Bat Algorithm Algorithm Research Articles

Multi-Level Thresholding with Fractional-Order Darwinian PSO and Tsallis Function

A novel optimal multi-level thresholding is proposed using gray scale images for Fractional-order Darwinian Particle Swarm Optimization (FDPSO) and Tsallis function. The maximization of Tsallis entropy is chosen as the Objective Function (OF) which monitors FDPSO’s exploration until the search converges to an optimal solution. The proposed method is tested on six standard test images and compared with heuristic methods, such as Bat Algorithm (BA) and Firefly Algorithm (FA). The robustness of the proposed thresholding procedure was tested and validated on the considered image data set with Poisson Noise (PN) and Gaussian Noise (GN). The results obtained with this study verify that, FDPSO offers better image quality measures when compared with BA and FA algorithms. Wilcoxon’s test was performed by Mean Structural Similarity Index (MSSIM), and the results prove that image segmentation is clear even in noisy dataset based on the statistical significance of the FDPSO with respect to BA and FA.

A novel modified bat algorithm hybridizing by differential evolution algorithm

The bat algorithm (BA) is one of the metaheuristic algorithms that are used to solve optimization problems. The differential evolution (DE) algorithm is also applied to optimization problems and has successful exploitation ability. In this study, an advanced modified BA (MBA) algorithm was initially proposed by making some modifications to improve the exploration and exploitation abilities of the BA. A hybrid system (MBADE), involving the use of the MBA in conjunction with the DE, was then suggested in order to further improve the exploitation potential and provide superior performance in various test problem clusters. The proposed hybrid system uses a common population, and the algorithm to be applied to the individual is selected on the basis of a probability value, which is calculated in accordance with the performance of the algorithms; thus, the probability of applying a successful algorithm is increased. The performance of the proposed method was tested on functions that have frequently been studied, such as classical benchmark functions, small-scale CEC 2005 benchmark functions, large-scale CEC 2010 benchmark functions, and CEC 2011 real-world problems. The obtained results were compared with the results obtained from the standard BA and other findings in the literature and interpreted by means of statistical tests. The developed hybrid system showed superior performance to the standard BA in all test problem sets and produced more acceptable results when compared to the published data for the existing algorithms. In addition, the contribution of the MBA and DE algorithms to the hybrid system was examined.

A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems

Multi-purpose advanced systems are considered a complex problem in water resource management, and the use of data-intelligence methodologies in operating such systems provides major advantages for decision-makers. The current research is devoted to the implementation of hybrid novel meta-heuristic algorithms (e.g., the bat algorithm (BA) and particle swarm optimization (PSO) algorithm) to formulate multi-purpose systems for power production and irrigation supply. The proposed hybrid modelling method was applied for the multi-purpose reservoir system of Bhadra Dam, which is located in the state of Karnataka, India. The average monthly demand for irrigation is 142.14 (106 m3), and the amount of released water based on the new hybrid algorithm (NHA) is 141.25 (106 m3). Compared with the shark algorithm (SA), BA, weed algorithm (WA), PSO algorithm, and genetic algorithm (GA), the NHA decreased the computation time by 28%, 36%, 39%, 82%, and 88%, respectively, which represents an excellent enhancement result. The amount of released water based on the proposed hybrid method attains a more reliable index for the volumetric percentage and provides a more effective operation rule for supplying the irrigation demand. Additionally, the average demand for power production is 18.90 (106 kwh), whereas the NHA produces 18.09 (106 kwh) of power. Power production utilizing the NHA’s operation rule achieved a sufficient magnitude relative to that of stand-alone models, such as the BA, PSO, WA, SA, and GA. The excellent proficiency of the developed intelligence expert system is the result of the hybrid structure of the BA and PSO algorithm and the substitution of weaker solutions in each algorithm with better solutions from other algorithms. The main advantage of the proposed NHA is its ability to increase the diversity of solutions and hence avoid the worst possible solutions obtained using BA, that is, preventing a decrease in local optima. In addition, the NHA enhances the convergence rate obtained using the PSO algorithm. Hence, the proposed NHA as an intelligence model could contribute to providing reliable solutions for complex multi-purpose reservoir systems to optimize the operation rule for similar reservoir systems worldwide.

Demand estimation of water resources based on algorithm comparison

Water is the source of life and the correct assessment of water resources is an important pre-requisite for the rational utilisation of water resources. In this paper, water resources are evaluated and predicted by three different algorithms, including Bat Algorithm (BA), Particle Swarm Optimisation (PSO) and Pigeon-Inspired Optimisation (PIO). Comparing the errors of water resources assessed for the three algorithms, we select an algorithm of the minimum error to predict the future water demand. In the experiments, firstly, the water data from 2003 to 2012 are used to find the optimal weights of the models. Then, the weight factor is combined with the given model to gain the error between predicted value and the remaining data (2013-2015). Finally, the simulation results show that PIO algorithm has a better performance compared with the BA and PSO algorithms.

