A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization under Linear Constraints
This addresses optimization under linear constraints for domains like simulation optimization and finite element methods, but it appears incremental as an adaptation of an existing evolution strategy.
The paper tackled optimization problems with linear constraints by developing a Linear Constraint CMSA-ES algorithm, which uses a mutation operator and repair by projection to evolve on a linear manifold and achieve considerable results on test problems.
This paper addresses the development of a covariance matrix self-adaptation evolution strategy (CMSA-ES) for solving optimization problems with linear constraints. The proposed algorithm is referred to as Linear Constraint CMSA-ES (lcCMSA-ES). It uses a specially built mutation operator together with repair by projection to satisfy the constraints. The lcCMSA-ES evolves itself on a linear manifold defined by the constraints. The objective function is only evaluated at feasible search points (interior point method). This is a property often required in application domains such as simulation optimization and finite element methods. The algorithm is tested on a variety of different test problems revealing considerable results.