Figure 1 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
Optimization techniques are stimulated by Swarm Intelligence wherever the target is to get a decent competency of a problem. The knowledge of the behavior of animals or insects has a variety of models in Swarm Intelligence. Swarm Intelligence has become a potential technique for evolving many robust optimization problems. Researchers have developed...
Contexts in source publication
Context 1
... are simple with limited capabilities, but by interacting with the other agents of their own kind they achieve the task necessary for their survival like the ants' forage for the food (Figure 1), bees communicate with others by waggle dance (Figure 2), flock of birds flying together (Figure 3). The agents follow ...
Citations
... The bat algorithm has proven to outperform other optimization methods in feature selection. This algorithm is inspired by bat echolocation behavior [26]. The bat algorithm employs intelligent and efficient search strategies in exploring the search space [27]. ...
Cardiovascular disease stands as one of the primary contributors to global mortality, with the World Health Organization (WHO) reporting approximately 17.9 million deaths annually. Swift and accurate diagnosis of heart attacks is crucial to ensure timely and specialized intervention for patients afflicted by this ailment. A machine learning algorithm that can be employed for addressing such issues is the Random Forest algorithm. However, the efficacy of the model is significantly influenced by the features selected during the training phase. To mitigate this, the Binary Bat Algorithm (BBA) with greedy crossover has been utilized to enhance feature selection within the model. This approach is particularly adept at preventing convergence issues often associated with local minima. The optimal parameters for BBA with greedy crossover are determined to be , , , and . With these parameters, the proposed algorithm identifies the most relevant features, including age, gender, cp, chol, thalach, oldpeak, slope, and ca, achieving an accuracy of 94.19% on the training data and 91.8% on the test data. Furthermore, the precision and recall values for both classes range from 0.87 to 0.96, contributing to an approximate -score of 0.92. The proposed method has increased its -score by 0.05 if compared with the regular Random Forest model. These results underscore the effectiveness of the proposed algorithm in providing accurate and reliable predictions for heart disease diagnosis. As such, this model makes diagnosing heart attack more convenient and effective because it does not require too much medical features or patient data. Hopefully, the results of this research help medical practitioners make better and timely decisions in the diagnosis and treatment of heart attacks, as well as assist in planning more effective public health programs for heart attack prevention.
... Recently, swarm intelligence methods have aroused universal interest due to their strong robustness, simplicity, self-organization and extensibility (Li et al., 2011(Li et al., , 2015Islam et al., 2018;Wang et al., 2020a;Mitra et al., 2022). Consequently, more and more classification methods are developed based on swarm intelligence. ...
Background
Recently, researchers have been attracted in identifying the crucial genes related to cancer, which plays important role in cancer diagnosis and treatment. However, in performing the cancer molecular subtype classification task from cancer gene expression data, it is challenging to obtain those significant genes due to the high dimensionality and high noise of data. Moreover, the existing methods always suffer from some issues such as premature convergence.
Methods
To address those problems, we propose a new ant colony optimization (ACO) algorithm called DACO to classify the cancer gene expression datasets, identifying the essential genes of different diseases. In DACO, first, we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed; then, a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution; finally, the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally. To demonstrate the performance of the proposed algorithm in classification, different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets.
Results
The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets. It can be used to address the classification problem effectively. Moreover, a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses.
Conclusion
The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.
... The cost function represents the network's prediction error. The bat algorithm's ultimate solution produces a trained network, as seen in Figure 1 [26,27]. ...
The article investigates the temperature prediction in rectangular timber cross-sections exposed to fire. Timber density, exposure time, and the point coordinates within the cross-section are treated as inputs to determine the temperatures. A total of 54,776 samples of wood cross-sections with a variety of characteristics were considered in this study. Of the sample data, 70% was dedicated to training the networks, while the remaining 30% was used for testing the networks. Feed-forward networks with various topologies were employed to examine the temperatures of timber exposed to fire for more than 1500 s. The weight of the artificial neural network was optimized using bat and genetic algorithms. The results conclude that both algorithms are efficient and accurate tools for determining the temperatures, with the bat algorithm being marginally superior in accuracy than the genetic algorithm.
... Based on simple individuals and rules, the swarm intelligence optimization algorithm has stronger robustness, stability, and adaptability. Swarm intelligence methods have been widely used in image processing, path planning, vehicle scheduling, fault diagnosis, and other fields [17][18][19][20][21][22][23]. Typical swarm intelligence optimization algorithms include the particle swarm optimization algorithm (PSO) [24], artificial bee colony algorithm (ABC) [25], gravitational search algorithm (GSA) [26], differential evolution algorithm (DE) [27], among others. ...
