Decision Science in Action

Theory and Applications of Modern Decision Analytic Optimisation

  • Kusum Deep
  • Madhu Jain
  • Said Salhi

Part of the Asset Analytics book series (ASAN)

Table of contents

  1. Front Matter
    Pages i-vii
  2. Jesús-Adolfo Mejía-de-Dios, Efrén Mezura-Montes
    Pages 65-74
  3. Liping Xie, Jianchao Zeng, Qiongqiong Yang, Richard A. Formato
    Pages 75-104
  4. Shipra Singh, Anuradha Aggarwal, Harendera Kumar, Pradeep Kumar Yadav
    Pages 119-126
  5. Tamal Dutta, Deepjyoti Santra, Chee Peng-Lim, Jaya Sil, Paramita Chottopadhyay
    Pages 127-137
  6. Puneet Kumar Pal, Kusum Deep, Atulya K. Nagar
    Pages 139-154
  7. Kamal Kumar Mittal, Pramod Kumar Jain, Dinesh Kumar
    Pages 193-202
  8. Sandip Joardar, Arnab Sanyal, Dwaipayan Sen, Diparnab Sen, Amitava Chatterjee
    Pages 217-226
  9. Dipti Singh, Shilpa Pal, Abhishek Singh
    Pages 227-236
  10. Ruchi Panwar, N. Sukavanam
    Pages 237-250
  11. Shuai Zhang, Weiheng Zhang, Yuvraj Gajpal, S. S. Appadoo
    Pages 251-260
  12. Anusuya Ghosh, Vishnu Narayanan
    Pages 261-276

About this book


This book provides essential insights into a range of newly developed numerical optimization techniques with a view to solving real-world problems. Many of these problems can be modeled as nonlinear optimization problems, but due to their complex nature, it is not always possible to solve them using conventional optimization theory. Accordingly, the book discusses the design and applications of non-conventional numerical optimization techniques, including the design of benchmark functions and the implementation of these techniques to solve real-world optimization problems. 

The book’s twenty chapters examine various interesting research topics in this area, including: Pi fraction-based optimization of the Pantoja–Bretones–Martin (PBM) antenna benchmarks; benchmark function generators for single-objective robust optimization algorithms; convergence of gravitational search algorithms on linear and quadratic functions; and an algorithm for the multi-variant evolutionary synthesis of nonlinear models with real-valued chromosomes.

Delivering on its promise to explore real-world scenarios, the book also addresses the seismic analysis of a multi-story building with optimized damper properties; the application of constrained spider monkey optimization to solve portfolio optimization problems; the effect of upper body motion on a bipedal robot’s stability; an ant colony algorithm for routing alternate-fuel vehicles in multi-depot vehicle routing problems; enhanced fractal dimension-based feature extraction for thermal face recognition; and an artificial bee colony-based hyper-heuristic for the single machine order acceptance and scheduling problem.

The book will benefit not only researchers, but also organizations active in such varied fields as Aerospace, Automotive, Biotechnology, Consumer Packaged Goods, Electronics, Finance, Business & Banking, Oil, Gas & Geosciences, and Pharma, to name a few.


Optimization Science Π Fraction-based Optimization Artificial Bee Colony Based Hyper-heuristic Artificial Physics Optimization Benchmark Function Generators Evolutionary Optimization Feature Analysis Feature Extraction Gravitational Search Algorithm Multistoried Building Large-scale Optimization Problems Long Wave Equations Multi-variant Evolutionary Synthesis Optimal Configuration Selection Optimality Conditions Robust Optimisation Algorithms Seismic Analysis Sine-Cosine Algorithm Spider Monkey Optimization Task Scheduling Algorithm

Editors and affiliations

  • Kusum Deep
    • 1
  • Madhu Jain
    • 2
  • Said Salhi
    • 3
  1. 1.Department of MathematicsIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.Department of MathematicsIndian Institute of Technology RoorkeeRoorkeeIndia
  3. 3.Kent Business School, Centre for Logistics and Heuristic Optimization (CLHO)University of KentCanterburyUK

Bibliographic information

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