Bat Algorithm Algorithm Research Articles

Improved load frequency control performance by tuning parameters of PID controller and BESS using Bat algorithm

The oscillation of frequency can cause the generator to run out of sync in a power system. Therefore, load frequency control (LFC) is needed to reduce frequency oscillation and prevent out of sync operation. The LFC regulates the governor to balance the turbine speed with changes in the load of existing. In this paper, a proportional integral differential (PID) controller and a battery energy storage system (BESS) are added to an LFC to improve the frequency stability of a power system. The parameters of the PID controller and BESS are optimized using the Bat algorithm (BA) to attain a good coordination. The frequency performance analysis is done by introducing disturbance in the form of changes in load power. The simulation results show that the frequency deviation of the system with the PID controller and BESS, has a faster settling time and smaller overshoot value. The system performs better than those with only the PID controller or the BESS. In conclusion, the BA algorithm can be used to find optimal parameter values of the PID controller and BESS for a synchronized coordination of an LFC.

The corona virus search optimizer for solving global and engineering optimization problems

In this paper, a new and effective meta-heuristic method named corona-virus search optimizer (CVSO) is proposed inspired based on the movement and search of the corona-virus among different societies, prevalence, and death of individuals. The CVSO has local and global search suitable balance due to the use of evolutionary strategies as well as appropriate social learning. This algorithm has desirable optimization power, easy implementation and requires only a control parameter. The CVSO algorithm is implemented on 30 modern and standard test functions of CEC2014 and also 10 test functions of CEC2019. The performance and capability of CVSO is compared with new and well-known meta-heuristics including particle swarm optimization (PSO), artificial bee colony (ABC) tree growth algorithm (TGA), and Lion optimization algorithm (LOA), bat algorithm (BA) and harris hawks optimization (HHO). The results showed that the CVSO can be more effective than other algorithms to solve the test functions and also various complex real-world engineering optimization problems in terms of optimization accuracy and convergence rate. Also, the superiority of CVSO is demonstrated in the designing of a hybrid energy system by achieving a lower cost of M$ 2.7492 compared to TGA, LOA, ABC, PSO, BA, and HHO algorithms, which achieve costs of M$ 2.7878, M$ 2.7556, M$ 2.8521, M$ 2.9503, M$ 3.0942, and M$ 2.9614, respectively. In addition, due to the optimal balance between local and global search and strength, it is able to avoid getting stuck in locally optimal solutions while obtaining global optimal solutions with a higher convergence rate than other algorithms. The source code of CVSO is publicly available at https://github.com/Irajfaraji/CVSO.

Impact of Parameter Tuning with Genetic Algorithm, Particle Swarm Optimization, and Bat Algorithm on Accuracy of the SVM Model in Landslide Susceptibility Evaluation

As one of the most serious geological hazards, landslides affect infrastructure construction. Thus, it is vital to prepare reliable landslide susceptibility evaluation maps to avoid landslide‐prone areas in construction projects. More studies on landslide susceptibility using machine learning have emerged in recent years, but there is a need to study ways to draw up high‐precision evaluation maps. In this article, a 60 km oil pipeline in China’s Kunming was selected as the research area. The data of 141 landslide points in the research area were obtained through field work and data collection. Meanwhile, lithology, elevation, aspect, slope, stream power index, topographic wetness index, average annual rainfall from 2017 to 2021, distance to roads, and terrain roughness were selected as causal factors of landslide susceptibility in the research area. First, the information value method was used to quantify the impact of conditional factors on landslides. Genetic algorithm (GA), particle swarm optimization (PSO), and bat algorithm (BA) were then used for parameter tuning, and the support vector machine (SVM) was used to analyze landslide susceptibility in the research area. Finally, the receiver operating characteristic (ROC) curve was used to test the model performance after parameter tuning with GA, PSO, and BA. The results show that the area‐under‐the‐curve (AUC) values obtained through SVM, GA‐SVM, PSO‐SVM, and BA‐SVM are 81.1%, 86.2%, 89%, and 91.8%, respectively. SVM had the best performance after parameter tuning with the BA algorithm.

An improved bat algorithms for optimization design of truss structures

This paper exploits a novel improved metaheuristic to entrance the standard bat algorithm (BA) performance for optimization engineering problems and optimization design of truss structures. As is the case with many metaheuristic algorithms, BA is straightforward to implement, and such a basic algorithm may address a wide variety of problems with great flexibility. However, when its exploitation stage is changed too quickly by rapidly shifting the loudness and pulse emission rate, it may stagnate after an early stage. It could result in a low probability of success in finding the real prey. Therefore, a new loudness function is proposed to adapt the step size of the random walk efficiently. Also, this study proposes a push-process technique that can generates an intervention in the BA algorithm to expose the true global optimal in a few generations instead of many calculation times. Then, all results are validated to those of well-established research. Finally, the proposed BA is applied to optimize the structural weight of truss structures.

