Swarm Bat Research Articles

Optimizing Wireless Sensor Network Localization: Hybrid Natural Inspired with Bat Swarm Algorithm

In wireless sensor networks, the data received from sensor nodes is processed and communicated to the next node or cluster head. Sensed data are meaningful only when location information is embedded with the data. Therefore, the sensor should be able to estimate its location information and embed the location information with the sensed data. This research work proposes three hybrid nature-inspired localization optimization algorithms for location estimation of sensor nodes, namely the Hybrid genetic –Bat Localization Algorithm (GA-BAT LA), Hybrid Bat- Particle Swarm Optimization Localization Algorithm (BAT-PSO LA), and Hybrid genetic -Bat- Particle Swarm Optimization Localization Algorithm (GA-BAT-PSO LA). The localization algorithms are developed, implemented, and compared to ensure the accuracy of the estimation of location information. A fixed number of location-known anchor nodes with a variable location-unknown sensor node are considered. The algorithms are compared for a minimum number of iterations, average number of iterations, standard deviation, maximum number of iterations, time complexity, and accuracy to estimate the location of the sensor nodes.

Distributed chaotic bat algorithm for sensor fault diagnosis in AHUs based on a decentralized structure

The effective operation of sensors in heating, ventilation and air conditioning (HVAC) systems to promote high-efficiency, energy-saving and low-risk intelligent buildings. However, most existing methods are either based on a centralized architecture or employ only pure digital simulation platforms for HVAC sensor fault diagnosis and verification, which are difficult to meet the complex and dynamic needs of HVCA systems. To overcome the difficulty in employment of centralized architecture involved multi sensor fault diagnosis and fault diagnosis methods limited by pure digital simulation platform, a fully distributed chaotic bat algorithm is proposed to diagnosis multiple-sensor faults and is verified by the hardware-in-the-loop simulation platform in HVAC systems. First, inspired by the bat swarm intelligence and multiple agents, we characterize the sensor fault diagnosis problem as an optimization problem of the bat predation behavior. Specifically, each sensor node is abstracted as a bat colony, and each bat colony communicates with directly connected neighboring bat colony in physical space. Second, to avoid the tendency of bat algorithm to fall into locally optimal solutions, the tent chaotic map is employed to enhance the bat colony search ability for improving the randomness of bat algorithm, involves escaping from local extreme points and increasing the diversity of the bat colony. Finally, we embed the temperature and humidity sensors into the hardware-in-the-loop simulation platform to construct the near-actual physical environment. The performance and convergence of the proposed method are verified using both a pure digital simulation platform and a built-in hardware-in-the-loop simulation platform. Compared with basic bat algorithm (BA), particle swarm optimization algorithm (PSO) and chaotic bat algorithm (CBA), the proposed fully distributed chaotic bat algorithm (DCBA) have higher accuracy and can achieve the 98.41 % accuracy in fixed bias (FB) sensor faults, 96.96 % accuracy in complete failure (CF), 95.84 % in drift bias (DB) and 96.08 % accuracy in accuracy drops (AD).

Enhancing Transportation Efficiency with Optimal Container Placement Using the Bat Algorithm

Abstract The objective of this article is to provide an in-depth exploration of the complex task of container storage at seaports, a problem characterized as one of the challenging NP (Non-Deterministic Polynomial time) problems. Seaports are faced with the dilemma of accommodating a finite number of containers due to the constrained surface area available, making the management of container storage operations a formidable task. To address this challenge, the present study leverages a meta-heuristic approach aimed at identifying an optimal storage plan for containers within a storage area. This approach is informed by insights drawn from bat swarm intelligence, commonly known as the Bat Algorithm. By integrating principles from this nature-inspired algorithm, the authors seek to develop a robust solution for optimizing container storage strategies in seaports. This approach takes into account several critical constraints, including container travel distances and considerations related to container type and departure dates.

