A random forest assisted evolutionary algorithm using competitive neighborhood search for expensive constrained combinatorial optimization

Abstract

Many real-world combinatorial optimization problems have both expensive objective and constraint functions. Although surrogate models for the discrete decision variables can be trained to replace the expensive fitness evaluations in evolutionary algorithms, the approximation errors of the surrogate models for the constraint function easily misguide the search. The classic genetic algorithm, which is often applied straightforwardly to the combinatorial optimization problems, gradually exposes its inefficiency in the search process. Therefore, we proposed a random forest assisted evolutionary algorithm using a new competitive neighborhood search, where random forest is used as the surrogate models to approximate both the objective and constraint functions and the competitive neighborhood search is to improve the search efficiency. Moreover, competitive neighborhood search shows a natural adaptability to the surrogate model, which helps to reduce the impact of approximation errors. The proposed algorithm is tested on 01 knapsack problems and quadratic knapsack problems with various dimensions and constraints. The experimental results demonstrate that the proposed algorithm is able to solve the expensive constrained combinatorial optimization problems efficiently.