Features extraction of fingerprints based on hybrid particle swarm optimization and bat algorithms

Most security system's essential errand is to check that the people are in fact who they claim to be. In Contrast to traditional techniques such as passwords and smart cards that are used in some organizations, fingerprint identification may be preferred as it makes the information virtually impossible to steal. The most extensive used biometric features are Fingerprints, in order to identify a person because of their uniqueness and invariance. The fingerprint consists of valleys and ridges on the surface of a fingertip. In this paper, a new hybrid strategy Particle Swarm Optimization (PSO) with Bat Algorithm (BA) is proposed to extract features from fingerprint images. Both PSO and BA algorithms are swarm-based algorithms that mimics the swarm behaviour of particles and bats in nature. In the field of image processing, features are extremely significant. Before obtaining features, the noisy area should be removed from the foreground first, and then several important techniques are applied on each sample image in the database such as Fingerprint Enhancement by using Fast Fourier Transform (FFT), Binarization, and Thinning. The hybrid (PSO-BA) algorithm is proposed as a pre-enhancing step to select the clear minutiae (or feature) structures across several iterations, which will be more suited for the matching phase. By comparing the proposed method with several methods in calculating FAR and FRR, the results showed that the FAR (0.001) and FRR (0.01) were less than the other proposed methods. That means the hybrid (PSO-BA) algorithm has the better results, which means it can be used as one of the best search approaches to extract features from fingerprints

Cost optimization of a wind-solar-diesel system with battery storage

This work presents the optimization of a wind-solar-diesel system with battery storage for a continuous and reliable production of electrical energy. In this context, detailed mathematical modeling of the present system and its operation algorithm has been presented. The objective function of the system is to minimize its cost of energy (CoE) which estimates the average lifetime cost of power production per kWh. The cost elements comprising the CoE include investment costs, fuel costs, and operation and maintenance costs. The optimization is performed in the HOMER software in addition to three metaheuristic optimization techniques namely the Cuckoo Search algorithm (CS), the BAT algorithm (BA) and the Firefly algorithm (FA). The simulations conducted in this paper are based on meteorological data collected from an installation in Bouzareah. Simulation results show the excellent properties and superiority of the CS optimization method compared to HOMER, BA and FA algorithms and demonstrate the feasibility of the proposed hybrid PV-wind-diesel-battery system in Bouzareah.

Hybrid Particle Swarm and Whale Optimization Algorithm for Multi-Visit and Multi-Period Dynamic Workforce Scheduling and Routing Problems

This paper focuses on the dynamic workforce scheduling and routing problem for the maintenance work of harvesters in a sugarcane harvesting operation. Technician teams categorized as mechanical, hydraulic, and electrical teams are assumed to have different skills at different levels to perform services. The jobs are skill-constrained and have time windows. During a working day, a repair request from a sugarcane harvester may arrive, and as time passes, the harvester’s position may shift to other sugarcane fields. We formulated this problem as a multi-visit and multi-period dynamic workforce scheduling and routing problem (MMDWSRP) and our study is the first to address the workforce scheduling and routing problem (WSRP). A mixed-integer programming formulation and a hybrid particle swarm and whale optimization algorithm (HPSWOA) were firstly developed to solve the problem, with the objective of minimizing the total cost, including technician labor cost, penalty for late service, overtime, travel, and subcontracting costs. The HPSWOA was developed for route planning and maintenance work for each mechanical harvester to be provided by technician teams. The proposed algorithm (HPSWOA) was validated against Lingo computational software using numerical experiments in respect of static problems. It was also tested against the current practice, the traditional whale optimization algorithm (WOA), and traditional particle swarm optimization (PSO) in respect of dynamic problems. The computational results show that the HPSWOA yielded a solution with significantly better quality. The HPSWO was also tested against the traditional genetic algorithm (GA), bat algorithm (BA), WOA, and PSO to solve the well-known CEC 2017 benchmark functions. The computational results show that the HPSWOA achieved more superior performance in most cases compared to the GA, BA, WOA, and PSO algorithms.

