CGDE3: An Efficient Center-based Algorithm for Solving Large-scale Multi-objective Optimization Problems

For several years, the Differential Evolution (DE) algorithm has been an effective method for solving complex real-world optimization problems. Due to its success and popularity, there are several multi-objective optimization algorithms proposed based on DE. However, when DE comes to solving large-scale problems its performance deteriorates. Several recent studies clearly confirm that utilizing center-based sampling method can increase the probability of the closeness of initialized population individuals to the solutions in black-box problems. In this paper, we propose center-based mutation for Third Generalized Differential Evolution (CGDE3) algorithm in order to solve large-scale multi-objective optimization problems; in fact, this time center-based sampling scheme is employed during the optimization process not just during the population initialization phase. For its mutation scheme, the CGDE3 algorithm utilizes five randomly selected candidate solutions from its current population to generate a new trial vector. The proposed method enhances the GDE3 algorithm by improving its exploration ability using extra center-based sampling during the evolution process. This algorithm is tested on benchmarks of CEC 2017 competition on evolutionary multi-objective optimization with dimensions of 100, 500 and 1000. Experimental results confirm that CGDE3 outperforms GDE3 over all three studied large-scale dimensions.