Sensing in the swarm: Spectro-temporal variation may facilitate self-recognition of echoes for bats flying in dense groups

Most biosonar models predict that bats rely on the correct assignment of an echo from a broadcast signal to successfully perceive their environment, which should be difficult for bats in dense groups. Brazilian free-tailed bats (Tadarida brasiliensis) form some of the largest aggregations on the planet and have flexible characteristics of their echolocation signals. We hypothesize they use subtle variations in spectro-temporal characteristics to facilitate echolocation in dense groups. To record from inside the swarm, we used both a stationary microphone that opportunistically captured the passing bat swarm, and a custom video and acoustic recorder carried by a trained hawk that flew through the swarm. We computed spectrograms from the acoustic recordings and extracted “time-frequency ridges.” These ridges were fit to low-order polynomials to generate model frequency modulation functions, which in turn uniquely determine continuous time-series representations of modeled emission waveforms. Standard signal detection methods (cross correlation and background normalization) enabled quantitative estimation of detection performance, including rejection of interfering emissions and echoes. Our results demonstrate that subtle but specific variation in spectro-temporal shape can constitute the basis of call differentiation, which may be an adaptive strategy used to reject acoustic signals from conspecifics when echolocating in dense swarms.

The soundscape of dense bat swarms: Differences based on location within the group

The laboratory work of Jim Simmons and colleagues has provided key information on how bats use harmonic structure to identify targets from background clutter. Although advantageous for detecting targets straight ahead, the question remains as to how bats modify echolocation, if at all, when identification of objects in the periphery may be needed, such as when flying in a dense group. Based on the work of Dr. Simmons and colleagues, we predicted that free-tailed bats in central positions of swarms may use different echolocation signals than bats on the periphery. We investigated this by quantifying the soundscape based on location within the group. To overcome the challenge of recording inside a dense swarm, we developed a microphone/camera unit carried by a trained Harris hawk that flew through the bat swarm. Frequency spectra were extracted and analyzed based on the frequency-dependent amplitude compared to position in the swarm. We found a significant difference in the soundscape between the ‘Edge’ and ‘Middle’ of the swarm for frequencies above 40 kHz, suggesting bats in central positions of the swarm produce echolocation signals with more energy in higher frequencies, which may aid in distinguishing their calls from conspecifics.

Proficient link state routing in mobile ad hoc network‐based deep Q‐learning network optimized with chaotic bat swarm optimization algorithm

SummaryMobile ad‐hoc network (MANET) is a category of ad‐hoc network that can be reconfigurable its network. MANETS are self‐organized networks, that can use the wireless links to connect various networks via mobile nodes: but it consumes more energy and it also has routing problems. This is the major drawback of being connected with the MANET technology. Therefore, this study proposes a new protocol as deep Q‐learning network optimized with chaotic bat swarm optimization algorithm (CBS)‐based optimized link state routing (OLSR) (CBS‐OLSR) for MANET. This protocol reduces MANET energy usage and adopts OLSR multi‐point relay (MPR) technology. MANET's OLSR and the CBS algorithm utilize a similar method to locate the best optimum path from source to destination node. By embedding the new improved deep Q‐learning and OLSR algorithms, both are used for optimizing the MPR sets selection, it can efficiently diminish the energy consumption in the network topology, but automatically increase the lifespan of the network. It also enhances the package delivery ratio and decreases end‐to‐end delay. The experimental outcomes prove that the proposed protocol is reliable and proficient that is appropriate for numerous MANET applications.

Using Artificial Intelligence Methods For Diagnosis Of Gingivitis Diseases

Artificial Intelligence Techniques, and image processing are playing a major role in medical science. In this paper, several methods of artificial intelligence techniques were used to diagnose Gingivitis disease. The Bat swarm algorithm, the Self-Organizing Map(SOM) algorithm and the Fuzzy Self-Organizing Map (FSOM)network algorithm were used to diagnose Gingivitis disease. Also, was used the traditional algorithm, which is the Principal Component Analysis (PCA) algorithm, for Feature Extraction of Gingivitis disease images. We compute the diagnostic accuracy on this images dataset. Next, we compared the final results of these three methods used and applied to this data. In this paper the best of these methods is the BAT, because in testing state the BAT was obtained higher accuracy for diagnose of Gingivitis disease equal (97.942%).

A novel path planning algorithm for mobile robot in dynamic environments using modified bat swarm optimization

This paper describes the path planning of an autonomous mobile robot using an advanced version of the swarm bat algorithm in a time-varying situation. The foremost objective of this present contribution is to acquire the shortest path between the initial and end-point avoiding dynamic obstacles. In this work, an adaptation of the frequency parameter of the standard bat algorithm to develop an improved version known as the modified frequency bat (MFB) algorithm is proposed. The modified frequency bat algorithm works in two steps, in the first step, the robot path is generated when there are no obstacles in the environment using the proposed method. As soon as an obstacle is detected near the robot, the second mode is initiated to avoid any obstacle. Extensive simulations are performed to validate the effectiveness of the proposed modified frequency bat algorithm by implementing it on standard benchmark functions and path planning problems and simultaneously matching its performance with the original bat algorithm. It has been concluded that the modified frequency bat algorithm shows superior performance as compared to the standard bat algorithm by drawing a shorter and collision-free path for the mobile robot path-planning problem.

