.png){kind=icon}
.png){kind=icon}
Return
A corner point-based algorithm to solve constrained multi-objective optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-017-1126-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The search ability of an algorithm in terms of convergence and diversity can be improved with the help of corner points. A corner point-based algorithm (CPA) based on a differential evolution (DE) algorithm is proposed to solve constrained multi-objective optimization problems. The evolutionary algorithm consists of two stages. The first stage is to find corner points by the proposed method. The second stage is to approach the real Pareto front. A novel diversity and convergence mechanism is implemented in the second stage. The performance of the proposed algorithm is evaluated on nineteen test functions. Compared with the constrained handling techniques and latest optimization algorithms, the numerical results have indicated that the proposed algorithm is effective. At last, the algorithm is used to solve resource schedule in emergency management to further validate its effectiveness.
Keywords:
Corner points
Evolutionary algorithm
Differential evolution
Multi-objective
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
No organization information available