Learning to Optimize -- Optimal seating assignment in the COVID-19 era via Quantum Computing -- Hybrid Ant Colony Optimization Algorithms - Behaviour Investigation Based on Intuitionistic Fuzzy Logic -- Scheduling algorithms for single machine problem with release and delivery times -- Key Performance Indicators to Improve e-Mail Service Quality through ITIL Framework -- Contemporary Bioprocesses Control Algorithms for Educational Purposes -- Monitoring a Fleet of Autonomous Vehicles through A* like Algorithms and Reinforcement Learning -- Rather "good in, good out" than "garbage in, garbage out": A comparison of various discrete subsampling algorithms using COVID-19 data without a response variable.

This book presents recent advances in computational optimization. The book includes important real problems like modeling of physical processes, parameter settings for controlling different processes, transportation problems, machine scheduling, air pollution modeling, solving multiple integrals and systems of differential and integral equations which describe real processes, solving engineering and financial problems. It shows how to develop algorithms for them based on new intelligent methods like evolutionary computations, ant colony optimization, constrain programming Monte Carlo method and others. This research demonstrates how some real-world problems arising in engineering, economics and other domains can be formulated as optimization problems.