Efficient blood plasma distribution is essential for minimizing wastage and ensuring timely availability in blood banks. This study proposes an optimized assignment framework that enhances blood plasma management by maximizing utilization before expiration and ensuring Rhesus compatibility. We implement and compare three evolutionary optimization algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), with enhancements to balance exploration and exploitation for optimal inventory management. Extensive experiments were conducted by varying key control parameters, such as population size and the number of generations or iterations, while maintaining consistency across all algorithms. Performance evaluation on real-world blood bank datasets demonstrates that DE outperforms GA and PSO, achieving the lowest blood importation and expiration rates. The results highlight the potential of evolutionary computation in optimizing healthcare inventory management problems, which offers practical strategies for improving blood bank efficiency, data-driven decision-making, and patient care outcomes.

ABSTRACT In this paper, the improved chameleon swarm algorithm (ICSA) enhances the exploration–exploitation balance while optimizing feature subset selection. The integration of Lévy flight‐based exploration refines ICSA's search strategy, complemented by rotation‐type refinement and adaptive parameter‐setting mechanisms. These modifications ensure that exploration aligns effectively with the feature selection process, leading to a more adaptive and efficient approach. To evaluate ICSA's effectiveness, it is tested on the NSL‐KDD benchmark, a well‐established dataset in intrusion detection systems. Performance is assessed based on key metrics, including accuracy, detection rate, false alarm rate, execution time, and the number of selected features. Comparative analysis against six advanced classifiers demonstrates that ICSA achieves superior results with minimal computational overhead. The algorithm attains the highest accuracy (97.91%) and detection rate (98.75%), the fastest execution time, and the lowest false alarm rate (0.0021), eliminating the need for excessive feature selection. These results confirm that modifying feature selection mechanisms within ICSA significantly enhances computational efficiency and detection performance, as validated through rigorous experimental testing at the classifier level.

Abstract Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learning methods as a generative model to learn historical content, including global best and worst solutions, solution sequences, function evaluation patterns, solution space characteristics, population modification trajectories, and movement between local and global search processes. An LSTM-based architecture is trained on dynamic optimization data collected during the metaheuristic optimization process. The trained model generates an initial solution space and is integrated into the optimization algorithms to intelligently monitor the search process during exploration and exploitation phases. The proposed deep learning-based methods are evaluated on 55 benchmark functions of varying complexities, including CEC 2017 and compared with 13 biology-based, evolution-based, and swarm-based metaheuristic algorithms. Experimental results demonstrate that all the deep learning-based optimization algorithms achieve high-quality solutions, faster convergence rates, and significant performance improvements. These findings highlight the critical role of deep learning in addressing design issues, enhancing solution quality, trajectory, and performance speed in metaheuristic algorithms.

Abstract This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study.

Abstract The article investigates the influence of several initialization methods on the performance of the newly proposed advanced arithmetic optimization algorithm, also known as the nAOA. The initialization conditions considered include population sizes, diversity of the population, and the number of iterations. We used 23 different probability distributions with different diversity to test the influence of initialization schemes on the convergence and accuracy of the nAOA. The benchmark classical test functions and the functions defined in the CEC 2020 suite having different properties and modalities were used to compare the possible effects of the initialization methods. The numerical results showed that the nAOA is sensitive to population size and the number of iterations, which must be large for optimal performance. Friedman's and correlation tests were used to gain useful insight into the results we obtained. Findings showed that the performance of the nAOA is not particularly sensitive to the different initialization schemes used for most test functions. The implication is that nAOA is relatively stable and robust. However, the variant of beta distribution, b(3,2), recorded the lowest mean rank for most functions. Hence, it is the best performing initialization distribution scheme for the test problems considered. We can also observe from the experimental results that 47.83% and 100% of the classical and CEC2020 test functions used in this article show significant differences for different initialization methods, respectively. We also investigated the applicability of the nAOA to solve the unrelated parallel machine scheduling problem with sequence‐dependent setup times by using the best‐identified set of initialization methods. The performance of the nAOA is evaluated by comparing its solution to seven other well‐known metaheuristic algorithms, and the results reveal that the nAOA can provide promising results in solving all problem instances of the problem under study using the best initialization schemes.

