along with some heuristic that can be used to design and implement these constituents and briefly surveys recent applications of these models for the Semantic Web. The study shows that the objective of the Semantic Web is better met with such approach and the Web can be accessed in more human-oriented way. At the end, a generic framework for web content filtering based on neuro-fuzzy approach is presented. By considering online webpages and fuzzy user profile, the proposed system classifies the webpages into vague categories using a neural network.

**Swarm Intelligence and Bio-Inspired Computation**

14. Modeling to Generate Alternatives Using Biologically Inspired Algorithms

#### by **Raha Imanirad,Julian Scott Yeomans**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128069007
- Language : En, Es, Fr & De

In solving many practical mathematical programming applications, it is generally preferable to formulate several quantifiably good alternatives that provide very different approaches to the particular problem. This is because decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult—if not impossible—to quantify and capture at the time that the supporting decision models are constructed. There are invariably unmodeled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, it is preferable to generate several alternatives that provide multiple, disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known modeled objective(s) but be fundamentally different from each other in terms of the system structures characterized by their decision variables. This solution approach is referred to as modeling-to-generate-alternatives (MGA). This chapter provides a synopsis of various MGA techniques and demonstrates how biologically inspired MGA algorithms are particularly efficient at creating multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces. The efficacy and efficiency of these MGA methods are demonstrated using a number of case studies.

**Swarm Intelligence and Bio-Inspired Computation**

18. Opportunities and Challenges of Integrating Bio-Inspired Optimization and Data Mining Algorithms

#### by **Simon Fong**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 012806904X
- Language : En, Es, Fr & De

Data mining has evolved from methods of simple statistical analysis to complex pattern recognition in the past decades. During the progression, the data mining algorithms are modified or extended in order to overcome some specific problems. This chapter discusses about the prospects of improving data mining algorithms by integrating bio-inspired optimization, which has lately captivated much of researchers’ attention. In particular, high dimensionality and the unavailability of the whole data set (as in stream mining) in the training data have known to be two major challenges. We demonstrated that these two challenges, through two small examples such as K-means clustering and time-series classification, can be overcome by integrating data mining and bio-inspired algorithms.

**Swarm Intelligence and Bio-Inspired Computation**

6. Particle Swarm Algorithm: Convergence and Applications

#### by **Shichang Sun,Hongbo Liu**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128068922
- Language : En, Es, Fr & De

In this chapter, we present the convergence analysis and applications of particle swarm optimization algorithm. Although it is difficult to analyze the convergence of this algorithm, we discuss its convergence based on its iterated function system and probabilistic theory. The dynamic trajectory of the particle is described based on single individual. We also attempt to theoretically prove that the swarm algorithm converges with a probability of 1 toward the global optimal. We apply the algorithms to solve the scheduling problem and peer-to-peer neighbor selection problem. This chapter is also concerned to employ the nature-inspired optimization methods in machine learning. We introduce the swarm algorithm to reoptimize hidden Markov models.

**Swarm Intelligence and Bio-Inspired Computation**

3. Lévy Flights and Global Optimization

#### by **Momin Jamil,Hans-Jürgen Zepernick**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128068892
- Language : En, Es, Fr & De

Random walks play an important and central role in metaheuristic and stochastic optimization algorithms. The two key components of the search process in metaheuristic algorithms (MAs) are intensification and diversification. The overall efficiency of a metaheuristic optimization algorithm depends on a sound balance between these two components. In MAs, exploration is achieved by randomization in combination with a deterministic procedure. In this way, the newly generated solutions are distributed as diversely as possible in the problem search space. In most of the MAs, randomization is realized using a uniform or Gaussian distribution. However, this is not the only way to achieve randomization. In recent years, the use of Lévy distribution has emerged as an alternative to uniform or Gaussian distributions. In view of these details, this chapter focuses on using Lévy flights (LFs) in the context of global optimization. A survey of the most important MAs using LFs to achieve intensification and diversification for solving global optimization problems is presented. The different components and concepts of Lévy-flight-based MAs are discussed and their similarities and differences are analyzed.

