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Constraint Handling in Metaheuristics and Applications

Constraint Handling in Metaheuristics and Applications

Trending Research Book in Constraint Handling in Metaheuristics and Applications

Author(s) Name:  Anand J. Kulkarni, Efrén Mezura-Montes, Yong Wang, Amir H. Gandomi, Ganesh Krishnasamy

About the Book:

   This book aims to discuss the core and underlying principles and analysis of the different constraint handling approaches. The main emphasis of the book is on providing an enriched literature on mathematical modelling of the test as well as real-world problems with constraints, and further development of generalized constraint handling techniques. These techniques may be incorporated in suitable metaheuristics providing a solid optimized solution to the problems and applications being addressed. The book comprises original contributions with an aim to develop and discuss generalized constraint handling approaches/techniques for the metaheuristics and/or the applications being addressed. A variety of novel as well as modified and hybridized techniques have been discussed in the book.
   The conceptual as well as the mathematical level in all the chapters is well within the grasp of the scientists as well as the undergraduate and graduate students from the engineering and computer science streams. The reader is encouraged to have basic knowledge of probability and mathematical analysis and optimization.
   The book also provides critical review of the contemporary constraint handling approaches. The contributions of the book may further help to explore new avenues leading towards multidisciplinary research discussions. This book is a complete reference for engineers, scientists, and students studying/working in the optimization, artificial intelligence (AI), or computational intelligence arena.

Table of Contents

  • The Find-Fix-Finish-Exploit-Analyze (F3EA) Meta-Heuristic Algorithm with an Extended Constraint Handling Technique for Constrained Optimization
  • An Improved Cohort Intelligence with Panoptic Learning Behavior for Solving Constrained Problems
  • Nature-Inspired Metaheuristic Algorithms for Constraint Handling: Challenges, Issues, and Research Perspective
  • Experimental Comparison of Constraint Handling Schemes in Particle Swarm Optimization
  • Online Landscape Analysis for Guiding Constraint Handling in Particle Swarm Optimisation
  • On the use of Gradient-Based Repair Method for Solving Constrained Multiobjective Optimization Problems—A Comparative Study
  • MAP-Elites for Constrained Optimization
  • Optimization of Fresh Food Distribution Route Using Genetic Algorithm with the Best Selection Technique
  • Optimal Cutting Parameters Selection of Multi-Pass Face Milling Using Evolutionary Algorithms
  • Role of Constrained Optimization Technique in the Hybrid Cooling of High Heat Generating IC Chips Using PCM-Based Mini-channels
  • Maximizing Downlink Channel Capacity of NOMA System Using Power Allocation Based on Channel Coefficients Using Particle Swarm Optimization and Back Propagation Neural Network
  • Rank Reduction and Diagonalization of Sensing Matrix for Millimeter Wave Hybrid Precoding Using Particle Swarm Optimization
  • Comparative Analysis of Constraint Handling Techniques Based on Taguchi Design of Experiments
  • ISBN:  978-981-33-6710-4

    Publisher:  Springer, Singapore

    Year of Publication:  2021

    Book Link:  Home Page Url