Multi-objective optimization path planning with moving target

<div align="left"><span>Path planning or finding a collision-free path for mobile robots between starting position and its destination is a critical problem in robotics. This study is concerned with the multi objective optimization path planning problem of autonomous mobile robots with moving targets in dynamic environment, with three objectives considered: path security, length and smoothness. Three modules are presented in the study. The first module is to combine particle swarm optimization algorithm (PSO) with bat algorithm (BA). The purpose of PSO is to optimize two important parameters of BA algorithm to minimize distance and smooth the path. The second module is to convert the generated infeasible points into feasible ones using a new local search algorithm (LS). The third module obstacle detection and avoidance (ODA) algorithm is proposed to complete the path, which is triggered when the mobile robot detects obstacles in its field of vision. ODA algorithm based on simulating human walking in a dark room. Several simulations with varying scenarios are run to test the validity of the proposed solution. The results show that the mobile robots are able to travel clearly and completely safe with short path, proving the effectiveness of this method.</span><span> </span> </div>

Deep learning of GPS geodetic velocity

Installing permanent global positioning system (GPS) stations and receiving and monitoring long-term crustal deformation requires a high cost. Another solution, which could be an appropriate alternative, is applying some modern and smart estimation methods such as deep learning of artificial neural networks (DLANN). Based on the observations of the 42 GPS permanent stations in NW Iran, the velocity vectors of stations are estimated in unknown locations by four methods: back propagation of artificial neural networks (BPANN), least square collocation (LSC), Bat algorithm (BA) and random forest algorithm (RFA). BPANN has better performance and less variance than RFA, and the largest difference is in the gap areas, which estimates each vector method differently and it is preferred not to estimate these areas. The performances of LSC and BA algorithm is not better than BPANN. Therefore, it seems that the BPANN method can be considered as a suitable option for estimating the geodetic velocity field compared to other methods.

Artificial Bee Colony Algorithm and Bat Algorithm for Solving Travel Salesman Problem

There are many algorithms for optimization meta heuristic have developed on swarm intelligence-based that depending in the nature of design. One of them, Bat Algorithm (BA), is based on the "echolocation behaviors" of bats. Micro bat used echolocation to specify the prey, avoid obstacles and locate their roosting crevices in the dark. Another algorithm is Artificial Bee Colony (ABC) is a new optimization algorithm which depend on the "bee behavior" towards colony to search about the food. In this paper, presents the BA and ABC algorithms steps for solving TSP then try to search about best solution depending on the parameters for both algorithms. The results show the ABC is best performance than BA for finding the best tour quickly compared with other by consuming time lesser than by effecting on the convergence speed for searching the solution. Furthermore, The BA required more parameters to achieve each output efficiently and need using improved control strategy to balance between exploitation and exploration that consuming more time for it.

Soft Sensor Model Based on IBA-LSSVM for Photosynthetic Bacteria Fermentation Process

It is difficult to measure the key biological process variables of photosynthetic bacteria fermentation in real-time, and offline measurement has a large time lag and cannot meet the needs of real-time optimization control. In this paper, a soft sensor model based on least square support vector machine with an improved bat algorithm (IBA-LSSVM) was proposed. The velocity equation of the bat algorithm (BA) was improved and the random variation operation in differential evolution algorithm was introduced into BA algorithm. Thus, the diversity of the population can be increased, and the global and local searching ability of the BA algorithm can be enhanced. Furthermore, the IBA-LSSVM soft sensor model was established for the living cell concentration and compared with BA-LSSVM soft sensor model. Finally, the simulation results show that the improved model was the better learning ability and prediction performance than BA-LSSVM, the measurement error is 0.1358. The improved model could provide accurate guidance for the photosynthetic bacteria fermentation control optimization. This model has certain practical value.

A Novel Hybrid Approach Based on BAT Algorithm with Artificial Neural Network to Forecast Iran’s Oil Consumption

In this paper, we develop a function of population, GDP, import, and export by applying a hybrid bat algorithm (BAT) and artificial neural network (ANN). We apply these methods to forecast oil consumption in Iran. For this purpose, an improved artificial neural network (ANN) structure, which is optimized by the BAT is proposed. The variables between 1980 and 2017 were used, partly for installing and testing the method. This method would be helpful in forecasting oil consumption and would provide a level playing field for checking the energy policy authority impacts on the structure of the energy sector in an economy such as Iran with high economic interventionism by the government. The result of the model shows that the findings are in close agreement with the observed data, and the performance of the method was excellent. We demonstrate that its efficiency could be a helpful and reliable tool for monitoring oil consumption.

