Benchmarking the performance of genetic algorithms on constrained dynamic problems

The growing interest in dynamic optimisation has accelerated the development of genetic algorithms with specific mechanisms for these problems. To ensure that these developed mechanisms are capable of solving a wide range of practical problems it is important to have a diverse set of benchmarking functions to ensure the selection of the most appropriate Genetic Algorithm. However, the currently available benchmarking sets are limited to unconstrained problems with predominantly continuous characteristics. In this paper, the existing range of dynamic problems is extended with 15 novel constrained multi-objective functions. To determine how genetic algorithms perform on these constrained problems, and how this behaviour relates to unconstrained dynamic optimisation, 6 top-performing dynamic genetic algorithms are compared alongside 4 re-initialization strategies on the proposed test set, as well as the currently existing unconstrained cases. The results show that there are no differences between constrained/unconstrained optimisation, in contrast to the static problems. Therefore, dynamicity is the prevalent characteristic of these problems, which is shown to be more important than the discontinuous nature of the search and objective spaces. The best performing algorithm overall is MOEA/D, and VP is the best re-initialisation strategy. It is demonstrated that there is a need for more dynamic specific methodologies with high convergence, as it is more important to performance on dynamic problems than diversity.

Automated summary

The growing interest in dynamic optimization has led to the development of genetic algorithms with specific mechanisms. To ensure the effectiveness of these mechanisms, a diverse set of benchmarking functions is needed. However, the currently available benchmarking sets are limited to unconstrained problems. This paper extends the range of dynamic problems with 15 novel constrained multi-objective functions and compares the performance of different genetic algorithms on these problems.