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About 3,260 papers for “Bat algorithm”

Bat algorithm with Gaussian walk

Xingjuan Cai, Lei Wang, Qi Kang et al. · International Journal of Bio-Inspired Computation · 2014

Bat algorithm is a novel branch of evolutionary computation. Although there are several research papers that focus on this new algorithm, however, few of them concerns the high-dimensional numerical problems. In this paper, a new variant called bat algorithm with Gaussian walk (BAGW) is proposed aiming to solve this problem. In this variant, a Gaussian walk is employed in the local turbulence instead of the original uniform walk to improve the local search capability. Furthermore, to keep the high exploitation pressure, the velocity update equation is also changed. Finally, to increase the population diversity, the frequency is dominated by each dimension in our modification, as well as it is depended on the different bat in the standard version. To test the performance of our variant, four famous un-constraint numerical benchmarks are employed, and test on different dimensional cases, simulation results show our modification is effective.

Cited by 56

Local Enhanced Catfish Bat Algorithm

Yi Liu, Xingchun Diao, Jianjun Cao et al. · 2016

To resolve the conflict between convergence speed and diversity in Bat Algorithm (BA), we propose a novel improved BA algorithm called local enhanced catfish bat algorithm (LECBA). In LECBA, some inferior bats of initial population are reserved and each bat's historical worst position is updated. While population has been trapped into a local optimum, the initial inferior bats' positions and current bats' historical worst positions can attract population to leap out of the local optimums at a high speed through catfish effect which can improve population's diversity and preserve exploration ability. Furthermore, in each generation, the difference between the best and second-best global solutions is adopted to guide the best one to carry out a local search process called local learning behavior which can improve exploitation ability. The local learning behavior can be executed with population's evolution in parallel, and the local scale factor is dynamically adjusted during evolution. Experimental results show that LECBA has better global search ability and higher convergence speed than other modified bat algorithms.

Cited by 1

Bat-Cluster: A Bat Algorithm-based Automated Graph Clustering Approach

International Journal of Electrical and Computer Engineering (IJECE) · Bulletin of Miscellaneous Information (Royal Gardens Kew) · 2018

Defining the correct number of clusters is one of the most fundamental tasks in graph clustering. When it comes to large graphs, this task becomes more challenging because of the lack of prior information. This paper presents an approach to solve this problem based on the Bat Algorithm, one of the most promising swarm intelligence based algorithms. We chose to call our solution, “Bat-Cluster (BC).” This approach allows an automation of graph clustering based on a balance between global and local search processes. The simulation of four benchmark graphs of different sizes shows that our proposed algorithm is efficient and can provide higher precision and exceed some best-known values.

Cited by 0

A novel complex-valued bat algorithm

Liangliang LiYongquan Zhou, Springer-Verlag London · 2014

Bat algorithm is a recent optimization algo- rithm with quick convergence, but its population diversity can be limited in some applications. This paper presents a new bat algorithm based on complex-valued encoding where the real part and the imaginary part will be updated separately. This approach can increase the diversity of the population and expands the dimensions for denoting. The simulation results of fourteen benchmark test functions show that the proposed algorithm is effective and feasible. Compared to the real-valued bat algorithm or particle swarm optimization, the proposed algorithm can get high precision and can almost reach the theoretical value.

Cited by 0

Sawtooth Wave Improved Evolved Bat Algorithm

Pei‐Wei Tsai, Jing Zhang, Yuan Liu et al. · 2015

A strategy for improving the searching ability of the Evolved Bat Algorithm is proposed in this paper. Our proposed strategy utilizes a time dependent triangular signal to replace a fix parameter designed in the conventional Evolved Bat Algorithm to gain higher impact on the searching diversity. To test whether our proposed strategy provides positive contribution to the searching results, three test functions with known global optimum values are used in the experiments. The experimental results indicate that our proposed strategy improves the searching accuracy of the evolved bat algorithm about 44.161% in average.

