An Agent Based Evolutionary Approach for Nonlinear Optimization with Equality Constraints

Abstract

To represent practical problems appropriately, many mathematical optimization models require equality constraints in addition to inequality constraints. The existence of equality constraints reduces the size of the feasible space, which makes it difficult to locate feasible and optimal solutions. This paper shows the enhanced performance of an agent-based evolutionary algorithm in solving Constrained Optimization Problems (COPs) with equality constraints. In the early generations of the evolutionary process, the agents use a new learning process that is specifically designed for handling equality constraints. In the later generations, the agents improve their performance through other learning processes by exploiting their own potential. The performance of the proposed algorithm is tested on a set of well-known benchmark problems including two new problems. The experimental results confirm the improved performance of the proposed algorithm.