Mapping constrained optimization problems to algorithms and constraint handling techniques

During the past few decades, many Evolutionary Algorithms together with the constraint handling techniques have been developed to solve the constrained optimization problems which have attracted a lot of research interest. But it´s still very difficult to decide when and how to use these algorithms and constraint handling techniques effectively. Some researchers have proposed some general frameworks like population-based algorithm portfolios (PAP), cooperative coevolving or ensemble strategies which use different subpopulations to run the algorithm parallel. These ideas don´t consider the problems´ characteristics in detail. Motivated by these observations, we propose a new method to construct the relationship between problems and algorithms as well as the constraint handling techniques standing the qualitative and quantitative point of view. This paper first summaries and extracts the problems´ characteristics systematically, then combines different qualitative and quantitative methods in the Evolutionary Algorithms and constraint handling techniques respectively so as to get a reasonable correspondence. The experimental results confirm this relationship, which is valuable to guide future research.