DIFFERENT APPROACHES FOR SOLVING OPTIMIZATION PROBLEMS USING INTERACTIVE E-LEARNING TOOLS

Abstract: Solving optimization problems, regardless of the scope, involves knowledge of mathematical apparatus based on the techniques and methods that are not always simple (differential calculus, operational research, etc.) and concepts of artificial intelligence, machine learning, evolutionary computing, graph theory. These problems are NP-complete and very often the optimization process targets more than one objective, at least two, and they can have an antagonist behavior. As an example, we can consider a simple car design: two objectives- cost (production cost or fuel consumption) that should be minimized and performance (speed limit or reliability) which are to be maximized. Or, if we talk about a microprocessor design the multi-criteria analysis must targets: high performance, small integration area, small energy consumption having also constraints about thermal dissipation. Given the above, becomes more difficult to teach optimization methods, to communicate new concepts and skills in an informative and formative manner, and in the same time to be attractive for students. Thus, developing effective e-learning tools targeting evolutionary algorithms in order to solve optimization problems is a continuous challenge. Moreover, technology applied to education became a key issue in nowadays knowledge society and education represents an essential element for knowledge improvement and economy growth. In this work we try to tackle the above challenges by developing the ETTOP too