Light beam search based multi-objective optimization using evolutionary algorithms

Abstract— For the past decade or so, evolutionary multiobjective optimization (EMO) methodologies have earned wide popularity for solving complex practical optimization problems, simply due to their ability to find a representative set of Pareto-optimal solutions for mostly two, three, and some extent to four and five-objective optimization problems. Recently, emphasis has been made in addressing the decision-making activities in arriving at a single preferred solution. The multiple criteria decision making (MCDM) literature offers a number of possibilities for such a task involving user preferences which can be supplied in different forms. This paper presents an interactive methodology for finding a preferred set of solutions, instead of the complete Pareto-optimal frontier, by incorporating preference information of the decision maker. Particularly, we borrow the concept of light beam search and combine it with the NSGA-II procedure. The working of this procedure has been demons...

• Type: Conference
• Year: 2007
• Where: CEC
• Authors: Kalyanmoy Deb, Abhay Kumar
• Keywords: Artificial Intelligence, CEC 2007, Optimization Problems, Pareto-optimal Solutions, Practical Optimization Problems