An investigation of the most common multi-objective optimization methods with propositions for improvement

In everyday life, many decisions are made, from personal and individual issues to major issues. In most decision-making topics, there are generally several goals and factors for discussion that are analyzed by multi-objective models. Researchers have conducted various studies on multi-objective problem-solving methods and have proposed several approaches to the principles of different theories. Most of these methods face a set of solutions that, based on the structure of the method, suggest one to the Decision Maker (DM), and decision analysis is necessary to arrive at an applicable solution. The process of interacting with the DM in most of these methods is both insufficient or very complex and time consuming. This paper investigates the most common multi-objective optimization methods and in each case proposes suggestions for improving the performance of the method and further interaction with the DM using weight restrictions and discrimination intensity functions; thus providing a more powerful tool for decision support. Each method and its improvement are illustrated with a numerical example.