Centroid mutation-based Search and Rescue optimization algorithm for feature selection and classification

Optimal design of stand-alone hybrid PV/wind/biomass/battery energy storage system in Abu-Monqar, Egypt

Incremental learning relies on the availability of ample training data for novel classes, a requirement that is often unfeasible in various application scenarios, particularly when new classes are rare groups that are pricey or challenging to attain. The main focus of incremental learning is on the tricky task of continuously learning to classify new classes in incoming data with no erasing knowledge of old classes. The research intends to develop a comparative analysis of optimization algorithms in training few-shot continual learning models to conquer catastrophic forgetting. The presented mechanism integrates various steps: pre-processing and classification. Images are initially pre-processed through contrast enhancement to elevate their quality. Pre-processed outputs are then classified by employing Continually Evolved Classifiers, generated to address a matter of catastrophic forgetting. Furthermore, to further enhance performance, Serial Exponential Sand Cat Swarm optimization algorithm (SE-SCSO) is employed and compared against ten other algorithms, containing Grey Wolf Optimization (GWO) algorithm, Moth flame optimization (MFO), cuckoo Search Optimization Algorithm (CSOA), Elephant Search Algorithm (ESA), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Cat Swarm Optimization (CSO), Fish Swarm Algorithm (FSA), Genetic Bee Colony (GBC) Algorithm, and Particle swarm optimization (PSO). From the experiment results, SE-SCSO had attained the maximum performance with an accuracy of 89.6%, specificity of 86%, precision of 83%, recall of 92.3% and f-measure of 87.4%.

Feature selection is the procedure of extracting the optimal subset of features from an elementary feature set, to reduce the dimensionality of the data. It is an important part of improving the classification accuracy of classification algorithms for big data. Hybrid metaheuristics is one of the most popular methods for dealing with optimization issues. This article proposes a novel feature selection technique called MetaSCA, derived from the standard sine cosine algorithm (SCA). Founded on the SCA, the golden sine section coefficient is added, to diminish the search area for feature selection. In addition, a multi-level adjustment factor strategy is adopted to obtain an equilibrium between exploration and exploitation. The performance of MetaSCA was assessed using the following evaluation indicators: average fitness, worst fitness, optimal fitness, classification accuracy, average proportion of optimal feature subsets, feature selection time, and standard deviation. The performance was measured on the UCI data set and then compared with three algorithms: the sine cosine algorithm (SCA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). It was demonstrated by the simulation data results that the MetaSCA technique had the best accuracy and optimal feature subset in feature selection on the UCI data sets, in most of the cases.

Nature is a great source of inspiration for solving complex problems in real-world. In this paper, a hybrid nature-inspired algorithm is proposed for feature selection problem. Traditionally, the real-world datasets contain all kinds of features informative as well as non-informative. These features not only increase computational complexity of the underlying algorithm but also deteriorate its performance. Hence, there an urgent need of feature selection method that select an informative subset of features from high dimensional without compromising the performance of the underlying algorithm. In this paper, we select an informative subset of features and perform cluster analysis by employing a cross breed approach of binary particle swarm optimization (BPSO) and sine cosine algorithm (SCA) named as hybrid binary particle swarm optimization and sine cosine algorithm (HBPSOSCA). Here, we employ a V-shaped transfer function to compute the likelihood of changing position for all particles. First, the effectiveness of the proposed method is tested on ten benchmark test functions. Second, the HBPSOSCA is used for data clustering problem on seven real-life datasets taken from the UCI machine learning store and gene expression model selector. The performance of proposed method is tested in comparison to original BPSO, modified BPSO with chaotic inertia weight (C-BPSO), binary moth flame optimization algorithm, binary dragonfly algorithm, binary whale optimization algorithm, SCA, and binary artificial bee colony algorithm. The conducted analysis demonstrates that the proposed method HBPSOSCA attain better performance in comparison to the competitive methods in most of the cases.

One of the most parameters and variables in every economics is the interest rate. Government officials and lawmakers change interest rates for various purposes: controlling liquidity, inflation, and prices, Economic growth and development, lending, etc. So, it is important to set the interest rate correctly. If you can predict the interest rate correctly, you can earn and gain profit by investing in various sectors. Moreover, the interest rate can impact other sectors through parallel markets such as the stock market, automobile, housing, etc. Interest rates are related to parallel markets. Thus, if you can forecast the interest rate, you can predict the parallel markets too. The main goal of this article, as it is clear from the title, is the prediction of interest rate using ANN and improving the network using some novel heuristic algorithms such as Moth Flame Optimization algorithm (MFO), Chimp Optimization Algorithm (CHOA), Time-varying Correlation Particle Swarm Optimization algorithm (TVAC-PSO), etc. we used 17 variables such as oil price, gold coin price, house price, etc. as input variables. We used GA and a new algorithm called Grey Wolf Optimization, Particle Swarm Optimization (GWO-PSO) algorithm as a feature selection and choosing the best variables. We have used eight loss functions such as MSE, RMSE, MAE, etc. too. Finally, we have compared different algorithms due to their estimation errors. The main contribution of this paper is that, first, this is for the first time which these novel metaheuristic algorithms have been used for the prediction of interest rate. Second, it has tried to use different graphs and tables for better understanding and totally a comprehensive research paper. The results show that Whale Optimization Algorithm (WOA) performed better than other methods along with less error.

Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)

Breast cancer is the second most frequent malignant tumor in the world. Early findings of breast cancer can significantly improve treatment effectiveness. Manual methods of breast cancer diagnosis are prone to human fault and inaccuracy, and they take time. A computer‐aided diagnosis can assist radiologists in making better choices by overcoming the disadvantages of manual methods. One of the significant steps in the breast cancer diagnosis process is feature selection. In recent decades, many studies have proposed numerous hybrid optimization methods to select the optimal features in the breast cancer detection system. However, many hybrid optimization algorithms are trapped in local optima and have slow convergence speed. Thus, it reduces the classification accuracy. For resolving these issues, this work proposes a hybrid optimization algorithm that combines the grasshopper optimization algorithm and the crow search algorithm for feature selection and classification of the breast mass with multilayer perceptron. The simulation is experimented with using MATLAB 2019a. The efficacy of the proposed hybrid grasshopper optimization‐crow search algorithm with multilayer perceptron system is compared to multilayer perceptron based algorithms of enhanced and adaptive genetic algorithm, teaching learning‐based whale optimization algorithm, butterfly optimization algorithm, whale optimization algorithm, and grasshopper optimization algorithm. From the results obtained, the proposed grasshopper optimization‐crow search algorithm with the multilayer perceptron method outperforms the comparative models in terms of classification accuracy (97.1%), sensitivity (98%), and specificity (95.4%) for the mammographic image analysis society dataset.

Feature selection is a very important direction for network intrusion detection. However, current feature selection technology of network intrusion detection has the problems of low detection rate and low accuracy due to feature redundancy. An improved Butterfly Optimization Algorithm combined with Black Widow Optimization (BWO-BOA) is proposed in this paper, which introduces a dynamic adaptive search strategy in the global search phase of the Butterfly Optimization Algorithm (BOA), uses the movement search process of Black Widow Optimization (BWO) algorithm as the local search, and at the same time, in order to overcome the improved butterfly optimization algorithm easily falling into a local optimum in local search phase, takes advantage of the small probability mutation strategy to filter out the redundant features. This paper then tries to apply the proposed BWO-BOA algorithm to feature selection of network intrusion detection. In order to verify the performance of the proposed BWO-BOA algorithm, the UNSW-NB15 dataset is selected for binary classification and multi-classification simulation experiments, and the feature selection models of BWO-BOA algorithm, BOA algorithm, BWO algorithm, Particle Swarm Optimization, Salp Swarm Algorithm, Whale Optimization Algorithm and improved Butterfly Optimization Algorithm are compared for validation. The experimental results show that the proposed BWO-BOA algorithm can enhance the performance of the feature selection model in network intrusion detection and significantly boost the reduction of feature dimensions.

An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection

In this paper, a feature selection system is introduced applies the whale optimization algorithm (WOA). WOA is a recently introduced meta-heuristic optimization algorithm that mimics the natural behavior of the humpback whales. The proposed model applies the wrapper-based method to reach the optimal subset of features. This technique was applied to find the best feature subset that maximizes the accuracy of the classification while preserving the minimum number of features. The proposed model is compared with the particle swarm optimization (PSO) and genetic algorithm (GA) using a number of assessment indicators on 16 different data-sets from UCI data repository. The results demonstrate the advantage of the introduced algorithm compared to the other optimizers.

ABSTRACTThree‐dimensional path planning for UAV in complex terrains and obstacle‐limited areas is one of the major challenges faced during mission execution, requiring a simple yet effective algorithm. To solve such problems, an improved Sand Cat Swarm Optimization (SCSO) is proposed, addressing the issue where traditional SCSO is prone to getting stuck in local optima. In this improved approach, a nonlinear adjustment mechanism based on a dynamic factor k is introduced to better balance the exploration and exploitation phases. Additionally, the predatory attack strategy of Harris's Hawks was introduced to improve the position update formula during the exploration phase of the SCSO, thus enhancing the algorithm's convergence speed. A new variant of the SCSO, named HKSCSO, is proposed and applied to UAV path planning. Cost functions are introduced to evaluate path length, flight altitude, and angle comprehensively. HKSCSO's performance was tested in three 3D urban environments, showing faster convergence and safer paths compared to SCSO, Harris's Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA), Parrot Optimizer (PO), and Mantis Search Algorithm (MSA). These results indicate HKSCSO's potential as an effective solution for UAV three‐dimensional path planning.

