An evolutionary algorithm recommendation method with a case study in flow shop scheduling

Numerous optimization problems exist in the product and process design, engineering, and planning, as well as production management, and the evolutionary algorithms (EAs) have been proved to be effective optimization methods to solve these problems. However, how to choose the appropriate EA is one of the key issues due to the variety of EAs and lack of experience. Under this circumstance, firstly, a novel EA recommendation system framework is designed and proposed, and the implementation of the EA recommendation method is also described in this paper. Then, computational experiments are implemented to demonstrate the effectiveness and accuracy of the proposed method on the basis of 14 typical benchmark problems and 20 classical EAs. Finally, a case study regarding permutation flow shop scheduling is also carried out to test its effectiveness and accuracy. The proposed recommendation method provides a new way to select the appropriate EAs scientifically and reasonably to solve a given optimization problem.