Summary Convolutional neural networks (CNN) are deep learning models widely reported as performing well in several image classification tasks. Training the networks for optimal performance is considered an NP‐hard problem. The combination of a metaheuristic algorithm with CNN has been proposed to address this problem because the metaheuristic algorithms have efficient performance, parameter tuning, and stagnation prevention methods. Moreover, there is now an increasing need to apply the best performing metaheuristic algorithm to optimize the parameters, training and learning rate of the CNN models. This study aims to investigate the best performing metaheuristic algorithm for fine‐tuning the weights, biases, and hyperparameters of CNN networks for solving the problem of characterization of abnormalities in breast images. Furthermore, hybrid models consisting of a CNN architecture and five representative metaheuristic algorithms to efficiently detect breast cancer abnormalities are presented. The adopted approach involves training a CNN network using genetic algorithm (GA), whale optimization algorithm (WOA), multiverse optimizer (MVO), satin bower optimization (SBO), and life choice‐based optimization (LCBO) algorithms to optimize only weights and bias of the model. Two categories of experiments were carried out: the first involved training of the CNN model, while the second involved optimizing the training of CNN model with metaheuristic algorithms. A comparative analysis of the impact of the proposed GA, WOA, MVO, SBO, and LCBO metaheuristic algorithms on the performance of the CNN architecture was carried out. Empirical and statistical analyses are presented to validate further the findings obtained in the study. Results obtained showed that MVO, SBO and LCBO outperformed GA, WOA. The classification accuracy obtained for CNN‐GA, CNN‐WOA, CNN‐MVO, CNN‐SBO, and CNN‐LCBO at the fifth epoch was 0.76, 0.75, 0.84, 0.80, and 0.86, respectively. At the same time, the traditional CNN achieved an accuracy of 0.66. The outcome of this study showed that contrary to widespread practice, physics‐based, biology‐based, and human‐based optimization algorithms promise better performance parameter tuning for CNN as compared with evolutionary‐based and swarm‐based metaheuristic algorithms.

Abstract Deep learning models have been widely reported to have achieved significant performance in image processing and classification tasks. They have mainly been harnessed and applied to the problem of detecting abnormalities in digital breast images. However, the significant number and high dimensional space requirement of hyperparameters in deep learning models often make it challenging to find the best configuration for such parameters when tuning for best performance. In appropriately handling this performance tuning often lead to difficulty in striking balance between underfitting and overfitting. This article proposes an optimized convolutional neural network (CNN) architecture through application of hybrid selection model in obtaining best hyperparameter configuration which outperforms similar existing models. We approached this non‐trivial challenge by defining a hybrid of the grid‐based and random‐based model for the selection of hyperparameters and then investigate the performance of the configurations. To further improve the performance of the CNN model, data augmentation technique was applied. Furthermore, the study undertook a comparative study of the performance of the best configuration on some benchmarked datasets. The resulting model was applied to publicly available benchmark datasets, namely, the DDSM and MIAS datasets. Findings from the experimentations revealed that hyperparameters with Adam optimization algorithm showed superiority by yielding an accuracy of 1.0 using DDSM dataset, while SGD, RMSprop, Adam, and Adagrad output an accuracy of 0.9375 with MIAS dataset. The outcome of this study further strengthens the appropriateness of Adam optimizer and has also produced a state‐of‐the‐art CNN model suitable for solving the problem of detection and classification of breast cancer from digital mammography.