... convergence factor a has a great influence on parameter A, which is the adjustment of the algorithm's global search ability and local dev the original algorithm, as the number of iterations increases, the con creases linearly from 2 to 0. This linear change in the convergence well the global exploration and local development capabilities of the to make the algorithm maintain the diversity of the population and j optimal solution in time, a nonlinearly changing convergence factor Equation (17). Figure 3 shows the change in the convergence factor a as the n increases. ...
... Step 5. Update a according to Equation (17), update k according to Equation (18), update A and C according to Equations (4) and (20). ...
The charging station location model is a nonlinear programming model with complex constraints. In order to solve the problems of weak search ability and low solution accuracy of the whale optimization algorithm (WOA) in solving location models or high-dimensional problems, this paper proposes an improved whale optimization algorithm (IWOA) based on hybrid strategies. Chaos mapping and reverse learning mechanism are introduced in the original algorithm, and the change mode of convergence factor and probability threshold is improved. Through optimization experiments on 18 benchmark functions, the test results show that IWOA has the best solution ability. Finally, IWOA is used to solve a site selection optimization model aiming at the minimum comprehensive cost. The results show that the proposed algorithm and model can effectively reduce the comprehensive cost of site selection. This provides a necessary decision-making reference for the scientific site selection for electric vehicle charging stations.
... Nevertheless, an adaptation was applied to the technique's formulation to provide an algorithm suitable for structural optimization problems. The flowchart in Fig. 8 depicts the necessary steps in the application of the proposed Bat algorithm [53]. ...
While design codes provide guidelines to prevent brittle punching shear failures in flat reinforced concrete (RC) slabs, they are associated with high inaccuracy. This study scrutinizes existing design provisions, highlighting its features and limitations. Sensitivity analysis is then used to identify the influential mechanical and geometric parameters. Subsequently, an artificial neural network coupled with a metaheuristic Bat algorithm (Bat-ANN) is used to develop a hybrid model for estimating punching shear strength. Several statistical metrics revealed that the Bat-ANN model achieved superior predictive accuracy. The novel hybrid model was deployed to assess the influence of key parameters affecting punching shear strength, including the slab effective depth, concrete strength, reinforcement ratio, reinforcement yield strength, and width of the square loaded area. The analysis identified the importance of the flexural reinforcement, which is not typically considered in estimating punching shear strength. Subsequently, using the supervised machine learning method through the EUREQA software, a new regression expression was proposed to estimate the punching shear resistance of flat slabs. This hybrid computational intelligence model could be integrated in future automated design platforms of RC structures.
... The bat algorithm [40] is a meta-heuristic based algorithm built on the ecological behavior of bats in finding their prey [28]. The bat algorithm flow chart is considered from an earlier study [42]. Initial bat population is generated and the initial position is given by x i , velocity v i , pulse rate (r i ), loudness (A i ) and frequency f i for each bat. ...
The predominant role of an active suspension system in Heavy Duty Vehicles (HDV) is to minimize vibrations caused by road disturbances. In this study, linear two degrees of freedom HDV model is simulated in a MATLAB/Simulink environment. The conventional Proportional Integral and Derivative (PID) controller has little effect on vibration suppression. To reduce the effect of vibration in the HDV, the PID controller’s output parameters are optimized by bat and Grey Wolf optimization techniques. The proposed optimization techniques have the potential to enrich the ride comfort of the passenger when the HDV is travelling over different types of road surfaces such as step, sinusoidal and ISO 8608 random road profiles. The ISO 2631 standard is used to examine the health criterion and ride comfort of the passengers. Further, comparative time domain and power spectrum density analyses are carried out for the PID controller and proposed optimization techniques. The simulation results confirm that the proposed bat tuned PID is more efficient in all aspects and significantly improves the ride comfort for the three different road profiles.
... Here, IAASA selects the initial subsets and then applies the crossover fitness selection (CFS) formulation for finalizing the exact features which are useful for identifying the four types of attacks. Finally, the hidden semi-Markov model (HSMM) [19] and SVM [20] are used for classification for selected features. These classification tasks are carried out using the existing classifier to test the performance of the proposed feature selection algorithms. ...
This review paper provides the reader with an overview of some of the many resource scheduling algorithms. The paper also describes the characteristics of these algorithms and highlights their strengths and weaknesses. The main focus is on comparing and evaluating different resource scheduling algorithms so that one can incorporate them as required. The paper also discusses potential directions for future study in the field of resource scheduling in virtual environments. A conclusion is drawn upon careful analysis, comparison, and assessment of various algorithms and their applicability for use in practical applications.
