An Agent-based Memetic Algorithm (AMA) for nonlinear optimization with equality constraints

Over the last two decades several methods have been proposed for handling functional constraints while solving nonlinear optimization problems using Evolutionary Algorithms (EA). However EAs have inherent difficulty in dealing with equality constraints. This paper presents an Agent-based Memetic Algorithm (AMA) for solving nonlinear optimization problems with equality constraints. A new learning process for agents is introduced specifically for handling the equality constraints in the evolutionary process. The basic concept is to reach a point on the equality constraint from its current position by the selected individual agents. The proposed algorithm is tested on a set of standard benchmark problems. The preliminary results show that the proposed technique works very well on those benchmark problems.