Disruption-Based Multiobjective Equilibrium Optimization Algorithm

Hao Chen; Weikun Li; Weicheng Cui

doi:10.1155/2020/8846250

Disruption-Based Multiobjective Equilibrium Optimization Algorithm

Comput Intell Neurosci. 2020 Nov 30:2020:8846250. doi: 10.1155/2020/8846250. eCollection 2020.

Authors

Hao Chen^{1

2

3}, Weikun Li^{2

3}, Weicheng Cui^{2

3}

Affiliations

¹ Zhejiang University-Westlake University Joint Training, Zhejiang University, Hangzhou 310024, China.
² Key Laboratory of Coastal Environment and Resources Research of Zhejiang Province, Institute of Advanced Technology, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China.
³ School of Engineering, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China.

Abstract

Nature-inspired computing has attracted huge attention since its origin, especially in the field of multiobjective optimization. This paper proposes a disruption-based multiobjective equilibrium optimization algorithm (DMOEOA). A novel mutation operator named layered disruption method is integrated into the proposed algorithm with the aim of enhancing the exploration and exploitation abilities of DMOEOA. To demonstrate the advantages of the proposed algorithm, various benchmarks have been selected with five different multiobjective optimization algorithms. The test results indicate that DMOEOA does exhibit better performances in these problems with a better balance between convergence and distribution. In addition, the new proposed algorithm is applied to the structural optimization of an elastic truss with the other five existing multiobjective optimization algorithms. The obtained results demonstrate that DMOEOA is not only an algorithm with good performance for benchmark problems but is also expected to have a wide application in real-world engineering optimization problems.

MeSH terms

Algorithms*