Ali Mohamed & Diego Oliva 
Handbook of Nature-Inspired Optimization Algorithms: The State of the Art [PDF ebook] 
Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems

The introduction of nature-inspired optimization algorithms (NIOAs), over the past three decades, helped solve nonlinear, high-dimensional, and complex computational optimization problems. NIOAs have been originally developed to overcome the challenges of global optimization problems such as nonlinearity, non-convexity, non-continuity, non-differentiability, and/or multimodality which traditional numerical optimization techniques had difficulties solving.

The main objective for this book is to make available a self-contained collection of modern research addressing the general bound-constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects.

Table of Content

Chaotic-SCA Salp Swarm Algorithm Enhanced with Opposition Based Learning:  Application to Decrease Carbon Footprint in Patient Flow.- Design and Performance Evaluation of Objective Functions Based on Various Measures of Fuzzy Entropies for Image Segmentation using Grey Wolf Optimization.- Improved Artificial Bee Colony Algorithm with Adaptive Pursuit Based Strategy Selection.- Beetle Antennae Search Algorithm for the Motion Planning of Industrial Manipulator.- Solving Optimal Power Flow with Considering Placement of TCSC and FACTS Cost Using Cuckoo Search Algorithm.