**Swarm Intelligence and Bio-Inspired Computation**

11. A Review of the Development and Applications of the Cuckoo Search Algorithm

#### by **Sean Walton,Oubay Hassan,Kenneth Morgan,M. Rowan Brown**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128068973
- Language : En, Es, Fr & De

The cuckoo search is a relatively new gradient free optimization algorithm, which has been growing in popularity. The algorithm aims to replicate the particularly aggressive breeding behavior of cuckoos and it makes use of the Lévy flight, which is an efficient search pattern. In this chapter, the original development of the cuckoo search is discussed and a number of modifications that have been made to the basic procedure are compared. A number of applications of the cuckoo search are described and some possible future developments of the cuckoo search algorithm are summarized.

**Swarm Intelligence and Bio-Inspired Computation**

2. Analysis of Swarm Intelligence–Based Algorithms for Constrained Optimization

#### by **M.P. Saka,E. Doğan,Ibrahim Aydogdu**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128068884
- Language : En, Es, Fr & De

Swarm intelligence refers to collective intelligence. Biologists and natural scientist have been studying the behavior of social insects due to their efficiency of solving complex problems such as finding the shortest path between their nest and food source or organizing their nests. In spite of the fact that these insects are unsophisticated individually, they make wonders as a swarm by interaction with each other and their environment. In last two decades, the behaviors of various swarms that are used in finding preys or mating are simulated into a numerical optimization technique. In this chapter, eight different swarm intelligence–based algorithms are summarized and their working steps are listed. These techniques are ant colony optimizer, particle swarm optimizer, artificial bee colony algorithm, glowworm algorithm, firefly algorithm, cuckoo search algorithm, bat algorithm, and hunting search algorithm. Two optimization problems taken from the literature are solved by all these eight algorithms and their performance are compared. It is noticed that most of the swarm intelligence–based algorithms are simple and robust techniques that determine the optimum solution of optimization problems efficiently without requiring much of a mathematical struggling.

**Swarm Intelligence and Bio-Inspired Computation**

16. Artificial Plant Optimization Algorithm

#### by **Zhihua Cui,Xingjuan Cai**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128069023
- Language : En, Es, Fr & De

Artificial plant optimization algorithm (APOA) is a novel evolutionary strategy inspired by tree’s growing process. In this chapter, the methodologies of prototypal APOA and its updated version are illustrated. First, the primary framework is introduced by accounting for photosynthesis and phototropism phenomena. Since some important factors are ignored during mimicking branch’s growing, the optimization is sometimes misleading and time-consuming. Therefore, the standard version is developed by adding geotropism mechanism and apical dominance operator. The quality of the proposed technique is verified by two applications on artificial neural network training and toy model of protein folding. Simulation results are consistent with reported numerical data, indicating that the new optimization approach is valid and shows broad application in other fields.

**Swarm Intelligence and Bio-Inspired Computation**

8. Test Functions for Global Optimization: A Comprehensive Survey

#### by **Momin Jamil,Xin-She Yang,Hans-Jürgen Zepernick**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128068949
- Language : En, Es, Fr & De

Test functions are important to validate and compare the performance of various optimization algorithms. In previous years, there have been many test or benchmark functions reported in the literature. However, there is no standard list or set of benchmark functions with diverse properties that algorithms may be tested upon. On the other hand, any new optimization algorithm should be tested by a diverse range of test or benchmark functions so as to see if it can solve certain types of problems or not. For this purpose, we compile here 140 benchmark functions for unconstrained optimization problems.

**Swarm Intelligence and Bio-Inspired Computation**

9. Binary Bat Algorithm for Feature Selection

#### by **Rodrigo Yuji Mizobe Nakamura,Luís Augusto Martins Pereira,Douglas Rodrigues,Kelton Augusto Pontara Costa,João Paulo Papa,Xin-She Yang**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128068957
- Language : En, Es, Fr & De

Feature selection aims to find the most important information to save computational efforts and data storage. We formulated this task as a combinatorial optimization problem since the exponential growth of possible solutions makes an exhaustive search infeasible. In this work, we propose a new nature-inspired feature selection technique based on bats behavior, namely, binary bat algorithm The wrapper approach combines the power of exploration of the bats together with the speed of the optimum-path forest classifier to find a better data representation. Experiments in public datasets have shown that the proposed technique can indeed improve the effectiveness of the optimum-path forest and outperform some well-known swarm-based techniques.