Wildebeest optimization algorithm based on swarm intelligence method in solving optimization problems

Metaheuristic algorithms are effective ways to solve optimization problems and use existing phenomena in nature to solve these problems. Due to the independence of metaheuristic algorithms from the gradient information, the objective function can be used to solve large-scale problems by optimization solutions. The organisms’ behavior in nature in their interaction with each other is one of the optimization methods that are modeled as swarm-based algorithms. Swarm-based algorithms are a set of metaheuristic algorithms which are modeled based on group behavior of their organisms and social interactions. The behavior of wildebeests in nature is considered as a swarm-based algorithm for survival because it can be seen that these organisms migrate in groups and try to survive for themselves and their own herd. In this paper, a new metaheuristic algorithm (WOA) based on migratory and displacement behavior of wildebeests is presented of solving optimization problems. In this algorithm, problem solutions are defined as wildebeest herds that search the problem space for appropriate habitat. The results of the implementation of a set of benchmark functions for solving optimization problems such as the Wildebeest Optimization Algorithm, Whale Optimization Algorithm, BAT, Firefly and Particle Swarm Optimization (PSO) algorithms show that the proposed algorithm is less error rate to find global optimum and also caught up rate in the local optimum is less than the methods.

Digital Modulation Classification Based On BAT Swarm Optimization and Random Forest

The applications of digitally modulated signals are still in progress and expansion. Automatic Modulation Identification (AMI) is important to classify the digitally modulated signals ..To get better results of the system suggested optimization the features to discard weak or irrelevant features in the system and keep only strong relevant features .In this work, present hybrid intelligent system for the recognition related to the digitally modulated signals where used . The proposed (AMI) had been built to classify ten most popular schemes of digitally modulated signals, namely (2ASK, , 2PSK, 4PSK, 8PSK, 8QAM,16QAM ,32QAM, 64 QAM, 128QAM, and 256QAM), with the signal to noise ratio ranging from (-2 to 13) dB. High-order cumulants (HOCs) as well as high-order moments (HOMs) were utilized. .In this thesis used , Bat Swarm Optimization (BA).The Random Forest ( RF) classifier was introduced for the first time in this work. Simulation results of the System proposed , under additive white Gaussian noise channel, show that .While algorithm ( BA Swarm Optimization ) for the modulated signals we obtained a classification accuracy of around 92% for the SNR between (-2....12 ) dB.

A Novel Fuzzy Bat Based Ambulance Detection and Traffic Counting Approach

In the recent years the importance of automatic traffic control has increased due to the traffic jams problem especially in big cities for signal control and efficient traffic management. The input video is processed and analyzed to detect an ambulance vehicle. This article introduces a robust approach for ambulance detection and traffic counting approach using novel fuzzy Bat swarm optimization and different image processing techniques. The fuzzy Bat based optimization algorithm is used to generate a template of ambulance from the abstracted frames obtained from predefined ambulance samples. This is done by using a collection of Gabor filters that have been particularly customized for the ambulance detection problem in which filter selection is achieved to group filters that have similar characteristics. The fitness criterion based on Support Vector Machine (SVM) is used to evaluate the output filters. The proposed approach is composed of the following five fundamental building phases: Fuzzy Bat based optimization, image acquisition, object detection, counting the connected objects, and finally ambulance detection. One of the main advantages of the proposed approach is that key Gabor filters is obtained from the selected features (filters with highest membership values) which have a vital role in the ambulance detection phase. Experimental results show that the overall accuracy confirms that the performance of the proposed approach is high.

