Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms

Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms

Sujata Dash, B.K. Tripathy, Atta ur Rahman

Copyright: © 2018 |Pages: 538

ISBN13: 9781522528579|ISBN10: 1522528571|EISBN13: 9781522528586

DOI: 10.4018/978-1-5225-2857-9

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MLA

Dash, Sujata, et al., editors. Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms. IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2857-9

APA

Dash, S., Tripathy, B., & Rahman, A. U. (Eds.). (2018). Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms. IGI Global. https://doi.org/10.4018/978-1-5225-2857-9

Chicago

Dash, Sujata, B.K. Tripathy, and Atta ur Rahman, eds. Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2857-9

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The digital age is ripe with emerging advances and applications in technological innovations. Mimicking the structure of complex systems in nature can provide new ideas on how to organize mechanical and personal systems.

The Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms is an essential scholarly resource on current algorithms that have been inspired by the natural world. Featuring coverage on diverse topics such as cellular automata, simulated annealing, genetic programming, and differential evolution, this reference publication is ideal for scientists, biological engineers, academics, students, and researchers that are interested in discovering what models from nature influence the current technology-centric world.

Table of Contents

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Front Materials

Title Page

Copyright Page

Advances in Computational Intelligence and Robotics (ACIR) Book Series

Editorial Advisory Board

Preface

Acknowledgment

Chapters

Chapter 1

Metaheuristic-Based Hybrid Feature Selection Models (pages 1-22)

Sujata Dash

This chapter focuses on key applications of metaheuristic techniques in the field of gene selection and classification of microarray data. The metaheuristic techniques are efficient in handling combinatorial optimization problems. In...

Chapter 2

Swarm-Based Nature-Inspired Metaheuristics for Neural Network Optimization (pages 23-53)

Swathi Jamjala Narayanan, Boominathan Perumal, Jayant G. Rohra

Nature-inspired algorithms have been productively applied to train neural network architectures. There exist other mechanisms like gradient descent, second order methods, Levenberg-Marquardt methods etc. to optimize the parameters of...

Chapter 3

A Novel Hybrid Model Using RBF and PSO for Net Asset Value Prediction (pages 54-72)

C. M. Anish, Babita Majhi, Ritanjali Majhi

Net asset value (NAV) prediction is an important area of research as small investors are doing investment in there, Literature survey reveals that very little work has been done in this field. The reported literature mainly used...

Chapter 4

Memetic Algorithms and Their Applications in Computer Science (pages 73-93)

B. K. Tripathy, Sooraj T. R., R. K. Mohanty

The term “memetic algorithm” was introduced by Moscato is an extension of the traditional genetic algorithm. It uses a local search technique to reduce the likelihood of the premature convergence. Memetic algorithms are intrinsically...

Chapter 5

A New Data Hiding Scheme Combining Genetic Algorithm and Artificial Neural Network (pages 94-103)

Ayan Chatterjee, Nikhilesh Barik

Today, in the time of internet based communication, steganography is an important approach. In this approach, secret information is embedded in a cover medium with minimum distortion of it. Here, a video steganography scheme is...

Chapter 6

A Statistical Scrutiny of Three Prominent Machine-Learning Techniques to Forecast Machining Performance Parameters of Inconel 690 (pages 104-120)

Binayak Sen, Uttam Kumar Mandal, Sankar Prasad Mondal

Computational approaches like “Black box” predictive modeling approaches are extensively used technique applied in machine learning operations of today. Considering the latest trends, present study compares capabilities of two...

Chapter 7

Insights Into Simulated Annealing (pages 121-139)

Khalil Amine

Simulated annealing is a probabilistic local search method for global combinatorial optimisation problems allowing gradual convergence to a near-optimal solution. It consists of a sequence of moves from a current solution to a better...

Chapter 8

Automatic Test Data Generation Using Bio-Inspired Algorithms: A Travelogue (pages 140-159)

Madhumita Panda, Sujata Dash

This chapter presents an overview of some widely accepted bio-inspired metaheuristic algorithms which would be helpful in solving the problems of software testing. Testing is an integral part of the software development process. A...

Chapter 9

Development of an Efficient Prediction Model Based on a Nature-Inspired Technique for New Products: A Case of Industries From the Manufacturing Sector (pages 160-182)

Vikas Bhatnagar, Ritanjali Majhi, S. L. Tulasi Devi

A lot of studies have been made on new product development process to make it an ideal procedure and many researchers have contributed significantly to achieve this by studying various factors associated with it. In this study, an...

Chapter 10

Applications of Hybrid Intelligent Systems in Adaptive Communication (pages 183-217)

Atta ur Rahman

Dynamic allocation of the resources for optimum utilization and throughput maximization is one of the most important fields of research nowadays. In this process the available resources are allocated in such a way that they are...