**Swarm Intelligence and Bio-Inspired Computation**

7. A Survey of Swarm Algorithms Applied to Discrete Optimization Problems

#### by **Jonas Krause,Jelson Cordeiro,Rafael Stubs Parpinelli,Heitor Silvério Lopes**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128068930
- Language : En, Es, Fr & De

Most swarm intelligence algorithms were devised for continuous optimization problems. However, they have been adapted for discrete optimization as well with applications in different domains. This survey aims at providing an updated review of research of swarm intelligence algorithms for discrete optimization problems, comprising combinatorial or binary. The biological inspiration that motivated the creation of each swarm algorithm is introduced, and later, the discretization and encoding methods are used to adapt each algorithm for discrete problems. Methods are compared for different classes of problems and a critical analysis is provided, pointing to future trends.

**Swarm Intelligence and Bio-Inspired Computation**

19. Improvement of PSO Algorithm by Memory-Based Gradient Search—Application in Inventory Management

#### by **Tamás Varga,András Király,János Abonyi**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128069058
- Language : En, Es, Fr & De

Advanced inventory management in complex supply chains requires effective and robust nonlinear optimization due to the stochastic nature of supply and demand variations. Application of estimated gradients can boost up the convergence of Particle Swarm Optimization (PSO) algorithm but classical gradient calculation cannot be applied to stochastic and uncertain systems. In these situations Monte-Carlo (MC) simulation can be applied to determine the gradient. We developed a memory-based algorithm where instead of generating and evaluating new simulated samples the stored and shared former function evaluations of the particles are sampled to estimate the gradients by local weighted least squares regression. The performance of the resulted regional gradient-based PSO is verified by several benchmark problems and in a complex application example where optimal reorder points of a supply chain are determined.

**Swarm Intelligence and Bio-Inspired Computation**

10. Intelligent Music Composition

#### by **Maximos A. Kaliakatsos-Papakostas,Andreas Floros,Michael N. Vrahatis**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128068965
- Language : En, Es, Fr & De

Automatic music composition has blossomed with the introduction of intelligent methodologies in computer science. Thereby, many methodologies for automatic music composition have been or could be described as “intelligent,” but what exactly is it that makes them intelligent? Furthermore, is there any categorization of intelligent music composition (IMC) methodologies that is both consistent and descriptive? This chapter aims to provide some insights on what IMC methodologies are, through proposing and analyzing a detailed categorization of them. Toward this perspective, methodologies that incorporate bioinspired intelligent algorithms (such as cellular automata, L-systems, genetic algorithms, swarm intelligence, among others) as well as their combinations are considered and briefly reviewed. At the same time, a consistent categorization of these methodologies is proposed, taking into account the utilization of their intelligent algorithm in accordance to their overall compositional aims. To this end, three main categories can be defined: the “unsupervised,” the “supervised,” and the “interactive” IMC methodologies.

**Swarm Intelligence and Bio-Inspired Computation**

15. Structural Optimization Using Krill Herd Algorithm

#### by **Amir Hossein Gandomi,Amir Hossein Alavi,Siamak Talatahari**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128069015
- Language : En, Es, Fr & De

A new metaheuristic optimization algorithm, called krill herd (KH), has been recently proposed by Gandomi and Alavi. In this study, KH is introduced for structural optimization. For more verification, KH is subsequently applied to three design problems reported in the literature. The performance of the KH algorithm is further compared with various algorithms representative of the state of the art in the area. The comparisons show that the results obtained by KH can be better than the best solutions obtained by the existing methods in these three case studies.

