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.
Table des matières
Chapter 1: π Fraction-based optimization of the pbm antenna benchmarks.- Chapter 2: Benchmark function generators for single-objective robust optimisation algorithms.- Chapter 3: Convergence of gravitational search algorithm on linear and quadratic functions.- Chapter 4: An algorithm of multi-variant evolutionary synthesis of nonlinear models with real-valued chromosomes.- Chapter 5: An artificial bee colony based hyper-heuristic for the single machine order acceptance and scheduling problem.- Chapter 6: A new evolutionary optimization method based on center of mass.- Chapter 7: Adaptive artificial physics optimization algorithm with pd controller.- Chapter 8: Nsga-ii based decision-making in fuzzy multi-objective optimization of system reliability.- Chapter 9: Ga based task scheduling algorithm for efficient utilization of available resources in computational grid.- Chapter 10: Statistical feature analysis of thermal images from electrical equipment.- Chapter 11: Performance of sine-cosine algorithm on large-scale optimization problems.- Chapter 12: Necessary and sufficient optimality conditions for fractional interval valued optimization problems.- Chapter 13: Application of constrained spider monkey optimization to solve portfolio optimization problem.- Chapter 14: Optimal configuration selection in reconfigurable manufacturing system.- Chapter 15: A comparative study of regularized long wave equations using collocation method with cubic b-spline.- Chapter 16: An enhanced fractal dimension based feature extraction for thermal face recognition.- Chapter 17: Seismic analysis of multistoried building with optimized damper properties.- Chapter 18: Effect of upper body motion on biped robot stability.- Chapter 19: Ant colony algorithm for routing alternate fuel vehicles in multi-depot vehicle routing problem.- Chapter 20: Semidefinite approximation of closed convex set
A propos de l’auteur
Dr. Kusum Deep is a Professor at the Department of Mathematics, Indian Institute of Technology Roorkee. Her research interests include numerical optimization, nature inspired optimization, computational intelligence, genetic algorithms, parallel genetic algorithms, and parallel particle swarm optimization.
Dr. Madhu Jain is an Associate Professor at the Department of Mathematics, Indian Institute of Technology Roorkee. Her research interests include computer communications networks, performance prediction of wireless systems, mathematical modeling, and biomathematics.
Dr. Said Salhi is Director of the Centre for Logistics & Heuristic Optimization (CLHO) at Kent Business School, University of Kent, UK. Prior to his appointment at Kent in 2005, Said served at the University of Birmingham’s School of Mathematics for 15 years, where in the latter years he acted as Head of the Management Mathematics Group. He obtained his BSc in Mathematics at Algiers’s University, and his MSc and Ph D in OR at Southampton (Institute of Mathematics) and Lancaster (School of Management), respectively. Dr. Said has edited 6 special journal issues, chaired the European Working Group in Location Analysis in 1996 and recently the International Symposium on Combinatorial Optimisation (CO2016) in Kent, 1–3 September 2016. He has published over 100 papers in academic journals.
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