.png){kind=icon}
.png){kind=icon}
Return
A novel two-stage constraints handling framework for real-world multi-constrained multi-objective optimization problem based on evolutionary algorithm
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-020-02174-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-constrained multi-objective optimization is a challenging topic, which is very common in dealing with real-world problems. This paper proposes a novel two-stage rho(g /) mu(g) framework based on multi-objective evolutionary algorithm (MOEA) to solve the multi-constrained multi-objective optimization problems (MCMOPs), which dynamically balances the diversity and convergence of solutions. During the multi-constraints handling process, rho(g /) mu(g) -MOEA makes the reduction of violated constraints as its primary goal, and converges to feasible regions by a proposed rho(g) -criterion based constraints relaxation method. Moreover, in the late stage of evolution, by introducing the improved dynamic stochastic ranking (DSR) strategy, the potential infeasible individuals are utilized to find more feasible regions, which would guarantee a good distribution of the obtained Pareto frontiers. Thereafter, the proposed framework combined with non-dominated sorting genetic algorithm II (NSGAII) is applied to ten benchmark functions and a series of real-world MCMOPs, and the performances are compared with those obtained by some state-of-the-art constraints handling methods. Experimental results indicate that the proposed rho(g /) mu(g) framework outperforms the current efficient methods in dealing with test CMOPs, and can achieve satisfactory results when solving real-world MCMOPs.
Keywords:
Multi-constrained multi-objective optimization
Two-stage constraints handling
Constraint relaxation
Dynamic stochastic ranking
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.5
Papers:
7.5K
Citations:
1.7W
Organization
E
View more organizations