A hybrid algorithm (BAPSO) for capacity configuration optimization in a distributed solar PV based microgrid

This paper proposes a hybrid algorithm for capacity configuration optimization of a solar PV-battery-based micro-grid. The hybrid algorithm (BAPSO), which is a combination of Particle Swarm Optimization (PSO) and Bat Algorithm (BA), is designed to optimize the solar generation location and capacity for the efficient performance of a micro-grid. The algorithm considers dynamic transmission power loss optimization and integrates PSO and BA algorithms’ respective advantages to form a hybrid algorithm. The proposed algorithm combines PSO’s fast convergence ability with the less computation time ability of BA to better optimal solution by incorporating the BA’s frequency into the PSO velocity equation to control the pace. The design from the proposed algorithm is tested and validated on IEEE 30 bus test system. The transmission power loss before implementing the algorithm was 22 kW and reduced to 20 kW after the algorithm is used. A further reduction of 0.3 kW of losses is observed by placing the solar generation system at bus number 29.

Improved Bat Algorithm for UAV Path Planning in Three-Dimensional Space

This paper describes the flight path planning for unmanned aerial vehicles (UAVs) based on the advanced swarm optimization algorithm of the bat algorithm (BA) in a static environment. The main purpose of this work is that the UAVs can obtain an accident-free, shorter, and safer flight path between the starting point and the endpoint in the complex three-dimensional battlefield environment. Based on the characteristics of the standard BA and the artificial bee colony algorithm (ABC), a new modification of the BA algorithm is proposed in this work, namely, the improved bat algorithm integrated into the ABC algorithm (IBA). The IBA mainly uses ABC to modify the BA and solves the problem of poor local search ability of the BA. This article demonstrates the convergence of the IBA and performs simulations in MATLAB environment to verify its effectiveness. The simulations showed that the time required for the IBA to obtain the optimum solution is approximately 50% lower than the BA, and that the quality of the optimum solution is about 14% higher than the ABC. Furthermore, by comparing with other traditional and improved swarm intelligent path planning algorithms, the IBA can plan a faster, shorter, safer, accident-free flight path for UAVs. Finally, this article proves that IBA also has good performance in optimizing functions and has broad application potential.

A New Hybrid BA_ABC Algorithm for Global Optimization Problems

Bat Algorithm (BA) and Artificial Bee Colony Algorithm (ABC) are frequently used in solving global optimization problems. Many new algorithms in the literature are obtained by modifying these algorithms for both constrained and unconstrained optimization problems or using them in a hybrid manner with different algorithms. Although successful algorithms have been proposed, BA’s performance declines in complex and large-scale problems are still an ongoing problem. The inadequate global search capability of the BA resulting from its algorithm structure is the major cause of this problem. In this study, firstly, inertia weight was added to the speed formula to improve the search capability of the BA. Then, a new algorithm that operates in a hybrid manner with the ABC algorithm, whose diversity and global search capability is stronger than the BA, was proposed. The performance of the proposed algorithm (BA_ABC) was examined in four different test groups, including classic benchmark functions, CEC2005 small-scale test functions, CEC2010 large-scale test functions, and classical engineering design problems. The BA_ABC results were compared with different algorithms in the literature and current versions of the BA for each test group. The results were interpreted with the help of statistical tests. Furthermore, the contribution of BA and ABC algorithms, which constitute the hybrid algorithm, to the solutions is examined. The proposed algorithm has been found to produce successful and acceptable results.

BMDA: applying biogeography-based optimization algorithm and Mexican hat wavelet to improve dragonfly algorithm

One of the methods for solving optimization problems is applying metaheuristic algorithms that find near to optimal solutions. Dragonfly algorithm is one of the metaheuristic algorithms which search problem space by the inspiration of hunting and emigration behavior of dragonflies in nature. However, it suffers from the premature convergence of the population to an undesirable point in the detection ability (global search). In this research, an improved dragonfly algorithm called BMDA (applying Biogeography-based algorithm, Mexican hat wavelet, and Dragonfly algorithm) is presented to resolve the premature convergence in high workloads by creating a mutation phase based on the combination of the biogeography-based optimization (BBO) migration process and the Mexican hat wavelet transform in dragonfly algorithm (DA). The algorithm was evaluated for the mean error in comparison with standard dragonfly algorithm (DA), Memory-based Hybrid Dragonfly Algorithm (MHDA), chaotic dragonfly algorithm version 9 (CDA9), Adaptive_DA algorithm, bat algorithm (BAT), particle swarm optimization algorithm (PSO), raven roosting optimization (RRO) and whale optimization algorithm (WOA) using the CEC2017 benchmark functions. The implementation results of the proposed BMDA algorithm applying different benchmark functions outweighed the DA-based algorithm, MHDA algorithm, CDA9 algorithm, Adaptive_DA algorithm, BAT algorithm, PSO algorithm, RRO, and WOA algorithms in terms of mean error.