The unrelated parallel machine scheduling problem with sequence-dependent setup times is addressed in this paper with the objective of minimizing the elapsed time between the start and finish of a sequence of operations in a set of unrelated machines. The machines are considered unrelated because the processing speed is dependent on the job being executed and not on the individual machines. Generally, the problem is considered NP-hard, as it presents additional complexity to find an optimal solution in terms of minimum makespan. An advanced firefly metaheuristic optimization algorithm is introduced to solve this problem. The proposed method, called the FAII algorithm, aims to improve the standard firefly algorithm's performance by incorporating an enhanced global best solution update mechanism and adaptive mutation-based local and global neighborhood search scheme to improve the quality of the proposed algorithm's generated solution. Several experiments were conducted to compare and validate the proposed algorithms' performance on small and large-scale benchmarked problem instances with up to 12 machines and 120 job combinations. Moreover, the performance of the FAII was also compared with eight other metaheuristic algorithms, which were implemented in parallel with the FAII method. Furthermore, the numerical results of the FAII algorithm were compared with the scheduling results of six other well-known metaheuristics from the literature. The comparison results backed with a comprehensive statistical analysis showed the superiority of the enhanced FA-style scheduling optimization over other metaheuristic methods to find good quality solutions or minimum average makespan.

Blood is a valuable commodity in society due to its ability to save lives during crises. Furthermore, because of the scarcity of blood donors, blood assignment by blood banks requires meticulous planning and solid issuing policy. The multiple components of a blood banking system contribute to the complexity of maintaining an efficient structure for such a system. One particular aspect relates to the stochastic nature of the demand for blood units. This paper implements a mathematical model for a blood bank system in South Africa and additionally explores the possible implementation of a hybrid global optimisation metaheuristic approach for the efficient assignment of blood products in the blood bank system. The approximate optimisation method used is the hybridisation of the symbiotic organism search (SOS) algorithm and a pre-processing ecosystem services (PES) techniques. In order to show the practicability of the model and evaluate the accuracy and robustness of the newly proposed hybrid algorithm, several numerical computations were performed using synthetically generated datasets that fall within the initial blood volume bounds of 500 to 20, 000. The experimental results indicate that the hybrid symbiotic organisms search ecosystem services optimisation algorithm offers better solutions for blood allocation under a dynamic environment than does the standard symbiotic organism search algorithm and other previously proposed hybrid versions of the SOS methods.

Security of lives and properties is highly important for enhanced quality living. Smart home automation and its application have received much progress towards convenience, comfort, safety, and home security. With the advances in technology and the Internet of Things (IoT), the home environment has witnessed an improved remote control of appliances, monitoring, and home security over the internet. Several home automation systems have been developed to monitor movements in the home and report to the user. Existing home automation systems detect motion and have surveillance for home security. However, the logical aspect of averting unnecessary or fake notifications is still a major area of challenge. Intelligent response and monitoring make smart home automation efficient. This work presents an intelligent home automation system for controlling home appliances, monitoring environmental factors, and detecting movement in the home and its surroundings. A deep learning model is proposed for motion recognition and classification based on the detected movement patterns. Using a deep learning model, an algorithm is developed to enhance the smart home automation system for intruder detection and forestall the occurrence of false alarms. A human detected by the surveillance camera is classified as an intruder or home occupant based on his walking pattern. The proposed method’s prototype was implemented using an ESP32 camera for surveillance, a PIR motion sensor, an ESP8266 development board, a 5 V four‐channel relay module, and a DHT11 temperature and humidity sensor. The environmental conditions measured were evaluated using a mathematical model for the response time to effectively show the accuracy of the DHT sensor for weather monitoring and future prediction. An experimental analysis of human motion patterns was performed using the CNN model to evaluate the classification for the detection of humans. The CNN classification model gave an accuracy of 99.8%.

Abstract The quadratic assignment problem (QAP) is a well‐known challenging combinatorial optimization problem that has received many researchers' attention with varied real‐world and industrial applications areas. It is noteworthy to mention that a plethora of nature‐inspired optimization algorithms have successfully been used to solve various optimization problems, including several variants of the QAPs. In this article, a comprehensive literature review is presented to show the most relevant nature‐inspired algorithms that have been used in solving the QAP. More so, extensive experiments are conducted and analyzed to show the performance of the well‐known state‐of‐the‐art nature‐inspired meta‐heuristic optimization algorithms in solving the QAP, including the ant colony optimization (ACO), bat algorithm, genetic algorithm (GA), particle swarm optimization (PSO), and tabu search algorithm. Besides, a modified variant of the discrete PSO algorithm is implemented and compared with existing approaches. The six selected algorithms' performances, including the modified PSO, are validated on eight commonly used QAP instances of varying complexity and size, considering the quality of solutions achieved and computational time consumed by the representative algorithms. The numerical results revealed that the most competitive algorithm was ACO, while the GA appeared to be the worst performed algorithm among the six compared meta‐heuristic algorithms. However, based on the extensive analysis conducted on the tested algorithms, further improvements are suggested, including implementing new modified versions of the tested algorithms to tackle the QAP and its variant instances.

