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Traffic accidents are the leading cause of death and injury in many developed nations. Anyone utilizing the road can meet an accident at any moment of time. The type of collision also plays a role in determining who is accountable for the accident. The biggest advantage of classifying collisions in road accidents can pave a way for safer roads and reduced accident rates. A novel approach is proposed for classifying the type of collisions that might take place between vehicles and near by pedestrians, obstacles etc. on roads. A total of six hybrid classifiers are introduced in this article namely ` ` XGBoost c l a s s i f i e r u s i n g I S S A ′ ′ , ` ` XGBoost c l a s s i f i e r u s i n g E S S A ′ ′ , ` ` XGBoost c l a s s i f i e r u s i n g TVBSSA ′ ′ , ` ` CatBoost c l a s s i f i e r u s i n g I S S A ′ ′ , ` ` CatBoost c l a s s i f i e r u s i n g E S S A ′ ′ , and ` ` CatBoost c l a s s i f i e r u s i n g TVBSSA ′ ′ , The dataset considered in this article is the SWITRS dataset for classifying ` ` T y p e _ o f _ C o l l i s i o n ′ ′ . A total of 103000 accidents are considered when determining the ` ` T y p e _ o f _ C o l l i s i o n ′ ′ . It classifies the type of collisions using XGBoost algorithm, CatBoost Algorithm and three Nature Inspired Algorithms (NIA’s) have been used at the feature selection stage. The NIA’s considered for feature selection includes Improved Salp Swarm Algorithm (ISSA), Enhanced Salp Swarm Algorithm (ESSA), and Time-Varying Binary Salp Swarm Algorithm (TVBSSA). It is concluded that XGBoost c l a s s i f i e r u s i n g I S S A presents good stability with fewer hyper-parameters and the highest accuracy under different levels of training data volume. The value of Accuracy, Mean Square Error, and ROC-Auc in XGBoost using ISSA is 90.40, 0.1624 and 97.75, respectively. Moreover, the confusion matrix and evaluation metrics of XGBoost c l a s s i f i e r u s i n g I S S A performed better than the other two approaches. The findings of this study would be helpful in classifying the “type of collision”. These findings are highly significant in smart city projects to effectively establish timely proactive strategies and improve road traffic safety.

The detection of disease development in plants becomes very crucial because of its adverse effect on the quality and productivity of agriculture. The automatic disease detection in plants using image processing and machine learning is beneficial due to its fast computing and practicability for continuous monitoring of a large farm. This paper presents an automatic disease detection system using image segmentation, feature extraction, optimization, and classification algorithms. This paper proposes a memetic salp swarm optimization algorithm (MSSOA), which is transformed into binary MSSOA to search for the optimal number of features that give the best classification accuracy. The performance of the proposed algorithm for feature selection is compared with five metaheuristic feature selection (BSSA, BPSO, BMFO, BCOA, IBHHO) algorithms against the UCI benchmark datasets. The obtained results indicate the proposed algorithm outperforms the other algorithms in obtaining good classification accuracy and reducing the feature size. The proposed algorithm is implemented for automatic disease detection of maize, rice, and grape plant and achieved a classification accuracy of 90.6%, 67.9%, and 91.6% and best classification accuracy of 93.6%, 79.1%, and 95%, respectively.

In this paper, improved salp swarm algorithm is proposed. The algorithm integrates (1) random opposition-based learning (2) multiple leadership and (3) simulated annealing in swarm intelligence-based metaheuristic salp swarm algorithm. This integration increases the exploration and exploitation of the original salp swarm algorithm. Hence, the effectiveness of the proposed algorithm is better for complex multi-modal problems. The algorithm is tested on several standard numerical benchmark functions and CEC-2015 benchmarks. Results are compared with some well-known metaheuristics. The results represent the merit of the proposed algorithm with respect to other algorithms. The improved salp swarm algorithm is applied for feed-forward neural network training. Performance is compared with other metaheuristic-based feedforward neural network trainers for different data sets. The results show the efficiency and effectiveness of proposed algorithm in solving complex multi-modal problems.

