A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

A prosperous and thorough analysis of gravity profiles for resources exploration utilizing the metaheuristic Bat Algorithm

Here, we present a remarkable methodology for unveiling subsurface structures with the potential to transform the exploration of mineral and ores resources, as well as the study of volcanic activity. By incorporating the Metaheuristic Bat algorithm (MBA) with the second horizontal gravity gradient (SHG) and employing variable window lengths, we aim to eliminate the regional effect in gravity data, thereby improving the precision of subsurface structure parameter estimation. Through rigorous evaluation on synthetic cases, we have demonstrated the robustness of our approach and its ability to handle diverse geological complexities and noise levels. Furthermore, our method has been applied to actual gravity data from three distinct locations: Canada, India, and Cuba, yielding excellent results that confirm the reliability and applicability of our methodology to real-world geological settings. We are confident that the use of variable window lengths in the SHG computation, coupled with the optimization of the global optimal solution via the Metaheuristic Bat Algorithm, can significantly contribute to the enhanced precision of subsurface structural parameter estimation. We hope our research will inspire others to explore this groundbreaking methodology and continue advancing the field of subsurface structure optimization.

An enhanced dung beetle optimizer with multiple strategies for robot path planning

In order to make up for the shortcomings of the original dung beetle optimization algorithm, such as low population diversity, insufficient of global exploration ability, being easy to fall into local optimization and unsatisfactory convergence accuracy, etc. An improved dung beetle optimization algorithm using hybrid multi- strategy is proposed. Firstly, the cubic chaotic mapping approach is used to initialize the population to improve the diversity, expand the search range of the solution space, and enhance the global optimization ability. Secondly, the cooperative search algorithm is utilized to strength communication between individual dung beetles and dung beetle groups in foraging stage to expand the search range of the solution space and enhance the global optimization ability. Thirdly, T-distribution mutation and differential evolutionary variation strategies are introduced to provide perturbation to enhance the diversity of the population and avoid falling into local optimization. Fourthly, the proposed algorithm(named as SSTDBO) is compared with other optimization algorithms, including GODBO, QHDBO, DBO, KOA, NOA, WOA and HHO, by 29 benchmark test functions in CEC2017. The results show that the proposed algorithm has stronger robustness and optimization ability, and algorithm's performance has substantially enhanced. Finally, the proposed algorithm is applied to solve the real-world robot path planning engineering cases, to demonstrate its effectiveness in dealing with real optimization engineering cases, which further verified how noteworthy the enhanced strategy's efficacy and the enhanced algorithm's superiority are in addressing real-world engineering cases.

Transformer-based decoder of melanoma classification using hand-crafted texture feature fusion and Gray Wolf Optimization algorithm

Melanoma is a type of skin cancer that poses significant health risks and requires early detection for effective treatment. This study proposing a novel approach that integrates a transformer-based model with hand-crafted texture features and Gray Wolf Optimization, aiming to enhance efficiency of melanoma classification. Preprocessing involves standardizing image dimensions and enhancing image quality through median filtering techniques. Texture features, including GLCM and LBP, are extracted to capture spatial patterns indicative of melanoma. The GWO algorithm is applied to select the most discriminative features. A transformer-based decoder is then employed for classification, leveraging attention mechanisms to capture contextual dependencies. The experimental validation on the HAM10000 dataset and ISIC2019 dataset showcases the effectiveness of the proposed methodology. The transformer-based model, integrated with hand-crafted texture features and guided by Gray Wolf Optimization, achieves outstanding results. The results showed that the proposed method performed well in melanoma detection tasks, achieving an accuracy and F1-score of 99.54% and 99.11% on the HAM10000 dataset, and an accuracy of 99.47%, and F1-score of 99.25% on the ISIC2019 dataset. • We use the concepts of LBP and GLCM to extract features from the skin lesion images. • The Gray Wolf Optimization (GWO) algorithm is employed for feature selection. • A decoder based on Transformers is utilized for melanoma classification.

D. Demand side management using optimization strategies for efficient electric vehicle load management in modern power grids

The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid's balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value.

Kohonen neural network and symbiotic-organism search algorithm for intrusion detection of network viruses

Introduction

The development of the Internet has made life much more convenient, but forms of network intrusion have become increasingly diversified and the threats to network security are becoming much more serious. Therefore, research into intrusion detection has become very important for network security.

Methods

In this paper, a clustering algorithm based on the symbiotic-organism search (SOS) algorithm and a Kohonen neural network is proposed.

Results

The clustering accuracy of the Kohonen neural network is improved by using the SOS algorithm to optimize the weights in the Kohonen neural network.

Discussion

Our approach was verified with the KDDCUP99 network intrusion data. The experimental results show that SOS-Kohonen can effectively detect intrusion. The detection rate was higher, and the false alarm rate was lower.

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into; GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.