Constrained many-objective evolutionary algorithm based on adaptive infeasible ratio

Liang，Zhengping1; Chen，Canran1; Wang，Xiyu2; Liu，Ling1; Zhu，Zexuan1,3

2023-09-01

10.1007/s12293-023-00393-0

Memetic Computing   Impact Factor & Quartile

1865-9284

1865-9292

Constrained many-objective optimization problems (CMaOPs) pose great challenges for evolutionary algorithms to reach an appropriate trade-off of solution feasibility, convergence, and diversity. To deal with this issue, this paper proposes a constrained many-objective evolutionary algorithm based on adaptive infeasible ratio (CMaOEA-AIR). In the evolution process, CMaOEA-AIR adaptively determines the ratio of infeasible solutions to survive into the next generation according to the number and the objective values of the infeasible solutions. The feasible solutions then undergo an exploitation-biased environmental selection based on indicator ranking and diversity maintaining, while the infeasible solutions undergo environmental selection based on adaptive selection criteria, aiming at the enhancement of exploration. In this way, both feasible and infeasible solutions are appropriately used to balance the exploration and exploitation of the search space. The proposed CMaOEA-AIR is compared with the other state-of-the-art constrained many-objective optimization algorithms on three types of CMaOPs of up to 15 objectives. The experimental results show that CMaOEA-AIR is competitive with the compared algorithms considering the overall performance in terms of solution feasibility, convergence, and diversity.

Adaptive infeasible ratio (AIR) Constraint handing technology (CHT) Environmental selection Many-objective evolutionary algorithm (MaOEA)

SCI

English

Corresponding

Innovative Research Group Project of the National Natural Science Foundation of China[61871272];

Computer Science ; Operations Research & Management Science

Computer Science, Artificial Intelligence ; Operations Research & Management Science

WOS:001044211000001

SPRINGER HEIDELBERG

2-s2.0-85167404924

Scopus

1.College of Computer Science and Software Engineering,Shenzhen University,Shenzhen,518060,China
2.Central R &D Institute,ZTE Corporation,Shenzhen,518057,China
3.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Southern University of Science and Technology,Shenzhen,518055,China

Liang，Zhengping,Chen，Canran,Wang，Xiyu,et al. Constrained many-objective evolutionary algorithm based on adaptive infeasible ratio[J]. Memetic Computing,2023,15(3):281-300.

Liang，Zhengping,Chen，Canran,Wang，Xiyu,Liu，Ling,&Zhu，Zexuan.(2023).Constrained many-objective evolutionary algorithm based on adaptive infeasible ratio.Memetic Computing,15(3),281-300.

Liang，Zhengping,et al."Constrained many-objective evolutionary algorithm based on adaptive infeasible ratio".Memetic Computing 15.3(2023):281-300.