With increasing crisis in several aspects of our everyday life, from basic human necessities such as food and healthcare to advanced requirements in terms of ensuring geo-political security, it has become necessary to optimally utilize the limited resources while simultaneously maximizing the benefits and minimizing the negative consequences of our everyday activities. For example, an investor would possibly want to maximize the profit while the regulations would require the impact on environment to be minimal. Such problems occur not only in research but in fact occur almost in every activity of our life. The primary objective of this course is to help the participants address such common real-life situations by providing a formal optimization knowledge so as to help them to maximize the impact of their work. The hands-on session will give them an exposure to state-of-the-art tools in optimization and enable them to quickly adopt the learnings of the course and employ it in their regular academic, research and administrative activities. The course will also enable them to critically analyze the performance of both single and multi-objective optimization techniques and help them to propose new techniques or harness the advantages of various techniques to develop hybrid techniques for specialized applications. The course is designed to break some of the common myths that the optimization problems need to be postulated in terms of linear or non-linear equalities and inequalities. It is aimed that the course will also enable the faculty participants to offer Bachelors and Masters Projects in Computational Intelligence Techniques for the students in their own institute or at least explore to formally incorporate optimization in their regular projects.

Numerical problems are ubiquitous in nature and are encountered both in everyday practice and research. Solution of these numerical problems using commonly available tools has become a necessity. In many instances, innovations do not become commercially viable in a shorter duration despite their promising performance as these innovations need to compete with state-of-the-art technologies in industries and applications that have been optimized possibly over several decades. Very often there are multiple conflicting objectives that are required to be optimized and an approach of one-size-fits-all can prove to be detrimental. In view of the conflicting nature, it is not possible to determine a single solution but one needs to determine the entire set of trade-off solutions. Such trade-offs in the hands of a user will help them to maximize the use of innovation as per their immediate needs. Thus it has almost become inevitable for an innovator to formally optimize the performance of an innovation thereby making optimization an important tool and its working knowledge very essential. This course will enable the participants to quickly employ various optimization techniques with the help of easy to use state-of-the-art optimization tools which can be readily incorporated in their current work. A strong emphasis on optimizing using exact mathematical models subject to its availability, and the use of black-box approaches in the absence of rigorous mathematical models will enable the participant to employ optimization under most circumstances. Computational Intelligence Techniques for optimization is one of the important areas of research and the course will enable the participants to not only extend currently available single objective optimization techniques to handle multiple conflicting objectives but will also enable them to propose novel optimization techniques.