Optimization, also known as mathematical programming, collection of mathematical principles and methods used for solving quantitative problems in many disciplines, including physics, biology, engineering, economics, and business. The subject grew from a realization that quantitative problems in manifestly different disciplines have important mathematical elements in common. Because of this commonality, many problems can be formulated and solved by using the unified set of ideas and methods that make up the field of optimization.

The historical term mathematical programming, broadly synonymous with optimization, was coined in the 1940s before programming became equated with computer programming. Mathematical programming includes the study of the mathematical structure of optimization problems, the invention of methods for solving these problems, the study of the mathematical properties of these methods, and the implementation of these methods on computers. Faster computers have greatly expanded the size and complexity of optimization problems that can be solved. The development of optimization techniques has paralleled advances not only in computer science but also in operations research, numerical analysis, game theory, mathematical economics, control theory, and combinatorics.

In random-based optimization algorithms, the methods using chaotic variables instead of random variables are called chaotic optimization algorithm. They have the features of easy implementation, short execution time and robust mechanisms of escaping from the local optimum. Therefore, they are promising tools for the engineering applications. Due to the non-repetition and ergodicity of chaos, it can carry out overall searches at higher speeds than stochastic searches that depend on probabilities.

In this webinar we will explore more about Chaotic Optimization Algorithms and its applications in various open ended problems.