Demand estimation of water resources based on algorithm comparison

Water is the source of life and the correct assessment of water resources is an important pre-requisite for the rational utilisation of water resources. In this paper, water resources are evaluated and predicted by three different algorithms, including Bat Algorithm (BA), Particle Swarm Optimisation (PSO) and Pigeon-Inspired Optimisation (PIO). Comparing the errors of water resources assessed for the three algorithms, we select an algorithm of the minimum error to predict the future water demand. In the experiments, firstly, the water data from 2003 to 2012 are used to find the optimal weights of the models. Then, the weight factor is combined with the given model to gain the error between predicted value and the remaining data (2013-2015). Finally, the simulation results show that PIO algorithm has a better performance compared with the BA and PSO algorithms.

BA-PNN-based methods for power transformer fault diagnosis

This paper presents a machine learning-based approach to power transformer fault diagnosis based on dissolved gas analysis (DGA), a bat algorithm (BA), optimizing the probabilistic neural network (PNN). PNN is a radial basis function feedforward neural network based on Bayesian decision theory, which has a strong fault tolerance and significant advantages in pattern classification. However, one challenge still remains: the performance of PNN is greatly affected by its hidden layer element smooth factor which impacts the classification performance. The proposed approach addresses this challenge by deploying the BA algorithm, a kind of bio-inspired algorithm to optimize PNN. Using the real data collected from a transformer system, we conducted the experiments for validating the performance of the developed method. The experimental results demonstrated that BA is an effective algorithm for optimizing PNN smooth factor and BA-PNN can improve the fault diagnosis performance; in turn, and the machine learning-based model (BA-PNN) can significantly enhance the accuracies of power transformer fault diagnosis.

Bi-Population Based Discrete Bat Algorithm for the Low-Carbon Job Shop Scheduling Problem

Job shop scheduling problem (JSP) is a combinatorial optimization problem, which has been widely studied due to its strong theoretical and background for application. However, in previous studies on the traditional JSP, the optimization objective is mainly relative to time, such as makespan, flow time, tardiness, earliness, and workload. With the advent of green manufacturing, energy consumption should be considered in the JSP. Therefore, a low-carbon JSP is studied in this paper. Due to the NP-hard nature, a meta-heuristic algorithm, bat algorithm (BA), is considered in this paper. According to the characteristics of the problem, a kind of bi-population-based discrete BA (BDBA) is proposed to minimize the sum of the energy consumption cost and the completion-time cost. A parallel searching mechanism is first introduced to the algorithm, by which the population is divided into two sub-populations to, respectively, adjust the job permutation and the processing speed of each machine. Three communication strategies are used to implement the cooperation between the sub-populations. In addition, due to the fact that the original BA was developed to deal with the continuous problems, a modified discrete updating approach is proposed to make the BA algorithm directly work in a discrete domain. Finally, extensive simulations have been conducted to test the effectiveness of the proposed BDBA algorithm. The experimental data demonstrate that the proposed BDBA is effective in solving the low-carbon JSP under study.

A new Kriging–Bat Algorithm for solving computationally expensive black-box global optimization problems

ABSTRACTMany global optimization (GO) algorithms have been introduced in recent decades to deal with the Computationally Expensive Black-Box (CEBB) optimization problems. The high number of objective function evaluations, required by conventional GO methods, is prohibitive or at least inconvenient for practical design applications. In this work, a new Kriging–Bat algorithm (K–BA) is introduced for solving CEBB problems with further improved search efficiency and robustness. A Kriging surrogate model (SM) is integrated with the Bat Algorithm (BA) to find the global optimum using substantially reduced number of evaluations of the computationally expensive objective function. The new K–BA algorithm is tested and compared with other well-known GO algorithms, using a set of standard benchmark problems with 2 to 16 design variables, as well as a real-life engineering optimization application, to determine its search capability, efficiency and robustness. Results of the comprehensive tests demonstrated the suitability and superior capability of the new K–BA.

An Overview of Mutation Strategies in Bat Algorithm

Bat algorithm (BA) is a population based stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. BA has been mostly used to resolve diverse kind of optimization problems and one of major issue faced by BA is frequently captured in local optima meanwhile handling the complex real world problems. Many authors improved the standard BA with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This paper aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of BA. It has been tried to provide guidelines for the researchers who are active in the area of BA and its mutation strategies. The objective of this study is divided in two sections: primarily to display the improvement of BA with mutation strategies that may enhance the performance of standard BA up to great extent and secondly, to motivate the researchers and developers for using BA to solve the complex real world problems. This study presents a comprehensive survey of the various BA algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the BA algorithm in detail for the researcher.