In power networks, low‐frequency oscillations—particularly inter‐area oscillations—pose a serious threat to system stability because of disruptions like short‐circuit faults and the increasing use of renewable energy sources like wind farms. To lessen the negative impacts of time delays when sending distant signals, this study presents a novel approach for the creation and optimization of a wide‐area damping controller (WADC). To accomplish smooth integration with the power system stabilizer (PSS), the Salp Swarm Algorithm (SSA) is used to precisely alter the WADC's parameters. The study also looks into how time delays, changes in wind speed, and load variations affect the effectiveness of the controller. A typical six‐machine test system with 200 MW of wind power is used for simulations under various operating conditions and disruptions. The results show that the suggested controller counteracts the detrimental effects of time delays, considerably lowers inter‐area oscillations, and preserves overall system stability. This study emphasizes the significance of wide‐area signal usage and robust control frameworks while providing a workable method for enhancing the dynamic performance of power systems with significant penetration of renewable energy.

Salp Swarm Algorithm (SSA) is one of the most recently proposed algorithms driven by the simulation behavior of salps. However, similar to most of the meta-heuristic algorithms, it suffered from stagnation in local optima and low convergence rate. Recently, chaos theory has been successfully applied to solve these problems. In this paper, a novel hybrid solution based on SSA and chaos theory is proposed. The proposed Chaotic Salp Swarm Algorithm (CSSA) is applied on 14 unimodal and multimodal benchmark optimization problems and 20 benchmark datasets. Ten different chaotic maps are employed to enhance the convergence rate and resulting precision. Simulation results showed that the proposed CSSA is a promising algorithm. Also, the results reveal the capability of CSSA in finding an optimal feature subset, which maximizes the classification accuracy, while minimizing the number of selected features. Moreover, the results showed that logistic chaotic map is the optimal map of the used ten, which can significantly boost the performance of original SSA.

The increasing prevalence of network connections is driving a continuous surge in the requirement for network security and safeguarding against cyberattacks. This has triggered the need to develop and implement intrusion detection systems (IDS), one of the key components of network perimeter aimed at thwarting and alleviating the issues presented by network invaders. Over time, intrusion detection systems have been instrumental in identifying network breaches and deviations. Several researchers have recommended the implementation of machine learning approaches in IDSs to counteract the menace posed by network intruders. Nevertheless, most previously recommended IDSs exhibit a notable false alarm rate. To mitigate this challenge, exploring deep learning methodologies emerges as a viable solution, leveraging their demonstrated efficacy across various domains. Hence, this article proposes an optimized Long Short-Term Memory (LSTM) for identifying anomalies in network traffic. The presented model uses three optimization methods, i.e., Particle Swarm Optimization (PSO), JAYA, and Salp Swarm Algorithm (SSA), to optimize the hyperparameters of LSTM. In this study, NSL KDD, CICIDS, and BoT-IoT datasets are taken into consideration. To evaluate the efficacy of the proposed model, several indicators of performance like Accuracy, Precision, Recall, F-score, True Positive Rate (TPR), False Positive Rate (FPR), and Receiver Operating Characteristic curve (ROC) have been chosen. A comparative analysis of PSO-LSTMIDS, JAYA-LSTMIDS, and SSA-LSTMIDS is conducted. The simulation results demonstrate that SSA-LSTMIDS surpasses all the models examined in this study across all three datasets.

