A generator for dynamically constrained optimization problems
Gary Pamparà, Andries Petrus Engelbrecht · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2019
Dynamic constrained optimization problems (DCOPs) provide larger complexity for an optimization algorithm by changing the problem landscape throughout the optimization process. Introducing constraints to an already changing dynamic environment increases the observed complexity of the problem space. Allowing such constraints to have irregular shapes which change along with the problem space itself provides an even greater level of complexity for an optimization algorithm. This paper proposes a function generator capable of creating dynamically constrained dynamic environments by extending the moving peaks benchmark (MPB) function generator. An analysis of the resulting environments produced by the generator is performed using fitness landscape analysis (FLA). A visual inspection of the resulting generated environments is also included.