A hybrid bat–swarm algorithm for optimizing dam and reservoir operation

One of the major challenges and difficulties to generate optimal operation rule for dam and reservoir operation are how efficient the optimization algorithm to search for the global optimal solution and the time-consume for convergence. Recently, evolutionary algorithms (EA) are used to develop optimal operation rules for dam and reservoir water systems. However, within the EA, there is a need to assume internal parameters at the initial stage of the model development, such assumption might increase the ambiguity of the model outputs. This study proposes a new hybrid optimization algorithm based on a bat algorithm (BA) and particle swarm optimization algorithm (PSOA) called the hybrid bat–swarm algorithm (HB-SA). The main idea behind this hybridization is to improve the BA by using the PSOA in parallel to replace the suboptimal solution generated by the BA. The solutions effectively speed up the convergence procedure and avoid the trapping in local optima caused by using the BA. The proposed HB-SA is validated by minimizing irrigation deficits using a multireservoir system consisting of the Golestan and Voshmgir dams in Iran. In addition, different optimization algorithms from previous studies are investigated to compare the performance of the proposed algorithm with existing algorithms for the same case study. The results showed that the proposed HB-SA algorithm can achieve minimum irrigation deficits during the examined period and outperforms the other optimization algorithms. In addition, the computational time for the convergence procedure is reduced using the HB-SA. The proposed HB-SA is successfully examined and can be generalized for several dams and reservoir systems around the world.

An Improved Chaotic Bat Swarm Scheduling Learning Model on Edge Computing

Edge computing has strong real-time and big data interaction processing requirements. The long scheduling time and load imbalance among edge nodes and edge servers are the key problems of edge computing. The current cloud computing scheduling algorithms all have balance problems between algorithm complexity and performance, and cannot fundamentally solve the contradiction. It is a feasible method to use the deep learning model to train the scheduled data to achieve a direct prediction of the scheduling results. This paper mainly studies from two aspects, one is to obtain more accurate training data from the perspective of researching optimal scheduling algorithms, and the other is to improve the training speed from the perspective of improving the deep learning model. At first, an improved chaotic bat swarm algorithm is put forward. It introduces chaotic factors and second-order oscillation mechanisms to improve the speed of update and dynamic parameter mechanisms. Subsequently, the long short-term memory network deep learning model is trained with the historical data by the improved algorithm. The experimental results show that the improved learning model can achieve the purpose of quickly predicting the scheduling result.

A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

The firefly algorithm (FA), as a metaheuristic search method, is useful for solving diverse optimization problems. However, it is challenging to use FA in tackling high dimensional optimization problems, and the random movement of FA has a high likelihood to be trapped in local optima. In this research, we propose three improved algorithms, i.e., Repulsive Firefly Algorithm (RFA), Scattering Repulsive Firefly Algorithm (SRFA), and Enhanced SRFA (ESRFA), to mitigate the premature convergence problem of the original FA model. RFA adopts a repulsive force strategy to accelerate fireflies (i.e. solutions) to move away from unpromising search regions, in order to reach global optimality in fewer iterations. SRFA employs a scattering mechanism along with the repulsive force strategy to divert weak neighbouring solutions to new search regions, in order to increase global exploration. Motivated by the survival tactics of hawk-moths, ESRFA incorporates a hovering-driven attractiveness operation, an exploration-driven evading mechanism, and a learning scheme based on the historical best experience in the neighbourhood to further enhance SRFA. Standard and CEC2014 benchmark optimization functions are used for evaluation of the proposed FA-based models. The empirical results indicate that ESRFA, SRFA and RFA significantly outperform the original FA model, a number of state-of-the-art FA variants, and other swarm-based algorithms, which include Simulated Annealing, Cuckoo Search, Particle Swarm, Bat Swarm, Dragonfly, and Ant-Lion Optimization, in diverse challenging benchmark functions.

Republished Paper. Numerical study for single and multiple damage detection and localization in beam-like structures using BAT algorithm

This paper presents a new damage detection and localization technique based on the changes in vibration parameters using BAT and Particle Swarm Optimization algorithm. The finite element method is used to apply damage at specific element(s) of the considered beams. The damage is represented by a reduction in Young’s modulus and the identification of damage is formulated as an optimization problem using objective function based on Modal Scale Factor and changes in natural frequencies. A procedure for detecting and locating damage of beam-like structures based on BAT algorithm is used. This approach presents a method that can be used to detect the single and multiple-damage positions and the rate of damage in structural elements with high accuracy after the first iteration. The results obtained using BAT algorithm are compared to those obtained using Particle Swarm Optimization Algorithm. By taking noise into account in the damage detection and localization problem, it is shown that our approach based on BAT algorithm can detect the damage locations with high accuracy.

