The advent of the intelligent information era has witnessed a proliferation complex optimization problems across various disciplines. Although existing meta-heuristic algorithms have demonstrated efficacy in many scenarios, they still struggle with certain challenges such as premature convergence, insufficient exploration, and lack robustness high-dimensional, nonconvex search spaces. These limitations underscore need for novel techniques that can better balance exploration exploitation...

As the COVID-19 pandemic rapidly spreads across world, regrettably, misinformation and fake news related to have also spread remarkably. Such has confused people. To be able detect such misinformation, an effective detection method should applied obtain more accurate information. This will help people researchers easily differentiate between true news. The objective of this research was introduce enhanced evolutionary approach better results compared with previous approaches. proposed aimed...

Cloud computing offers dynamic, scalable, and virtualized resources to end users over the internet. Load balancing is crucial for efficient resource use, distributing workloads across multiple prevent overloading. utilization processing time reduction, but traditional algorithms are often stuck at local maxima, leading unequal allocation performance decline. A metaheuristic based algorithm proposed dynamically adjust load distribution, ensuring resilience sensitivity changing while managing...

Software defect prediction (SDP) is crucial in the early stages of defect-free software development before testing operations take place. Effective SDP can help test managers locate defects and defect-prone modules. This facilitates allocation limited quality assurance resources optimally economically. Feature selection (FS) a complicated problem with polynomial time complexity. For dataset N features, complete search space has 2N feature subsets, which means that algorithm needs an...

In the last decade, devices and appliances utilizing Internet of Things (IoT) have expanded tremendously, which has led to revolutionary developments in network industry. Smart homes cities, wearable devices, traffic monitoring, health systems, energy savings are typical IoT applications. The diversity standards, protocols, computational resources makes them vulnerable security attackers. Botnets challenging threats that cause severe Distributed Denial Service (DDoS) attacks. Intrusion...

Abstract A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous deficiencies that has to be strengthened. Premature convergence a disparity between exploitation exploration are some these challenges. Furthermore, the absence transfer parameter in typical when moving from phase direct impact on algorithm’s performance. This work proposes novel...

Archimedes Optimization Algorithm (AOA) is a new physics-based optimizer that simulates principles. AOA has been used in variety of real-world applications because potential properties such as limited number control parameters, adaptability, and changing the set solutions to prevent being trapped local optima. Despite wide acceptance AOA, it some drawbacks, assumption individuals modify their locations depending on altered densities, volumes, accelerations. This causes various shortcomings...

The use of IoT technology is rapidly increasing in healthcare development and smart system for fitness programs, monitoring, data analysis, etc. To improve the efficiency various studies have been conducted this field to achieve improved precision. architecture proposed herein based on integrated with a cloud which power absorption accuracy are major concerns. We discuss analyze domain performance systems related health care. Standards communication transmission reception can help understand...

Abstract Sea horse optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic the nuanced locomotion of SHO integrates logarithmic helical equation Levy flight, effectively incorporating both random movements with substantial step sizes refined local exploitation. Additionally, utilization Brownian motion facilitates more...

In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO a math-inspired optimizer that has many limitations in handling complex multi-modal tries solve these drawbacks using 2 operators: phasor operator for diversity enhancement adaptive p-best mutation strategy preventing it converging local optima. To validate effectiveness suggested optimizer, comprehensive...

Abstract The article introduces an innovative approach to global optimization and feature selection (FS) using the RIME algorithm, inspired by RIME-ice formation. algorithm employs a soft-RIME search strategy hard-RIME puncture mechanism, along with improved positive greedy resist getting trapped in local optima enhance its overall capabilities. also Binary modified (mRIME), binary adaptation of address unique challenges posed FS problems, which typically involve spaces. Four different types...

The problem of finding the shortest path between two nodes is a common that requires solution in many applications like games, robotics, and real-life problems. Since its deals with large number possibilities. Therefore, parallel algorithms are suitable to solve this optimization has attracted lot researchers from both industry academia find optimal terms runtime, speedup, efficiency, cost compared sequential algorithms. In mountain climbing, start node under reach destination fundamental...

This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims select optimal parameters in its process minimize intrusion detection errors networks. has been implemented using EvoloPy NN framework, open-source Python tool specialized for training MLPs evolutionary algorithms. For purposes of comparing HHO model against other methodologies...

Abstract According to Moore’s law, computer processing hardware technology performance is doubled every year. To make effective use of this technological development, the algorithmic solutions have be developed at same speed. Consequently, it necessary design parallel algorithms implemented on machines. This helps exploit multi-core environment by executing multiple instructions simultaneously processors. Traveling Salesman (TSP) a challenging non-deterministic-hard optimization problem that...

The medical field is witnessing rapid adoption of artificial intelligence (AI) and machine learning (ML), revolutionizing disease diagnosis treatment management. Researchers explore how AI ML can optimize decision-making, promising to transform healthcare. Feed Forward Neural Networks (FNN) are widely used create predictive models, cross-validated by experts. However, complex data like diabetes leads multi-modal search spaces prone local minima, affecting optimal solutions. In this study, we...

Feature selection (FS) is a challenging data mining problem that incorporates complex search process to find the most informative feature subset. In brute force methods generating entire space and applying an exhaustive makes FS NP-hard problem. Meta-heuristic algorithms are good alternative solutions provide (near) optimal through random instead of complete search. this paper, approach based on Moth Flame optimization algorithm (MFO) k-NN classifier proposed. MFO recent meta-heuristic has...

Abstract To identify risk factors for smoking among pregnant women, and adverse perinatal outcomes women. A case–control study of singleton full-term women who gave birth at a university hospital in Jordan June 2020. Pregnant were divided into three groups according to their status, active, passive, non-smokers. They interviewed using semi-structured questionnaire that included demographic data, current pregnancy history, neonatal outcomes. Low-level maternal education, unemployment,...

As cloud computing usage grows, data centers play an increasingly important role.To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks manage performance effectively.The purpose of this study provide extensive analysis task allocation management techniques employed in centers.The aim systematically categorize organize previous research by identifying the methodologies, categories, gaps.A literature review was conducted,...

Software requirements prioritization plays a crucial role in software development. It can be viewed as the process of ordering by determining which must done first and later. Powerful techniques are paramount importance to finish implementation on time budget. Many factors affect requirement such stakeholder expectations, complexity, dependency, scalability, risk cost. Therefore, finding proper order is challenging process. Hence, different types have been developed support this task. In...