**Swarm Intelligence and Bio-Inspired Computation**

17. Genetic Algorithm for the Dynamic Berth Allocation Problem in Real Time

#### by **Carlos Arango,Pablo Cortés,Alejandro Escudero,Luis Onieva**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128069031
- Language : En, Es, Fr & De

The container terminals (CTs) are designed to provide support to the continuous changes in the containerships. The most common schemes used for dock management are based on discrete and continuous locations. The consideration of continuous location in the CT allows arriving every container ship to the port independently of its size and dimensions. This work addresses the berth allocation problem with continuous dock, which is called dynamic berth allocation problem. We propose a mathematical model and develop a heuristic procedure based on a genetic algorithm to solve the corresponding mixed integer problem. Allocation planning aims to minimize the service time for each ship according to the berth and quay crane scheduling. Experimental analysis is carried out for the port of Algeciras that is the most important CT in Spain.

**Swarm Intelligence and Bio-Inspired Computation**

5. Modeling and Simulation of Ant Colony’s Labor Division: A Problem-Oriented Approach

#### by **Renbin Xiao**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128068914
- Language : En, Es, Fr & De

In view of labor division in swarm intelligence, a new research paradigm of “problem-oriented approach to swarm intelligence” is constructed. The key to the success of such an approach is to grasp the features of problem objects sufficiently. At first, the labor division behaviors of ant colonies are discoursed and some descriptions of ant colony’s labor division models are given. Taking three practical problems as the backgrounds, the corresponding modeling and simulation approaches to ant colony’s labor division are investigated. Considering the diverse nature of virtual enterprise tasks, ant colony’s labor division model with multitask is proposed. Similarly, ant colony’s labor division model with multistate is also proposed by considering the diverse characteristics of product varieties in pull production systems. According to the relation of resource constraints of task allocation in resilient supply chains, ant colony’s labor division model with multiconstraint is put forward. Finally, the key points to implement “problem-oriented approach to swarm intelligence” are refined and expounded.

**Swarm Intelligence and Bio-Inspired Computation**

13. Discrete Firefly Algorithm for Traveling Salesman Problem: A New Movement Scheme

#### by **Gilang Kusuma Jati,Ruli Manurung,null Suyanto**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 012806899X
- Language : En, Es, Fr & De

The “firefly algorithm” (FA) is a nature-inspired technique originally designed for solving continuous optimization problems. There are several existing approaches that apply FA also as a basis for solving discrete optimization problems, in particular the “traveling salesman problem” (TSP). In this chapter, we present a new movement scheme called edge-based movement, an operation which guarantees that a candidate solution more closely resembles another one. This leads to a more FA-like behavior of the algorithm. We investigate the performance of the ‘evolutionary discrete firefly algorithm” when using this new edge-based movement and compare it against previous methods. Computer simulations show that the new movement scheme produces slightly better accuracy with much faster average time. The average speedup factor is 14.06 times.

**Swarm Intelligence and Bio-Inspired Computation**

4. Memetic Self-Adaptive Firefly Algorithm

#### by **Iztok Fister,Xin-She Yang,Janez Brest,Iztok Jr. Fister**

- Publisher : Elsevier Inc. Chapters
- Release : 2013-05-16
- Pages : 450
- ISBN : 0128068906
- Language : En, Es, Fr & De

The “firefly algorithm” (FFA) is a modern metaheuristic algorithm, inspired by the behavior of fireflies. This algorithm and its variants have been successfully applied to many continuous optimization problems. This work analyzes the performance of the FFA when solving combinatorial optimization problems. In order to improve the results, the original FFA is extended and improved for self-adaptation of control parameters, and thus more directly balancing between exploration and exploitation in the search process of fireflies. We use a new population model to increase the selection pressure, and the next generation selects only the fittest between a parent and an offspring population. As a result, the proposed memetic self-adaptive FFA (MSA-FFA) is compared with other well-known graph coloring algorithms such as Tabucol, the hybrid evolutionary algorithm, and an evolutionary algorithm with stepwise adaptation of weights. Various experiments have been conducted on a huge set of randomly generated graphs. The results of these experiments show that the results of the MSA-FFA are comparable with other tested algorithms.