Mathematical Optimization and Control: Methods and Applications

Dear Colleagues,

Mathematical optimization is the discipline of adjusting a mathematical process so as to optimize (make the best use of) a specified set of parameters without violating certain constraints. The most common goals are minimizing cost and maximizing efficiency. Mathematical optimization uses optimization algorithms as the random research for the maximization or minimization of functions without violating certain constraints. It brings the necessity to research for optimization algorithms. Examples of these optimization algorithms are the genetic, bat, butterfly, grey wolf, particle swarm, ant colony, bee colony, and Bayesian algorithm. Additionally, the convergence of the mentioned optimization algorithms could be analyzed.

Mathematical control compares the value of a variable being controlled with the desired value, and applies the control signal to bring the variable to a desired value. The most common goals are regulation, trajectory tracking, stabilization, synchronization, nonlineariy compensation, obstacle avoidance, or disturbance rejection. It brings the necessity to research for control algorithms. Examples of these control algorithms are the adaptive, neural network, fuzzy, backstepping, sliding mode, robust, feedback, observer-based algorithms. Additionally, the stability of the mentioned control algorithms could be analyzed.

The objective of this Special Issue of Mathematics is to cover the optimization and control algorithms.

Original contributions are solicited from, but are not limited, the following topics of interest:

• genetic optimization;
• bat optimization;
• butterfly optimization;
• grey wolf optimization;
• particle swarm optimization;
• ant colony optimization;
• bee colony optimization;
• bayesian optimization;
• adaptive control;
• neural network control;
• fuzzy control;
• backstepping control;
• feedback control;
• sliding mode control; 
• robust control;
• observer-based control;
• other alternative optimization or control.

Prof. Dr. Jose de Jesus Rubio
Prof. Dr. Jeff Pieper
Prof. Dr. Jaime Pacheco Martinez
Prof. Dr. Mu-Yen Chen
Guest Editors

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• genetic optimization
• bat optimization
• butterfly optimization
• grey wolf optimization
• particle swarm optimization
• ant colony optimization
• bee colony optimization
• bayesian optimization
• adaptive control
• neural network control
• fuzzy control
• backstepping control
• feedback control
• sliding mode control
• robust control
• observer-based control
• other alternative optimization or control