.png){kind=icon}
.png){kind=icon}
Return
A novel parameter-free filled function method and its application in multi-objective optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1080/02331934.2026.2653846.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The filled function method is an effective approach for global optimization. The core of this method lies in the construction and design of the filled function. In this paper, we present a new parameter-free continuously differentiable filled function, whose gradient information can be efficiently utilized by local search algorithms. For diverse optimization problems, it eliminates the need for intricate parameter tuning. On the basis of theoretical analysis, an easily implementable filled function method is devised. The feasibility and effectiveness of the new method are confirmed by the comparative results of numerical experiments. Furthermore, we apply the new method to multi-objective optimization problems, and the results further validate the efficiency of the algorithm.
Keywords:
Global optimization
global minimizer
filled function method
multi-objective optimization