Red-tailed hawk algorithm for numerical optimization and real-world problems

An Intelligent Model of Segmentation and Classification Using Enhanced Optimization-Based Attentive Mask RCNN and Recurrent MobileNet With LSTM for Multiple Sclerosis Types With Clinical Brain MRI

In healthcare sector, magnetic resonance imaging (MRI) images are taken for multiple sclerosis (MS) assessment, classification, and management. However, interpreting an MRI scan requires an exceptional amount of skill because abnormalities on scans are frequently inconsistent with clinical symptoms, making it difficult to convert the findings into effective treatment strategies. Furthermore, MRI is an expensive process, and its frequent utilization to monitor an illness increases healthcare costs. To overcome these drawbacks, this research employs advanced technological approaches to develop a deep learning system for classifying types of MS through clinical brain MRI scans. The major innovation of this model is to influence the convolution network with attention concept and recurrent-based deep learning for classifying the disorder; this also proposes an optimization algorithm for tuning the parameter to enhance the performance. Initially, the total images as 3427 are collected from database, in which the collected samples are categorized for training and testing phase. Here, the segmentation is carried out by adaptive and attentive-based mask regional convolution neural network (AA-MRCNN). In this phase, the MRCNN's parameters are finely tuned with an enhanced pine cone optimization algorithm (EPCOA) to guarantee outstanding efficiency. Further, the segmented image is given to recurrent MobileNet with long short term memory (RM-LSTM) for getting the classification outcomes. Through experimental analysis, this deep learning model is acquired 95.4% for accuracy, 95.3% for sensitivity, and 95.4% for specificity. Hence, these results prove that it has high potential for appropriately classifying the sclerosis disorder.

Soft Materials and Devices Enabling Sensorimotor Functions in Soft Robots

Sensorimotor functions, the seamless integration of sensing, decision-making, and actuation, are fundamental for robots to interact with their environments. Inspired by biological systems, the incorporation of soft materials and devices into robotics holds significant promise for enhancing these functions. However, current robotics systems often lack the autonomy and intelligence observed in nature due to limited sensorimotor integration, particularly in flexible sensing and actuation. As the field progresses toward soft, flexible, and stretchable materials, developing such materials and devices becomes increasingly critical for advanced robotics. Despite rapid advancements individually in soft materials and flexible devices, their combined applications to enable sensorimotor capabilities in robots are emerging. This review addresses this emerging field by providing a comprehensive overview of soft materials and devices that enable sensorimotor functions in robots. We delve into the latest development in soft sensing technologies, actuation mechanism, structural designs, and fabrication techniques. Additionally, we explore strategies for sensorimotor control, the integration of artificial intelligence (AI), and practical application across various domains such as healthcare, augmented and virtual reality, and exploration. By drawing parallels with biological systems, this review aims to guide future research and development in soft robots, ultimately enhancing the autonomy and adaptability of robots in unstructured environments.

Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning

In recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, and environmental monitoring. However, planning reliable, safe, and economical paths for UAVs in real-world environments remains a significant challenge. In this paper, we propose a multi-strategy improved red-tailed hawk (IRTH) algorithm for UAV path planning in real environments. First, we enhance the quality of the initial population in the algorithm by using a stochastic reverse learning strategy based on Bernoulli mapping. Then, the quality of the initial population is further improved through a dynamic position update optimization strategy based on stochastic mean fusion, which enhances the exploration capabilities of the algorithm and helps it explore promising solution spaces more effectively. Additionally, we proposed an optimization method for frontier position updates based on a trust domain, which better balances exploration and exploitation. To evaluate the effectiveness of the proposed algorithm, we compare it with 11 other algorithms using the IEEE CEC2017 test set and perform statistical analysis to assess differences. The experimental results demonstrate that the IRTH algorithm yields competitive performance. Finally, to validate its applicability in real-world scenarios, we apply the IRTH algorithm to the UAV path-planning problem in practical environments, achieving improved results and successfully performing path planning for UAVs.

