A vertical and horizontal crossover sine cosine algorithm with pattern search for optimal power flow in power systems

3T dilated inception network for enhanced autism spectrum disorder diagnosis using resting-state fMRI data

Autism spectrum disorder (ASD) is one of the complicated neurodevelopmental disorders that impacts the daily functioning and social interactions of individuals. It includes diverse symptoms and severity levels, making it challenging to diagnose and treat efficiently. Various deep learning (DL) based methods have been developed for diagnosing ASD, which rely heavily on behavioral assessment. However, existing techniques have suffered from poor diagnostic outcomes, higher computational complexity, and overfitting issues. To address these challenges, this research work introduces an innovative framework called 3T Dilated Inception Network (3T-DINet) for effective ASD diagnosis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) images. The proposed 3T-DINet technique designs a 3T dilated inception module that incorporates dilated convolutions along with the inception module, allowing it to extract multi-scale features from brain connectivity patterns. The 3T dilated inception module uses three distinct dilation rates (low, medium, and high) in parallel to determine local, mid-level, and global features from the brain. In addition, the proposed approach implements Residual networks (ResNet) to avoid the vanishing gradient problem and enhance the feature extraction ability. The model is further optimized using a Crossover-based Black Widow Optimization (CBWO) algorithm that fine-tunes the hyperparameters thereby enhancing the overall performance of the model. Further, the performance of the 3T-DINet model is evaluated using the five ASD datasets with distinct evaluation parameters. The proposed 3T-DINet technique achieved superior diagnosis results compared to recent previous works. From this simulation validation, it's clear that the 3T-DINet provides an excellent contribution to early ASD diagnosis and enhances patient treatment outcomes.

Triangular-based sine cosine algorithm for global search and feature selection

The sine cosine algorithm (SCA) is a popular population-based optimization technique that utilizes sine and cosine functions to navigate complex search spaces. However, its inherent simplicity can hinder optimal exploration-exploitation balance, particularly in high-dimensional problems, leading to slow convergence and reduced precision. To address these issues, we propose a novel triangular optimization (TO) strategy combined with a theft mechanism (TM), resulting in an enhanced algorithm named TTOSCA. We rigorously evaluate TTOSCA against 27 competing algorithms using the IEEE CEC2017 benchmark functions and apply the Wilcoxon signed-rank test to assess performance. Our results indicate that TTOSCA significantly improves precision and convergence speed. Furthermore, we develop a binary variant, BTTOSCA, to validate its effectiveness in discrete spaces through feature selection experiments on 17 datasets from UCI, including medical and high-dimensional gene data. Comparative analysis shows that BTTOSCA excels in both exploration and exploitation, achieving smaller feature subsets without compromising classification accuracy. This positions BTTOSCA as a powerful tool for feature selection in high-dimensional datasets.

An improved beetle antennae search algorithm and its application in coverage of wireless sensor networks

This manuscript presents ABSAS-CS-GSA, an improved iteration of the beetle antennae search (BAS) algorithm, tailored to address the shortcoming of the BAS with low convergence accuracy and easily falling into local optima. The improvements are structured into three principal components: first, dynamic step-size adjustment predicated on initial population to bolster solution accuracy and expedite convergence; second, position update mechanism integrating golden sine algorithm to diversify search patterns and expedite convergence; and third, population disturbance based on vertical and horizontal cross strategy to avoid falling into local optima. Experimental results on 12 benchmark functions demonstrate ABSAS-CS-GSA's superiority over the standard BAS and its four variants, as well as over two classic swarm intelligence algorithms. The simulation results of the algorithm applied to coverage optimization of wireless sensor networks with 30 nodes and 50 nodes reveal a marked improvement in both optimal and average coverage metrics relative to seven alternative algorithms. The refined algorithm exhibits excellent performance and is well-suited for tackling the coverage issue within wireless sensor networks.

A Crisscross-Strategy-Boosted Water Flow Optimizer for Global Optimization and Oil Reservoir Production

The growing intricacies in engineering, energy, and geology pose substantial challenges for decision makers, demanding efficient solutions for real-world production. The water flow optimizer (WFO) is an advanced metaheuristic algorithm proposed in 2021, but it still faces the challenge of falling into local optima. In order to adapt WFO more effectively to specific domains and address optimization problems more efficiently, this paper introduces an enhanced water flow optimizer (CCWFO) designed to enhance the convergence speed and accuracy of the algorithm by integrating a cross-search strategy. Comparative experiments, conducted on the CEC2017 benchmarks, illustrate the superior global optimization capability of CCWFO compared to other metaheuristic algorithms. The application of CCWFO to the production optimization of a three-channel reservoir model is explored, with a specific focus on a comparative analysis against several classical evolutionary algorithms. The experimental findings reveal that CCWFO achieves a higher net present value (NPV) within the same limited number of evaluations, establishing itself as a compelling alternative for reservoir production optimization.