Pseodo-random number initialization vs good nodes set initialization. by Junhao Wei (6816803)

Populations generated by the initialization of the good nodes set. by Yanzhao Gu (21192659)

Population initialized by Good Nodes Set. by Junhao Wei (6816803)

“…The performance of the canonical WOA is improved through innovative strategies: first, an initialization process using Good Nodes Set is introduced to ensure that the search starts from a higher-quality baseline; second, a distance-based guided search strategy is employed to adjust the search direction and intensity by calculating the distance to the optimal solution, which enhances the algorithm’s ability to escape local optima; and lastly, LSWOA introduces an enhanced spiral updating strategy, while the enhanced spiral-enveloping prey strategy effectively balances exploration and exploitation by dynamically adjusting the spiral shape parameters to adapt to different stages of the search, thereby more accurately updating the positions of individuals and improving convergence speed. …”

The initial parameters of algorithms. by Eman A. Atta (14851677)

The initial parameters of algorithms. by Eman A. Atta (14851677)

(a) Random initialized population distribution (b) Good point set initialized population distribution. by Tengfei Ma (597633)

Iteration curves of different algorithms. by Tengfei Ma (597633)

“…<div><p>To address the limitations of the Zebra Optimization Algorithm (ZOA), including insufficient late-stage optimization search capability, susceptibility to local optima, slow convergence, and inadequate exploration, this paper proposes an enhanced Zebra Optimization Algorithm integrating opposition-based learning and a dynamic elite-pooling strategy (OP-ZOA: Opposition-Based Learning Dynamic Elite-Pooling Zebra Optimization Algorithm). he proposed search algorithm employs a good point set-elite opposition-based learning mechanism to initialize the population, enhancing diversity and facilitating escape from local optima. …”

Flowchart of OP-ZOA algorithm. by Tengfei Ma (597633)

“…<div><p>To address the limitations of the Zebra Optimization Algorithm (ZOA), including insufficient late-stage optimization search capability, susceptibility to local optima, slow convergence, and inadequate exploration, this paper proposes an enhanced Zebra Optimization Algorithm integrating opposition-based learning and a dynamic elite-pooling strategy (OP-ZOA: Opposition-Based Learning Dynamic Elite-Pooling Zebra Optimization Algorithm). he proposed search algorithm employs a good point set-elite opposition-based learning mechanism to initialize the population, enhancing diversity and facilitating escape from local optima. …”

Quantitative indicators of the weightless initialization model before and after improvement of the tire data source. by Mei Zhang (46138)

Quantitative metrics for no re-initialization models before and after improvement to the coin data source. by Mei Zhang (46138)

Quantitative metrics for weightless initialization models before and after cellular data source improvement. by Mei Zhang (46138)

Quantitative metrics of the no-weight initialization model before and after the improvement of the lighthouse data source. by Mei Zhang (46138)

The synchronization network’s graph layout. by Makoto Mizuno (235529)

Quantitative metrics of the without re-initialization model before and after the improvement of the rugby data source. by Mei Zhang (46138)

The parameters of the algorithms. by Yu Li (183836)

Initial GA Criteria. by Filipe Menezes (13205305)

Initial population example. by Filipe Menezes (13205305)

An improved Grey Wolf Optimization based heuristic initialization algorithm for feature selection in P2P lending default prediction by Muhammad Sam'an (20649621)

“…The research incorporated Ant Colony Optimization (ACO) algorithm for better population initialization to overcome this limitation. …”

Diagram of results generated during the initial segmentation process. by Dingyuan Hu (845782)

Comparison of the initial population. by Shenjie Cheng (18563736)