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E-raamat: Handbook of Nature-Inspired Optimization Algorithms: The State of the Art: Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems

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Handbook of Nature-Inspired Optimization Algorithms: The State of the Art: Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization ProblemsSuurem pilt
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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.
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.
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