Improved Raven Roosting Optimization algorithm (IRRO)

Abstract Optimization means finding the best solution from among an infinite number of possible solutions to a complex problem. Several methods are generally used for solving complex problems, such as meta-heuristic algorithms that are inspired from living organisms. Raven Roosting Optimization (RRO) is an algorithm inspired by the mimicking behavior of ravens but it has the problem of premature convergence. This paper provides an expansion of RRO algorithm namely IRRO to resolve the problem. It focuses on a population that follows the leader and divides the population into groups of weak ravens and greedy ravens. It also uses a parameter to control the amount of food remaining for the ravens. This is then extended by comparison of the proposed algorithm to RRO, Particle Swarm Optimization (PSO) approach Bat algorithm (BA), Chicken Swarm Optimization (CSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA). The experimental results on 30 standard benchmark functions how the improvement of the proposed algorithm compared to RRO, PSO, BA, CSO, GWO and WOA algorithms.

Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers

In this paper, a comparison among Particle swarm optimization (PSO), Bee Colony Optimization (BCO) and the Bat Algorithm (BA) is presented. In addition, a modification to the main parameters of each algorithm through an interval type-2 fuzzy logic system is presented. The main aim of using interval type-2 fuzzy systems is providing dynamic parameter adaptation to the algorithms. These algorithms (original and modified versions) are compared with the design of fuzzy systems used for controlling the trajectory of an autonomous mobile robot. Simulation results reveal that PSO algorithm outperforms the results of the BCO and BA algorithms.

An enhanced Bat algorithm with mutation operator for numerical optimization problems

This article introduces a new variation of a known metaheuristic method for solving global optimization problems. The proposed algorithm is based on the Bat algorithm (BA), which is inspired by the micro-bat echolocation phenomenon, and addresses the problems of local-optima trapping using a special mutation operator that enhances the diversity of the standard BA, hence the name enhanced Bat algorithm (EBat). The design of EBat is introduced and its performance is evaluated against 24 of the standard benchmark functions, and compared to that of the standard BA, as well as to several well-established metaheuristic techniques. We also analyze the impact of different parameters on the EBat algorithm and determine the best combination of parameter values in the context of numerical optimization. The obtained results show that the new EBat method is indeed a promising addition to the arsenal of metaheuristic algorithms and can outperform several existing ones, including the original BA algorithm.

Optimization studies in high speed turning of Ti-6Al-4V

Surface roughness is a major concern to the present manufacturing sector without the wastage of material. Hence, in order to achieve good surface roughness and reduce production time, optimization is necessary. In this study optimization techniques based on swarm intelligence (SI) namely firefly algorithm (FA), particle swarm optimization (PSO) and a newly introduced metaheuristic algorithm namely bat algorithm (BA) has been implemented for optimizing machining parameters namely cutting speed, feed rate, depth of cut and tool flank wear and cutting tool vibrations in order to achieve minimum surface roughness. Two parameters Ra and Rt have been considered for evaluating the surface roughness. The performance of BA algorithm has been compared with FA algorithm and PSO, which is a commonly and widely used optimization algorithm in machining. The results conclude that BA produces better optimization, when compared to FA and PSO. Based on the literature review carried out, this work is a first attempt at using a metaheuristic algorithm namely BA in machining applications.

Optimal Design of Short Fiber Bragg Grating Using Bat Algorithm With Adaptive Position Update

We propose a new method to optimally design short triangular-spectrum fiber Bragg gratings (TS-FBGs), using the metaheuristic bat algorithm (BA). In addition, we propose a novel modification, i.e., adaptive position update (APU), to optimize the index modulation within a predetermined range. The discrete layer-peeling algorithm is used to efficiently initialize BA. The APU improves the search performance and accuracy of the BA algorithm in fiber Bragg grating (FBG) design application. Using the proposed method, a 0.2-nm-bandwidth TS-FBG, operating in the C-band, is designed. The short-length FBG with chirp-free structure prevents complex phase modulation for practical considerations. In addition, a dual-wavelength TS-FBG of 0.75-nm bandwidth operating in the eye-safe region has been designed and provided the most accurate optimum design length. The latter is well suited for FBGs inscribed in high-birefringent fibers, which are utilized in multiwavelength fiber laser systems. For both cases, the accuracy and search performance of APU-BA are shown to be better than those of previously reported algorithms in the literature.

Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems

This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices.

A Hybrid Bat Algorithm for Process Planning Problem

In CAPP, process planning (PP) involves two simultaneous tasks: operations selection and operations sequencing, which is difficult to be carried out to achieve the satisfied process plan. In this paper, a hybrid bat algorithm (BA) has been employed to solve PP problem. To apply BA in PP problem, a 2 x n matrix is proposed to represent the process plan solution. Some strategies of encoding, decoding, population initializing have been developed to adapt the BA algorithm to qualify to solve PP problem. To avoid local convergence, two local search strategies are incorporated into the standard BA. A modification for the local search of BA is executed to improve the bats’ movement. Two new operators for the different parts of the solution representation have been proposed to diversify the bat population. A simulation experiment has been carried out to verify the validity of the hybrid BA. The interesting results show the hybrid BA can generate the satisfactory solutions.