The smart home is now an established area of interest and research that contributes to comfort in modern homes. With the Internet being an essential part of broad communication in modern life, IoT has allowed homes to go beyond building to interactive abodes. In many spheres of human life, the IoT has grown exponentially, including monitoring ecological factors, controlling the home and its appliances, and storing data generated by devices in the house in the cloud. Smart home includes multiple components, technologies, and devices that generate valuable data for predicting home and environment activities. This work presents the design and development of a ubiquitous, cloud-based intelligent home automation system. The system controls, monitors, and oversees the security of a home and its environment via an Android mobile application. One module controls and monitors electrical appliances and environmental factors, while another module oversees the home’s security by detecting motion and capturing images. Our work uses a camera to capture images of objects triggered by their motion being detected. To avoid false alarms, we used the concept of machine learning to differentiate between images of regular home occupants and those of an intruder. The support vector machine algorithm is proposed in this study to classify the features of the image captured and determine if it is that of a regular home occupant or an intruder before sending an alarm to the user. The design of the mobile application allows a graphical display of the activities in the house. Our work proves that machine learning algorithms can improve home automation system functionality and enhance home security. The work’s prototype was implemented using an ESP8266 board, an ESP32-CAM board, a 5 V four-channel relay module, and sensors.

Summary A distributed system consists of a collection of autonomous heterogeneous resources that provide resource sharing and a common platform for running parallel compute‐intensive applications. The different application characteristics combined with the heterogeneity and performance variations of the distributed system make it difficult to find the optimal set of needed resources. When deployed, user applications are usually handled by application domain experts or system administrators who depending on the infrastructure provide a scheduling strategy for selecting the best candidate resource over a set of available resources. However, the provided strategy is usually generic, aimed at handling a wide array of applications and does not take into consideration specific application resource requirements. As such, an intelligent method for selecting the best resources based on expert knowledge is needed. In this paper, we propose a neural network‐based multi‐agent resource selection technique capable of mimicking the services of an expert user. In addition, to cope with the geographical distribution of the underlying system, we employ a multi‐agent coordination mechanism. The proposed neural network‐based scheduling framework combined with the multi‐agent intelligence is a unique approach to efficiently deal with the resource selection problem. Results run on a simulated environment show the efficiency of our proposed method. Several scheduling simulations were conducted to compare the performance of some conventional resource selection methods against the proposed agent‐based neural network technique. The results obtained indicate that the agent‐based approach outperformed the classical algorithms by reducing the amount of time required to search for suitable resources irrespective of the resource size. Copyright © 2016 John Wiley & Sons, Ltd.

Summary In grid computing environment, several classes of multi‐component applications exist. These types of applications may often require additional resources of different types that go beyond what is available in any of the sites making up the grid resource composition. The heterogeneity nature of both the user application and the computing environment makes this a challenging problem. However, the current off‐the‐shelf scheduling software can hardly cope with these diversities in distributed computing application frameworks. Therefore, there is the need for an adequate scheduling system that would grant simultaneous or coordinated access to application of multi‐component nature that requires resources of possibly multiple types, in multiple locations, managed by different resource providers. The main focus of this paper is to develop a mobile agent scheduling model that addresses the aforementioned challenge. A scheduling policy that pertains to job scheduling and resource allocation is proposed. The scheduling policy treats different multi‐component applications requiring diverse heterogeneous resources fairly. The policy is used by mobile agents to schedule user applications and to also find available and suitable distributed resource that are capable of executing user application at a very minimal time. Copyright © 2015 John Wiley & Sons, Ltd.