.png){kind=icon}
Return
Hierarchical adaptive cuckoo search algorithm for global optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s10586-024-04924-3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The Cuckoo Search algorithm (CS) still has some shortcomings, such as insufficient global search capability, challenges in exploring values beyond local optima, and underutilization of effective information within populations. In order to address these existing shortcomings and enhance the search performance of the Cuckoo Search algorithm, a proposed solution is the introduction of the Hierarchical Adaptive Cuckoo Search algorithm for Global Optimization (HAGCS). This algorithm integrates the concept of distribution entropy and a weighted fitness-value-distance-balanced ranking selection method. These additions aim to enhance information exchange within populations and strengthen the local search abilities of CS algorithms. Furthermore, the algorithm includes an adaptive global search strategy to improve global search capabilities, enhance population diversity, and aid in the discovery of global optimal solutions. Additionally, a hierarchical nest replacement strategy is introduced to refine position updates, reducing the risk of getting trapped in local extremes. The incorporation of a sawtooth linear function population adaptive strategy accelerates algorithm convergence, while a shifting population enhances the algorithm's capacity to navigate suboptimal scenarios. The experiment assesses the impact of the proposed strategy on step size, compares it with variant algorithms and the TOP algorithm on the CEC 2022 test set, and applies the algorithm to three practical engineering optimization problems. The results demonstrate that HAGCS exhibits good practicality and feasibility.
Keywords:
Cuckoo search
Global search
Population adaptation
Solving distributional entropy
Hierarchical structure
Journal
C
IF:
4.1
Papers:
5.1K
Citations:
7.5K
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveShareSave
ShareSave
View more