Large-scale multiobjective optimization with adaptive competitive swarm optimizer and inverse modeling

Competitive swarm optimizer (CSO) is an efficient algorithm for solving larger-scale mul-tiobjective optimization problems (LSMOPs). However, the winners are not updated in original algorithm, and the random parameters model affects the convergence speed. To improve the performance of CSO for solving LSMOPs, an adaptive competitive swarm opti-mizer with inverse modeling is developed. In the method, an adaptive parameter model is designed to accelerate the convergence speed when the population not traps in local con-vergence, and the random parameter model is used to help the losers jump out of local con-vergence when the population traps in stagnation. Moreover, inverse modeling is used to update the winners, and the number of them is maintained by a new designed environ-ment selection method. The exploitation ability of algorithm is improved and the drawback that convergence speed of the algorithm might be slowed by few winners in the early stage of evolution is avoided. In addition, an adjacent individual competition method is proposed to improve the distribution of solutions. Finally, the proposed algorithm is tested on nine benchmark optimization problems, and the results indicate that the average ranks in terms of IGD and HV of the proposed algorithm outperform those of seven compared algorithms. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

The competitive swarm optimizer (CSO) is an efficient algorithm for solving larger-scale multiobjective optimization problems (LSMOPs). In this study, an adaptive competitive swarm optimizer with inverse modeling is proposed to improve the performance of CSO. The winners are updated using inverse modeling and an adjacent individual competition method is introduced to enhance the distribution of solutions. Experimental results demonstrate that the proposed algorithm outperforms other compared algorithms on benchmark optimization problems.