Optimized Sensor Nodes Deployment in Wireless Sensor Network Using Bat Algorithm

For the optimal performance of wireless sensor networks in different areas of applications needs to maximize the coverage area of sensor nodes. The coverage of sensor nodes in monitoring region can be improved by using efficient node deployment algorithms. In this paper node deployment based on bat algorithm (BA) is proposed to enhance the coverage rate of nodes. Each bat describes solution for deployment of sensor nodes individually. In bat algorithm based node deployment grid points covered by one sensor node are excluded for remaining sensor nodes. The benefit of eliminating the grid points is that the load on remaining nodes is decreased and there is no chance of overlapping i.e. grid point is covered by only one sensor node. The simulations of node deployment based on BA and fruit fly optimization algorithm (FOA) are also demonstrated. In this paper to further increase the coverage rate of sensor nodes the performance of various parameters of bat algorithm such as loudness, pulse emission rate, maximum frequency, grid points and sensing radius has been optimized. The simulation results of node deployment based on optimized bat algorithm are also compared with BA and FOA based node deployment in terms of mean coverage rate, computation time and standard deviation. The coverage rate curve for various numbers of iterations and sensor nodes are also presented for optimized bat algorithm, BA and FOA. The results demonstrate the effectiveness of optimized bat algorithm as it achieved more coverage rate than BA and FOA.

Highly Precise Modified Blue Whale Method Framed by Blending Bat and Local Search Algorithm for the Optimality of Image Fusion Algorithm

Image fusion has gained huge popularity in the field of medical and satellite imaging for image analysis. The lack of usages of image fusion is due to a deficiency of suitable optimization techniques and dedicated hardware. In recent days WOA (whale optimization algorithm) is gaining popularity. Like another straightforward nature-inspired algorithm, WOA has some problems in its searching process. In this paper, we have tried to improve the WOA algorithm by modifying the WOA algorithm. This MWOA (modified whale optimization algorithm) algorithm is amalgamed with LSA (local search algorithm) and BA (bat algorithm). The LSA algorithm helps the system to be faster, and BA algorithm helps to increase the accuracy of the system. This optimization algorithm is checked using MATLAB R2018b. Simulated using ModelSim, and the synthesizing is done using Xilinx Vivado 18.2 synthesis tool. The outcome of the simulation result and the synthesis result outshine other metaheuristic optimization algorithms.

GIS-Based Niche Hybrid Bat Algorithm for Solving Optimal Spatial Search

Complex nonlinear optimization problems are involved in optimal spatial search, such as location allocation problems that occur in multidimensional geographic space. Such search problems are generally difficult to solve by using traditional methods. The bat algorithm (BA) is an effective method for solving optimization problems. However, the solution of the standard BA is easily trapped at one of its local optimum values. The main cause of premature convergence is the loss of diversity in the population. The niche technique is an effective method to maintain the population diversity, to enhance the exploration of the new search domains, and to avoid premature convergence. In this paper, a geographic information system- (GIS-) based niche hybrid bat algorithm (NHBA) is proposed for solving the optimal spatial search. The NHBA is able to avoid the premature convergence and obtain the global optimal values. The GIS technique provides robust support for processing a substantial amount of geographical data. A case in Fangcun District, Guangzhou City, China, is used to test the NHBA. The comparative experiments illustrate that the BA, GA, FA, PSO, and NHBA algorithms outperform the brute-force algorithm in terms of computational efficiency, and the optimal solutions are more easily obtained with NHBA than with BA, GA, FA, and PSO. Moreover, the precision of NHBA is higher and the convergence of NHBA is faster than those of the other algorithms under the same conditions.

A Novel Bat Algorithm based on Cross Boundary Learning and Uniform Explosion Strategy

Population-based algorithms have been used in many real-world problems. Bat algorithm (BA) is one of the states of the art of these approaches. Because of the super bat, on the one hand, BA can converge quickly; on the other hand, it is easy to fall into local optimum. Therefore, for typical BA algorithms, the ability of exploration and exploitation is not strong enough and it is hard to find a precise result. In this paper, we propose a novel bat algorithm based on cross boundary learning (CBL) and uniform explosion strategy (UES), namely BABLUE in short, to avoid the above contradiction and achieve both fast convergence and high quality. Different from previous opposition-based learning, the proposed CBL can expand the search area of population and then maintain the ability of global exploration in the process of fast convergence. In order to enhance the ability of local exploitation of the proposed algorithm, we propose UES, which can achieve almost the same search precise as that of firework explosion algorithm but consume less computation resource. BABLUE is tested with numerous experiments on unimodal, multimodal, one-dimensional, high-dimensional and discrete problems, and then compared with other typical intelligent optimization algorithms. The results show that the proposed algorithm outperforms other algorithms.