A Reinforced Whale Optimization Algorithm for Solving Mathematical Optimization Problems

The whale optimization algorithm has several advantages, such as simple operation, few control parameters, and a strong ability to jump out of the local optimum, and has been used to solve various practical optimization problems. In order to improve its convergence speed and solution quality, a reinforced whale optimization algorithm (RWOA) was designed. Firstly, an opposition-based learning strategy is used to generate other optima based on the best optimal solution found during the algorithm's iteration, which can increase the diversity of the optimal solution and accelerate the convergence speed. Secondly, a dynamic adaptive coefficient is introduced in the two stages of prey and bubble net, which can balance exploration and exploitation. Finally, a kind of individual information-reinforced mechanism is utilized during the encircling prey stage to improve the solution quality. The performance of the RWOA is validated using 23 benchmark test functions, 29 CEC-2017 test functions, and 12 CEC-2022 test functions. Experiment results demonstrate that the RWOA exhibits better convergence accuracy and algorithm stability than the WOA on 20 benchmark test functions, 21 CEC-2017 test functions, and 8 CEC-2022 test functions, separately. Wilcoxon's rank sum test shows that there are significant statistical differences between the RWOA and other algorithms.

Multiple elite strategy enhanced RIME algorithm for 3D UAV path planning

With the wave of artificial intelligence sweeping the world in recent years, UAVs is widely used in various fields. UAV path planning has attracted much attention from scientists as an essential part of UAV work. In order to design an efficient and reasonable 3D UAV path planning program, recent researchers have invented and improved many algorithms. This paper proposes an elite RIME algorithm for 3D UAV path planning. First, we propose an elite reverse learning population selection strategy based on piecewise mapping to enhance the population diversity of the algorithm for better exploration. Second, this paper proposes a stochastic factor-controlled elite pool exploration strategy so that the algorithm is difficult to enter the local optimum and can better explore the global optimum. Then, this paper proposes a hard frost puncture exploitation strategy based on the sine-cosine function so that the algorithm can find the global optimum faster during the exploitation process. Meanwhile, in order to test the performance of the algorithm proposed in this paper, we compare it with 13 other intelligent optimization algorithms that are classical and popular nowadays on 52 test functions in three test sets, CEC2017, CEC2020, and CEC2022, and obtain competitive results. Finally, we applied it to the 3D UAV path planning problem in three different terrain scenarios, and the ELRIME algorithm achieved good results in all of them. Especially in the 7-peak model, the ELRIME algorithm improves the performance of the RIME algorithm by a factor of two. In the 9-peak model, the average value aspect also reduce the cost by 91 compared to the RIME algorithm, and more importantly, it has the smallest fluctuation in 30 runs, which is among the most stable of all the compared algorithms. In the 12-peak model, its stability is also significantly enhanced, and in terms of worst-case cost, it improves the cost by 340 compared to RIME.

An improved nutcracker optimization algorithm for discrete and continuous optimization problems: Design, comprehensive analysis, and engineering applications

This study is presented to examine the performance of a newly proposed metaheuristic algorithm within discrete and continuous search spaces. Therefore, the multithresholding image segmentation problem and parameter estimation problem of both the proton exchange membrane fuel cell (PEMFC) and photovoltaic (PV) models, which have different search spaces, are used to test and verify this algorithm. The traditional techniques could not find approximate solutions for those problems in a reasonable amount of time, so researchers have used metaheuristic algorithms to overcome those shortcomings. However, the majority of metaheuristic algorithms still suffer from slow convergence speed and stagnation into local minima problems, which makes them unsuitable for tackling these optimization problems. Therefore, this study proposes an improved nutcracker optimization algorithm (INOA) for better solving those problems in an acceptable amount of time. INOA is based on improving the performance of the standard algorithm using a newly proposed convergence improvement strategy that aims to improve the convergence speed and prevent stagnation in local minima. This algorithm is first applied to estimating the unknown parameters of the single-diode, double-diode, and triple-diode models for a PV module and a solar cell. Second, four PEMFC modules are used to further observe INOA's performance for the continuous optimization challenge. Finally, the performance of INOA is investigated for solving the multi-thresholding image segmentation problem to test its effectiveness in a discrete search space. Several test images with different threshold levels were used to validate its effectiveness, stability, and scalability. Comparison to several rival optimizers using various performance indicators, such as convergence curve, standard deviation, average fitness value, and Wilcoxon rank-sum test, demonstrates that INOA is an effective alternative for solving both discrete and continuous optimization problems. Quantitively, INOA could solve those problems better than the other rival optimizers, with improvement rates for final results ranging between 0.8355 % and 3.34 % for discrete problems and 4.97 % and 99.9 % for continuous problems.