Chapter 11

DE-Based RBFNs for Classification With Special Attention to Noise Removal and Irrelevant Features (pages 218-243)

Ch. Sanjeev Kumar Dash, Ajit Kumar Behera, Sarat Chandra Nayak

This chapter presents a novel approach for classification of dataset by suitably tuning the parameters of radial basis function networks with an additional cost of feature selection. Inputting optimal and relevant set of features to...

Chapter 12

Competency Mapping in Academic Environment: A Swarm Intelligence Approach (pages 244-263)

Sushri Samita Rout, Bijan Bihari Misra

This chapter will discuss and present a new model to perform a competency map in an educational institute which has moderate number of faculty members on whom the map has to be performed. Performing such a map is a tough but...

Chapter 13

An Overview of the Last Advances and Applications of Greedy Randomized Adaptive Search Procedure (pages 264-284)

Airam Expósito Márquez, Christopher Expósito-Izquierdo

One of the most studied methods to get approximate solutions in optimization problems are the heuristics methods. Heuristics are usually employed to find good, but not necessarily optima solutions. The primary purpose of the chapter...

Chapter 14

Defect Detection of Fabrics by Grey-Level Co-Occurrence Matrix and Artificial Neural Network (pages 285-297)

Dilip k. Choudhury, Sujata Dash

The class of Textiles produced from terephthalic acid and ethylene glycol by condensation polymerization has many end-uses for example these are used as filter fabric in railway track to prevent soil erosion, in cement industry these...

Chapter 15

A Holistic-Based Multi-Criterion Decision-Making Approach for Solving Engineering Sciences Problem Under Imprecise Environment (pages 298-330)

Syed Abou Iltaf Hussain, Sankar Prasad Mondal, Uttam Kumar Mandal

Multi-Criteria Decision Making has evolved as an important tool for taking some of the most important decisions in the today's hi-tech engineering world. But due to some reasons like measurement difficulty, lack of data, faulty...

Chapter 16

A Comprehensive Review of Nature-Inspired Algorithms for Feature Selection (pages 331-345)

Kauser Ahmed P, Senthil Kumar N

Due to advancement in technology, a huge volume of data is generated. Extracting knowledgeable data from this voluminous information is a difficult task. Therefore, machine learning techniques like classification, clustering...

Chapter 17

Nature-Inspired-Algorithms-Based Cellular Location Management: Scope and Applications (pages 346-362)

Swati Swayamsiddha, Chetna Singhal, Rajarshi Roy

Nature-Inspired algorithms have gained relevance particularly for solving complex optimization problems in engineering domain. An overview of implementation modeling of the established algorithms to newly developed algorithms is...

Chapter 18

Fuzziness in Ant Colony Optimization and Their Applications (pages 363-376)

Deepthi P. Hudedagaddi, B. K. Tripathy

Nature-inspired algorithms are still at a very early stage with a relatively short history, comparing with many traditional, well-established methods. Metaheuristics, in their original definition, are solution methods that...

Chapter 19

Application of Natured-Inspired Technique to Odia Handwritten Numeral Recognition (pages 377-399)

Puspalata Pujari, Babita Majhi

In this chapter an effort has been made to develop a hybrid system using functional link artificial neural network (FLANN) and differential evolution (DE) for effective recognition of Odia handwritten numerals. The S-transform (ST)...

Chapter 20

Intelligent Technique to Identify Epilepsy Using Fuzzy Firefly System for Brain Signal Processing (pages 400-412)

Sasikumar Gurumoorthy, B. K. Tripathy

In the new direction of understand the signal that is created from the brain organization is one of the main chores in the brain signal processing. Amid all the neurological disorders the human brain epilepsy is measured as one of...

Chapter 21

Analysis and Implementation of Artificial Bee Colony Optimization in Constrained Optimization Problems (pages 413-432)

Soumya Sahoo, Sushruta Mishra, Brojo Kishore Kishore Mishra, Monalisa Mishra

The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples that nature has...

Chapter 22

Escalation of Prediction Accuracy With Virtual Data: A Case Study on Financial Time Series (pages 433-461)

Sarat Chandra Nayak, Bijan Bihari Misra, Himansu Sekhar Behera

Random fluctuations occur in the trend of financial time series due to many macroeconomic factors. Such fluctuations lead to sudden fall after a constant raise or a sudden rise after a constant fall, which are difficult to predict...

Chapter 23

Determination of Spatial Variability of Rock Depth of Chennai (pages 462-479)

Pijush Samui, Viswanathan R., Jagan J., Pradeep U. Kurup

This study adopts four modeling techniques Ordinary Kriging(OK), Generalized Regression Neural Network (GRNN), Genetic Programming(GP) and Minimax Probability Machine Regression(MPMR) for prediction of rock depth(d) at...

Back Materials

Compilation of References

About the Contributors

Index

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