We live in a period when smart devices gather a large amount of data from a variety of sensors and it is often the case that decisions are taken based on them in a more or less autonomous manner. Still, many of the inputs do not prove to be essential in the decision-making process; hence, it is of utmost importance to find the means of eliminating the noise and concentrating on the most influential attributes. In this sense, we put forward a method based on the swarm intelligence paradigm for extracting the most important features from several datasets. The thematic of this paper is a novel implementation of an algorithm from the swarm intelligence branch of the machine learning domain for improving feature selection. The combination of machine learning with the metaheuristic approaches has recently created a new branch of artificial intelligence called learnheuristics. This approach benefits both from the capability of feature selection to find the solutions that most impact on accuracy and performance, as well as the well known characteristic of swarm intelligence algorithms to efficiently comb through a large search space of solutions. The latter is used as a wrapper method in feature selection and the improvements are significant. In this paper, a modified version of the salp swarm algorithm for feature selection is proposed. This solution is verified by 21 datasets with the classification model of K-nearest neighborhoods. Furthermore, the performance of the algorithm is compared to the best algorithms with the same test setup resulting in better number of features and classification accuracy for the proposed solution. Therefore, the proposed method tackles feature selection and demonstrates its success with many benchmark datasets.

Feature selection is a required preprocess stage in most of the data mining tasks. This paper presents an improved Harris hawks optimization (HHO) to find high-quality solutions for global optimization and feature selection tasks. This method is an efficient optimizer inspired by the behaviors of Harris' hawks, which try to catch the rabbits. In some cases, the original version tends to stagnate to the local optimum solutions. Hence, a novel HHO called IHHO is proposed by embedding the salp swarm algorithm (SSA) into the original HHO to improve the search ability of the optimizer and expand the application fields. The update stage in the HHO optimizer, which is performed to update each hawk, is divided into three phases: adjusting population based on SSA to generate SSA-based population, generating hybrid individuals according to SSA-based individual and HHO-based individual, and updating search agent in the light of greedy selection and HHO’s mechanisms. A large group of experiments on many functions is carried out to investigate the efficacy of the proposed optimizer. Based on the overall results, the proposed IHHO can provide a faster convergence speed and maintain a better balance between exploration and exploitation. Moreover, according to the proposed continuous IHHO, a more stable binary IHHO is also constructed as a wrapper-based feature selection (FS) approach. We compare the resulting binary IHHO with other FS methods using well-known benchmark datasets provided by UCI. The experimental results reveal that the proposed IHHO has better accuracy rates over other compared wrapper FS methods. Overall research and analysis confirm the improvement in IHHO because of the suitable exploration capability of SSA.

Feature selection (FS) is a machine learning process commonly used to reduce the high dimensionality problems of datasets. This task permits to extract the most representative information of high sized pools of data, reducing the computational effort in other tasks as classification. This article presents a hybrid optimization method for the FS problem; it combines the slap swarm algorithm (SSA) with the particle swarm optimization. The hybridization between both approaches creates an algorithm called SSAPSO, in which the efficacy of the exploration and the exploitation steps is improved. To verify the performance of the proposed algorithm, it is tested over two experimental series, in the first one, it is compared with other similar approaches using benchmark functions. Meanwhile, in the second set of experiments, the SSAPSO is used to determine the best set of features using different UCI datasets. Where the redundant or the confusing features are removed from the original dataset while keeping or yielding a better accuracy. The experimental results provide the evidence of the enhancement in the SSAPSO regarding the performance and the accuracy without affecting the computational effort.

The optimal reactive power dispatch (ORPD) problem represents a fundamental concern in the efficient and reliable operation of power systems, based on the proper coordination of numerous devices. Therefore, the ORPD calculation is an elaborate nonlinear optimization problem that requires highly performing computational algorithms to identify the optimal solution. In this paper, the potential of metaheuristic methods is explored for solving complex optimization problems specific to power systems. In this regard, an improved salp swarm algorithm is proposed to solve the ORPD problem for the IEEE-14 and IEEE-30 bus systems, by approaching the reactive power planning as both a single- and a multi- objective problem and aiming at minimizing the real power losses and the bus voltage deviations. Multiple comparison studies are conducted based on the obtained results to assess the proposed approach performance with respect to other state-of-the-art techniques. In all cases, the results demonstrate the potential of the developed method and reflect its effectiveness in solving challenging problems.