Retraction: Numerical study for single and multiple damage detection and localization in beam-like structures using BAT algorithm

This paper presents a new damage detection and localization technique based on the changes in vibration parameters using BAT and Particle Swarm Optimization algorithm. The finite element method is used to apply damage at specific element(s) of the considered beams. The damage is represented by a reduction in Young’s modulus and the identification of damage is formulated as an optimization problem using objective function based on Modal Scale Factor and changes in natural frequencies. A procedure for detecting and locating damage of beam-like structures based on BAT algorithm is used. This approach presents a method that can be used to detect the single and multiple-damage positions and the rate of damage in structural elements with high accuracy after the first iteration. The results obtained using BAT algorithm are compared to those obtained using Particle Swarm Optimization Algorithm. By taking noise into account in the damage detection and localization problem, it is shown that our approach based on BAT algorithm can detect the damage locations with high accuracy.

Using Swarm Intelligence for solving NPHard Problems

Swarm Intelligence algorithms are computational intelligence algorithms inspired from the collective behavior of real swarms such as ant colony, fish school, bee colony, bat swarm, and other swarms in the nature. Swarm Intelligence algorithms are used to obtain the optimal solution for NP-Hard problems that are strongly believed that their optimal solution cannot be found in an optimal bounded time. Travels Salesman Problem (TSP) is an NP-Hard problem in which a salesman wants to visit all cities and return to the start city in an optimal time. This article applies most efficient heuristic based Swarm Intelligence algorithms which are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bat algorithm (BA), and Ant Colony Optimization (ACO) algorithm to find a best solution for TSP which is one of the most well-known NP-Hard problems in computational optimization. Results are given for different TSP problems comparing the best tours founds by BA, ABC, PSO and ACO.

Optimal allocation of FACTS devices for static security enhancement in power systems via imperialistic competitive algorithm (ICA)

The problem of optimal allocation of flexible AC transmission systems (FACTS) devices is deemed as a formidable optimisation problem. Metaheuristics are the common approaches for solving FACTS allocation problems. Imperialistic competitive algorithm (ICA) is a well-established optimisation algorithm which has been successfully employed for solving complex optimisation problems in different fields. It is inspired by imperialistic competition and socio-political evolution of human beings and offers appropriate exploration and exploitation capabilities during the search for global optima. This paper employs ICA for solving FACTS allocation problem in a way that low values of overloads and voltage deviations are resulted both during line outage contingencies and demand growth. Thyristor-controlled phase shifting transformers (TCPST’s) and thyristor-controlled series compensators (TCSC’s) have been used as FACTS devices. The results of employing ICA for FACTS allocation problem indicate that ICA Offers better results than artificial bee colony (ABC), gravitational search algorithm (GSA), evolutionary programming (EP), bat swarm optimisation (BSO), nonlinear programming (NLP), pattern search (PS), asexual reproduction optimisation (ARO) and backtracking search algorithm (BSA).

Evaluation of a simple model for the acoustics of bat swarms

Bats using their biosonar while flying in dense swarms may face significant bioacoustic challenges, in particular mutual sonar jamming. While possible solutions to the jamming problem have been investigated several times, the severity of this problem has received far less attention. To characterize the acoustics of bat swarms, a simple model of the acoustically relevant properties of a bat swarm has been evaluated. The model contains only four parameters: bat density, biosonar beamwidth, duty cycle, and a scalar measure for the smoothness in the flight trajectories. In addition, a threshold to define substantial jamming was set relative to the emission level. The simulations results show that all four model parameters can have a major impact on jamming probability. Depending on the combination of parameter values, situations with or without substantial jamming probabilities could be produced within reasonable ranges of all model parameters. Hence, the model suggests that not every bat swarm does necessarily impose grave jamming problems. Since the model parameters should be comparatively easy to estimate for actual bat swarms, the simulation results could give researchers a way to assess the acoustic environment of actual bat swarms and determine cases where a study of biosonar jamming could be worthwhile.

Determination of optimal parameters for dual-layer cathode of polymer electrolyte fuel cell using computational intelligence-aided design.

Because of the demands for sustainable and renewable energy, fuel cells have become increasingly popular, particularly the polymer electrolyte fuel cell (PEFC). Among the various components, the cathode plays a key role in the operation of a PEFC. In this study, a quantitative dual-layer cathode model was proposed for determining the optimal parameters that minimize the over-potential difference and improve the efficiency using a newly developed bat swarm algorithm with a variable population embedded in the computational intelligence-aided design. The simulation results were in agreement with previously reported results, suggesting that the proposed technique has potential applications for automating and optimizing the design of PEFCs.