A peer-and self-group competitive behavior-based socio-inspired approach for household electricity conservation

This paper proposes a knowledge-based decision-making system for energy bill assessment and competitive energy consumption analysis for energy savings. As humans have a tendency toward comparison between peers and self-groups, the same concept of competitive behavior is utilized to design knowledge-based decision-making systems. A total of 225 house monthly energy consumption datasets are collected for Maharashtra state, along with a questionnaire-based survey that includes socio-demographic information, household appliances, family size, and some other parameters. After data collection, the pre-processing technique is applied for data normalization, and correlation technique-based key features are extracted. These features are used to classify different house categories based on consumption. A knowledge-based system is designed based on historical datasets for future energy consumption prediction and comparison with actual usage. These comparative studies provide a path for knowledgebase system design to generate monthly energy utilization reports for significant behavior changes for energy savings. Further, Linear Programming and Genetic Algorithms are used to optimize energy consumption for different household categories based on socio-demographic constraints. This will also benefit the consumers with an electricity bill evaluation range (i.e., normal, high, or very high) and find the energy conservation potential (kWh) as well as a cost-saving solution to solve real-world complex electricity conservation problem.

Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm

The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 115 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both exploitation and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The performance evaluation of the HO algorithm encompasses various aspects, including a comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, and IWO recognized as the most extensively researched metaheuristics, AOA as recently developed algorithms, and CMA-ES as high-performance optimizers acknowledged for their success in the IEEE CEC competition. According to the statistical post hoc analysis, the HO algorithm is determined to be significantly superior to the investigated algorithms. The source codes of the HO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho .

A bibliometric and visualized analysis of nanoparticles in musculoskeletal diseases (from 2013 to 2023)

As the pace of research on nanomedicine for musculoskeletal (MSK) diseases accelerates, there remains a lack of comprehensive analysis regarding the development trajectory, primary authors, and research focal points in this domain. Additionally, there's a need of detailed elucidation of potential research hotspots. The study gathered articles and reviews focusing on the utilization of nanoparticles (NPs) for MSK diseases published between 2013 and 2023, extracted from the Web of Science database. Bibliometric and visualization analyses were conducted using various tools such as VOSviewer, CiteSpace, Pajek, Scimago Graphica, and the R package. China, the USA, and India emerged as the key drivers in this research domain. Among the numerous institutions involved, Shanghai Jiao Tong University, Chinese Academy of Sciences, and Sichuan University exhibited the highest productivity levels. Vallet-Regi Maria emerged as the most prolific author in this field. International Journal of Nanomedicine accounted for the largest number of publications in this area. The top five disorders of utmost significance in this field include osteosarcoma, cartilage diseases, bone fractures, bone neoplasms, and joint diseases. These findings are instrumental in providing researchers with a comprehensive understanding of this domain and offer valuable perspectives for future investigations.

A hybrid algorithm of grey wolf optimizer and harris hawks optimization for solving global optimization problems with improved convergence performance

The grey wolf optimizer is an effective and well-known meta-heuristic algorithm, but it also has the weaknesses of insufficient population diversity, falling into local optimal solutions easily, and unsatisfactory convergence speed. Therefore, we propose a hybrid grey wolf optimizer (HGWO), based mainly on the exploitation phase of the harris hawk optimization. It also includes population initialization with Latin hypercube sampling, a nonlinear convergence factor with local perturbations, some extended exploration strategies. In HGWO, the grey wolves can have harris hawks-like flight capabilities during position updates, which greatly expands the search range and improves global searchability. By incorporating a greedy algorithm, grey wolves will relocate only if the new location is superior to the current one. This paper assesses the performance of the hybrid grey wolf optimizer (HGWO) by comparing it with other heuristic algorithms and enhanced schemes of the grey wolf optimizer. The evaluation is conducted using 23 classical benchmark test functions and CEC2020. The experimental results reveal that the HGWO algorithm performs well in terms of its global exploration ability, local exploitation ability, convergence speed, and convergence accuracy. Additionally, the enhanced algorithm demonstrates considerable advantages in solving engineering problems, thus substantiating its effectiveness and applicability.