Recent Developments and Engineering Applications in Multi-Objective Optimization using Evolutionary Algorithms - Rituparna Datta, University of South Alabama

Problems involving multiple conflicting objectives arise in most real world optimization problems. Evolutionary Algorithms (EAs) have gained a wide interest and success in solving problems of this nature for two main reasons: (1) EAs allow finding several members of the Pareto optimal set in a single run of the algorithm and (2) EAs are less susceptible to the shape of the Pareto front. Thus, Multi-objective EAs (MOEAs) have often been used to solve Multi-objective Problems (MOPs). This talk aims to summarize the efforts of various researcher’s algorithmic processes for MOEAs in an attempt to provide a review of the use and the evolution of the field. Hence, some basic concepts and a summary of the main MOEAs are provided. Furthermore, few interesting engineering applications will be discussed.

Bio:

Dr. Rituparna Datta is working as Computer Research Scientist in the University of South Alabama, USA. Prior to that, he was a Operations Research Scientist in Boeing Research & Technology (BR&T), Boeing, India. His current research work involves investigation of efficient algorithm for engineering optimization, evolutionary computation, machine learning, constraint handling, memetic algorithms, derivative-free optimization, knowledge extraction from data, manufacturing and robotics. His research has been published in 10 international SCI journals, few book chapters and 20+ international conferences with two edited books with Springer (one of them is the first book in the Infosys Science Foundation Series).