Seminar by Rituparno Datta
An Evolutionary-Penalty Approach for Single and Multi-objective Constrained Optimization: Development and Applications
Rituparno Datta
Indian Institute of Technology Kanpur
Date: Wednesday, July 31st, 2013
Time: 5:00PM
Venue: CS101.Abstract:
The holy grail of constrained optimization is the development of an efficient, scale invariant and generic constraint handling procedure in single and multi-objective constrained optimization problems. Most optimization problems in science and engineering consist of one or many constraints, which come into picture mainly due to some physical limitations or functional requirements. Constraints can be divided into inequality type and equality type, but the challenge is to obtain feasible solutions that satisfy all constraints with minimal computational effort.
The classical penalty function approach is a widely used constraint handling method, in which the objective function value is penalized in proportion to the constraint violation. Initially Evolutionary Algorithms (EAs) were designed for unconstrained optimization but have now evolved to include various constraint handling mechanisms. We propose a synergistic combination of bi-objective evolutionary approach with the penalty function methodology, to solve problems with single objective having inequality constraints. This methodology is then extended for equality and combination of inequality and equality constrained problems. Normalization of constraints is crucial for the efficient performance of any constraint handling algorithm. Therefore, our method is extended to normalize all the constraints adaptively during the optimization process. Having developed efficient constraint handling strategies for single-objective optimization problems we then develop constraint handling strategy for bi-objective optimization problems. We demonstrate the efficacy of the proposed method by solving real world single and multi-objective constrained optimization problems.
About the speaker:
Rituparno Datta has just finished his Ph.D in the Department of Mechanical Engineering, Indian Institute of Technology, Kanpur. His current research work involves investigation of evolutionary algorithms based approaches to constrained optimization, application of multi-objective optimization in engineering design problems, memetic algorithms, derivative free optimization and robotics. He is a member of Association for Computing Machinery (ACM), IEEE and IEEE Computational Intelligence Society.