Swarm optimization algorithms are widely used for feature selection before data mining and machine learning applications. The metaheuristic nature-inspired feature selection approaches are used for single-objective optimization task, though the major problem is their frequent premature convergence, leading to weak contribution to data mining. In this paper, we propose a novel feature selection algorithm named Reinforced Swarm Optimization (RSO) leveraging some of the existing problems in feature selection. This algorithm embeds the widely used Bee Swarm Optimization (BSO) algorithm along with Reinforcement Learning (RL) to maximize the reward of a superior search agent and punish the inferior ones. This hybrid optimization algorithm is more adaptive and robust with a good balance between exploitation and exploration of the search space. The proposed method is evaluated on 25 widely known UCI dataset containing a perfect blend of balanced and imbalanced data. The obtained results are compared with several other popular and recent feature selection algorithms with similar classifier configuration. The experimental outcome shows that our proposed model outperforms BSO in 22 out of 25 instances (88%). Moreover, experimental results also show that RSO performs the best among all the methods compared in this paper in 19 out of 25 cases (76%), establishing the superiority of our proposed method.

Conversion of DC to AC is widely used as an essential need for the power system to harness renewable power source in this era. Multilevel inverter is a mechanism which capable of harnessing these renewable energies. Unfortunately, harmonics occur in the system due to distortions and causing much unwanted problems at the power output. Therefore, the elimination of harmonics and reduce Total Harmonics Distortion (THD) are needed to maintain good power output. Application of Selective Harmonics Elimination Pulse Width Modulation (SHEPWM) can be used in the multilevel inverter to solve the problem. In this paper, optimization algorithms such as Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) and Sine Cosine Algorithm (SCA) were applied for finding suitable switching angle for SHEPWM. The calculated switching angle was applied into seven-level five-phase multilevel inverter for various modulation indexes. In addition, the simulation of the multilevel inverter also done using PSIM software. The results show that the selective lower order harmonics, 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> and 7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> are completely eliminated for all three algorithms. In addition, the time taken for newly discovered algorithms to find switching angles for seven-level five-phase inverter is faster compared to PSO.

Support Vector Machines (SVMs) excel in classification but require careful hyperparameter tuning and feature selection to handle high-dimensional data. We propose MDSH-SVM, a novel hybrid algorithm integrating Hunger Games Search (HGS) and Slime Mould Algorithm (SMA) for simultaneous feature selection and SVM parameter optimization. Extensive experiments on publicly available high-dimensional datasets demonstrate that MDSH-SVM achieves statistically significant improvements over GA-SVM, PSO-SVM, and other state-of-the-art heuristics in accuracy, model compactness, and runtime efficiency. Detailed sensitivity analyses and ablation studies further validate the robustness and adaptability of the proposed method across varying data characteristics.

Flexible AC transmission systems (FACTS) and optimal power-flow (OPF) solutions play an important role in solving power operation problems. The volatile nature of the power generation profiles from renewable energy sources, solar and wind systems, and determining the optimal locations and sizes of FACTS devices increase the complexity of the OPF problems in modern power network models, such as transmission power loss, power generation operation cost and voltage deviation, as a highly nonlinear-nonconvex optimization problem. Therefore, this article introduces and employs four new independent, reliable and efficient optimization algorithms inspired by nature and biological nature, namely: Slime Mould Algorithm (SMA), Artificial Ecosystem-based Optimization (AEO), Marine Predators Algorithm (MPA) and Jellyfish Search (JS), for solving both multi- and single-OPF objective problems for a power network incorporating FACTS and stochastic renewable energy sources. The proposed new metaheuristic optimization techniques are compared to the common and available alternatives in the literature, Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO) and Grey Wolf Optimizer (GWO), using IEEE 30-bus test system. To consider and address the challenges of the OPF in modern power network models, the proposed optimization techniques tested under different operation cases such as an increasing in the load, with and without FCTAS and renewable energy sources, different renewable energy sources locations on the network. The result showed that the MPA, SMA, JS and AEO algorithms are more effective solvers for the OPF problems cases compared to the PSO, GWO and MFO algorithms. For example, the AEO obtained 0.0844 p.u. in case of minimizing the voltage deviation compared to 0.1155 p.u. for PSO, which means that the AEO algorithm improved the voltage deviation term by 27% compared to the PSO algorithm.