Zhenxing Zhang - New Optimization Algorithms and their Applications: Atom-Based, Ecosystem-Based and Economics-Based

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ISBN: 978-0-323-90941-9

This book was originally published in China with the ISBN 978-7-03-064198-4

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Weiguo Zhao

Liying Wang

Zhenxing Zhang

Optimization means to search for optimal solutions effectively and efficiently from a given solution space under the given constraints, by either maximizing or minimizing the objective function. Metaheuristic optimization algorithms, which are powerful tools for addressing these challenging optimization problems, are becoming increasingly popular. For this book, three novel intelligent optimization techniques, based on atomic dynamics, artificial ecosystems, and economics were developed, tested, and applied to practical engineering issues. The book is organized as follows:

• introduces the development of intelligent optimization algorithms and their advantages and disadvantages.
• introduces the process and basic principles of the atom search optimization algorithm and tests the algorithm.
• applies the atom search optimization algorithm in engineering issues.
• introduces the proposed process and basic principle of the artificial ecosystem-based optimization algorithm and tests the algorithm.
• employs the artificial ecosystem-based optimization algorithm in engineering applications.
• presents the inspiration and description of the supply-demand-based optimization.
• demonstrates the applications of the supply-demand-based optimization in different engineering cases.
• lists mathematical benchmark functions used to test the performance of various algorithms.
• lists optimization formulations of various engineering problems.
• provides the MATLAB codes of three algorithms.

We have been working on intelligent optimization algorithms for many years and have made some achievements. The book is compiled on the basis of the results of these achievements.

We appreciate the support of the administration and colleagues of the School of Water Resources and Hydropower, Hebei University of Engineering, China, as well as professors from other universities. We would like to express our respect and gratitude for the references in the book. We also acknowledge the publishers who permitted our papers to be adopted and utilized in the preparation of this book.

This book can be used by technicians who are engaged in computing and intelligent algorithms and various engineering practices that use optimization. It is also a good reference for postgraduates and teachers of relevant specialties in colleges and universities.

Our warmest gratitude is due to our families for their continued support in the course of preparing this book. Dr. Zhao worked on the ideas and papers, which motivated the preparation of this book, in Illinois State Water Survey, University of Illinois at Urbana-Champaign, USA. Preparing the book brought back all the beautiful memories in Champaign, IL. Owing to the limitation of our knowledge, we are not able to eradicate incompleteness or errors in the book. Your suggestions would be much appreciated. Please contact us with your suggestions the publisher.

Acknowledgment is given to Springer for permission to reprint material from the following paper:

• Zhao W, Wang L, Zhang Z. 2020. Artificial ecosystem-based optimization: A novel nature-inspired meta-heuristic algorithm, Neural Computing and Applications 32:93839425.

Acknowledgment is given to the Institute of Electrical and Electronic Engineers (IEEE) for permission to reprint a portion of the material from the following paper:

• Zhao W, Wang L, Zhang Z. 2019. Supply-demand-based optimization: A novel economics-inspired algorithm for global optimization, IEEE Access 7:7318273206. [2019] IEEE.

Acknowledgment is also given to Elsevier for permission to reprint material from the following papers:

• Zhao W, Wang L. 2016. An effective bacterial foraging optimizer for global optimization, Information Sciences 329:719735.
• Zhao W, Wang L, Zhang Z. 2019. Atom search optimization and its application to solve a hydrogeologic parameter estimation problem, Knowledge-Based Systems 163:283304.

The authors are grateful that the copyright permission was granted from the above publishers.