The BAT Algorithm is an example of meta-heuristic Algorithms from the collective swarm intelligence, which is based on the echolocation behavior of BATs. This Algorithm preserves the diversity of the solution by using a frequency tuning method that can quickly and efficiently shift from exploration to exploitation. Therefore, when a fast and accurate solution is needed, this Algorithm becomes an efficient optimizer for any application. Although the BAT Algorithm has many practical benefits, it also has some disadvantages. One of these disadvantages that reduces its efficiency is being trapped in the local optimum. To solve the mentioned problem in this research, the position and speed of the initial population is updated in three ways with different formulas, this makes the final answer of the problem not trapped in the local optimum and diversity occurs in the population. In this article, the performance of the improved BAT Algorithm on 11 sample objective functions has been investigated and compared with other similar Algorithms, and finally the results show the superiority and accuracy of this Algorithm compared to similar samples.

Clustering is a method of data analysis and one of the important methods in data mining that has been considered by researchers in many fields as well as in many disciplines. In this paper, we propose combining WOA with BA for data clustering. To assess the efficiency of the proposed method, it has been applied in data clustering. In the proposed method, first, by examining BA thoroughly, the weaknesses of this Algorithm in exploitation and exploration are identified. The proposed method focuses on improving BA exploitation. Therefore, in the proposed method, instead of the random selection step, one solution is selected from the best solutions, and some of the dimensions of the position vector in BA are replaced We change some of the best solutions with the step of reducing the encircled mechanism and updating the WOA spiral, and finally, after selecting the best exploitation between the two stages of WOA exploitation and BA exploitation, the desired changes are applied on solutions. We evaluate the performance of the proposed method in comparison with other meta-heuristic Algorithms in the data clustering discussion using six datasets. The results of these experiments show that the proposed method is statistically much better than the standard BA and also the proposed method is better than the WOA. Overall, the proposed method was more robust and better than the Harmony Search Algorithm (HAS), Artificial Bee Colony (ABC), WOA and BA.

In this article, an improvement is proposed for BAT Algorithm and it is utilized for size optimization of skeletal structures consisting of truss and frame structures. Various optimization problems are implemented to demonstrate the ability of the enhanced BAT Algorithm. These numerical examples are along with different constraints and loading conditions such as stress, displacement and frequency constraints, static and time history dynamic loadings. Furthermore, these optimization problems are in two form of discrete and continuous. Results show the suitability and efficiency of the present Algorithm for optimal design of skeletal structures.

One of the challenges today is capturing fake images. One type of image forgery is a copy-move forgery. In this method, a part of the image is copy and placed at the most similar point. Due to the existing Algorithms and processing software, it is not easy to detect forgery areas and has created challenges in various applications. Based on the BAT Algorithm, the proposed method has tried to help detect fake images by finding forgery areas. The proposed method includes a simple image segmentation and detection of forgery areas with the help of the BAT Algorithm with Mutation. According to the proposed Algorithm, the image is first grayed out, then divided into 100 pieces. The optimal BAT Algorithm randomly selects some component image and performs a similarity search. The mutation operator is used to avoid getting stuck in the local optimization. The proposed Algorithm does not get stuck in the local optimization with the help of the mutation operator and can find forgery areas with a precision of about 81.39% for the IMD dataset and about 81.04% for the MICC-F600 dataset.

This paper presents a framework for the optimal design of hybrid energy systems with a BAT search optimization Algorithm. Wind turbines, solar panels, and combined heat and power (CHP) systems are considered power generators. The proposed framework can exchange power with the upstream network, taking into account the limitations of power exchange, the cost of installing wind turbines, solar panels, CHP systems, BATtery storage, and converters in the form of investment costs plus maintenance costs, CHP system fuel costs, expected load removal costs, and power exchange costs as target functions. Also, a complete set of constraints related to the hybrid energy system is included in the problem framework. Uncertainties related to the production capacity of wind turbines and solar panels along with the uncertainty of consumer power demand using the two-point estimation method are also considered in the proposed framework. The BAT search Algorithm is used to find the optimal solution to the optimization problem. The results of the proposed framework in the form of different scenarios are compared with the results obtained by the genetic Algorithm and particle swarm, which indicates its efficiency and optimal performance in designing hybrid energy systems.

One of the most important steps to design an engineering system is reliability allocation. Often, redundancy is used to achieve a highly reliable system. The redundancy allocation problem (RAP) is increasingly becoming an important tool in the initial stages of or prior to the plan, design, and control of systems. The multi-level redundancy allocation problem (MLRAP) is an extension of the traditional RAP such that all available items for redundancy (system, module, and component) can be simultaneously chosen. Although RAP has been considered by several researchers, MLRAP attracts only a little attention. Ordinarily, reliability uncertainty is ignored too. In this paper, this subject is studied and a new method to solve MLRAP is developed. The total cost is considered the most important constraint. A new meta-heuristic optimization Algorithm, called Modified BAT Algorithm (MBA), to solve the constrained optimization problem (MLRAP) is proposed. This method is based on the BAT behavior to detect a prey. To demonstrate this method's capability, MLRAP for a system is described. The results are comprised with HGA, MA, and two-dimensional arrays encoding and a hybrid genetic Algorithm (TDA-HGA). For this system, optimal results are the same as TDA-HGA and better than HGA and MA in all cases. Also, the reliability uncertainty and its influence on reliability allocation are studied. The optimal result is changed when uncertainty is considered. The proposed method is a simple and powerful tool to determine the optimal multi-level redundancy allocation and reliability uncertainty modeling.

Substitution-boxes (S-boxes) are very important nonlinear components used for achieving strong confusion for enhancing cryptographic security in most of the block ciphers. Designing cryptographically strong S-boxes has been a major research domain for the designers of symmetric crypto systems. In the proposed research work, BAT Algorithm based swarm technique is proposed to design strong S-boxes. Cryptographic strong S-boxes are obtained by the developed swarm technique. Authors analyze cryptographic strength of the obtained S-box by evaluating properties like Bijectivity, Nonlinearity, Bit-Independence Criterion, Linear Probability and Differential Uniformity. The obtained performance parameters for the designed new S-box by the swarm technique are compared with some recently reported S-boxes in the literature. The designed S-box has good cryptographic strength. The designed S-box has good cryptographic strength like nonlinearity = 110. 75 and average Strict Avalanche Criterion (SAC) value = 0. 506. For the constructed S-box, most of the Differential uniformity components are 4 and shows uniform distribution approximately. The proposed new S-box is also free from the fixed points.

The main aim of the present study is to achieve optimum design of reinforced concrete (RC) plane moment frames using BAT Algorithm (BA) which is a newly developed meta-heuristic optimization Algorithm based on the echolocation behaviour of BATs. The objective function is the total cost of the frame and the design constraints are checked during the optimization process based on ACI 318-08 code. Design variables are the cross-sectional assignments of the structural members and are selected from a data set containing a finite number of sectional properties of beams and columns in a practical range. Three design examples including four, eight and twelve story RC frames are presented and the results are compared with those of other Algorithms. The numerical results demonstrate the superiority of the BA to the other meta-heuristic Algorithms in terms of the frame optimal cost and the convergence rate.