Evolutionary Algorithm Based on Surrogate and Inverse Surrogate Models for Expensive Multiobjective Optimization

When dealing with expensive multiobjective optimization problems, majority of existing surrogate-assisted evolutionary algorithms (SAEAs) generate solutions in decision space and screen candidate solutions mostly by using designed surrogate models. The generated solutions exhibit excessive randomness, which tends to reduce the likelihood of generating good-quality solutions and cause a long evolution to the optima. To improve SAEAs greatly, this work proposes an evolutionary algorithm based on surrogate and inverse surrogate models by 1) Employing a surrogate model in lieu of expensive (true) function evaluations; and 2) Proposing and using an inverse surrogate model to generate new solutions. By using the same training data but with its inputs and outputs being reversed, the latter is simple to train. It is then used to generate new vectors in objective space, which are mapped into decision space to obtain their corresponding solutions. Using a particular example, this work shows its advantages over existing SAEAs. The results of comparing it with state-of-the-art algorithms on expensive optimization problems show that it is highly competitive in both solution performance and efficiency.