Search Results (4)

Optimal surgical scheduling necessitates a strategic balance between elective efficiency and responsiveness to stochastic emergency arrivals. This study evaluates a Genetic Algorithm alongside discretized variants of Particle Swarm Optimization, the Secretary Bird Optimization Algorithm, and the Mantis Shrimp Optimization Algorithm. These algorithms are assessed within a dynamic three-stage flexible flow shop model under no-buffer blocking constraints. Findings from 300 Monte Carlo replications demonstrate that while the Genetic Algorithm achieves peak global efficiency, discretized bio-inspired algorithms reach a comparable statistical efficiency frontier. Notably, the discretized Secretary Bird Optimization Algorithm facilitates superior emergency integration by maintaining natural capacity buffers, whereas the aggressive local optimization characteristic of alternative methods often triggers resource saturation in recovery units. These results indicate a potential recovery of 90 annual operating hours per theater.These results indicate a potential recovery of 90 annual operating hours per theater, representing a 6.7% increase in resource utilization efficiency. This improvement provides a critical data-driven capacity margin to mitigate the non-prioritized (Non-GES) surgical backlog in Chilean public hospitals. Full article

This paper proposes a novel intelligent optimization algorithm, ICPMSHOA, that effectively balances population diversity and convergence performance by integrating an iterative chaotic map with infinite collapses (ICMIC), centroid opposition-based learning, and periodic mutation strategy. To verify its performance, we adopted benchmark functions from the IEEE CEC 2017 and 2022 standard test suites and compared it with six algorithms, including OOA and BWO. The results show that ICPMSHOA has significant improvements in convergence speed, global search capability, and stability, with statistically significant advantages. Furthermore, the algorithm performs outstandingly in three practical engineering constrained optimization problems: Haverly’s pooling problem, hybrid pooling–preparation problem, and optimization design of industrial refrigeration systems. This study confirms that ICPMSHOA provides efficient and reliable solutions for complex optimization tasks and has strong practical value in engineering scenarios. Full article

This paper presents a novel meta-heuristic algorithm inspired by the visual capabilities of the mantis shrimp (Gonodactylus smithii), which can detect linearly and circularly polarized light signals to determine information regarding the polarized light source emitter. Inspired by these unique visual characteristics, the Mantis Shrimp Optimization Algorithm (MShOA) mathematically covers three visual strategies based on the detected signals: random navigation foraging, strike dynamics in prey engagement, and decision-making for defense or retreat from the burrow. These strategies balance exploitation and exploration procedures for local and global search over the solution space. MShOA’s performance was tested with 20 testbench functions and compared against 14 other optimization algorithms. Additionally, it was tested on 10 real-world optimization problems taken from the IEEE CEC2020 competition. Moreover, MShOA was applied to solve three studied cases related to the optimal power flow problem in an IEEE 30-bus system. Wilcoxon and Friedman’s statistical tests were performed to demonstrate that MShOA offered competitive, efficient solutions in benchmark tests and real-world applications. Full article

Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN–Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively. Full article