Cited by 1

An Automated Gradient Enhanced Bat Algorithm

M. Prashanth Reddy, Ranjan Ganguli · 2018

Stochastic optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and bat algorithm (BA) carry out global optimization but typically consume substantial computational effort. On the other hand, deterministic algorithms like gradient descent converge rapidly but may get stuck in the local minima of multimodal functions. Thus, a way that couples the strengths of stochastic and deterministic optimization algorithm schemes is needed for better accuracy of final solution without getting trapped in the local minima. Bat algorithm is a recently developed swarm optimization technique which has been found to be a powerful method for multimodal optimization problems. The standard BA shows premature convergence and reduced convergence speeds under some conditions. So, a recently proposed enhanced bat algorithm (EBA) is augmented with gradient search to create a gradient enhanced bat algorithm (GEBA). This paper presents GEBA for optimization which involves post-hybridization of EBA with a gradient based local search algorithm to ensure accurate local exploration at final stages of GEBA. GEBA is tested with several test functions and found to perform very well. An automated gradient enhanced bat algorithm (AGEBA) is developed to addresses the problem of selecting good initial guess for gradient based algorithm. AGEBA is found to be an efficient algorithm requiring only one tuning error parameter thereby saving considerable manual effort.

Cited by 2

Stock Price Prediction using Bat Algorithm

Shailaja K.P, Akhil Yadav C S, M Manjunath · International Journal on Recent and Innovation Trends in Computing and Communication · 2023

There is no such thing as a safe path of investment in the stock market because it is highly unpredictable, which has been a major concern of investors globally. As a result, stock market or stock price prediction has been a hot topic for scholars and researchers and a popular topic for investors worldwide.

Cited by 1Open access

Parallel Bat Algorithm Using MapReduce Model

Kapil Sharma, Sanchi Girotra · International Journal of Information Technology and Computer Science · 2017

Algorithm is among the most popular meta-heuristic algorithms for optimization. Traditional bat algorithm work on sequential approach which is not scalable for optimization problems involving large search space, huge fitness computation and having large number of dimensions E.g. stock market strategies therefore parallelizing meta-heuristics to run on parallel machines to reduce runtime is required. In this paper, we propose two parallel variants of Bat Algorithm (BA) using MapReduce parallel programming model proposed by Google and have used these two variants for solving the Software development effort optimization problem. The experiment is conducted using Apache Hadoop implementation of MapReduce on a cluster of 6 machines. These variants can be used to solve various complex optimization problems by simply adding more hardware resources to the cluster and without changing the proposed variant code.

Cited by 4Open access

Portfolio Optimization Using the Bat Algorithm

Hamza Kamili, Mohammed Essaid Riffi · International Review on Computers and Software (IRECOS) · 2016

The bat algorithm is a meta-heuristic algorithm inspired by the bats behaviour characterized by using the echolocation process to localize their objective. In this work applied the bat algorithm has been used in order to solve the portfolio selection problem. This method has been adapted to the cardinality constrained efficient frontier model CCEF using four indexes Hang Seng in Hong Kong, DAX 100 in Germany FTSE 100 in USA and NIKKEI in Japan and the obtained results were compared to those of the unconstrained efficient frontier model (UEF). The bat method illustrates an optimal approximation between the constrained and the unconstrained models.

Cited by 5

Microstrip Coupler Design Using Bat Algorithm

Ulker, Ezgi Deniz, Ulker, Sadik · arXiv (Cornell University) · 2014

Evolutionary and swarm algorithms have found many applications in design problems since todays computing power enables these algorithms to find solutions to complicated design problems very fast. Newly proposed hybrid algorithm, bat algorithm, has been applied for the design of microwave microstrip couplers for the first time. Simulation results indicate that the bat algorithm is a very fast algorithm and it produces very reliable results.

Cited by 0Open access

An Automated Gradient Enhanced Bat Algorithm

Reddy, Prashanth M, Ganguli, Ranjan · ePrints@IISc (Indian Institute of Science) · 2018

Stochastic optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and bat algorithm (BA) carry out global optimization but typically consume substantial computational effort. On the other hand, deterministic algorithms like gradient descent converge rapidly but may get stuck in the local minima of multimodal functions. Thus, a way that couples the strengths of stochastic and deterministic optimization algorithm schemes is needed for better accuracy of final solution without getting trapped in the local minima. Bat algorithm is a recently developed swarm optimization technique which has been found to be a powerful method for multimodal optimization problems. The standard BA shows premature convergence and reduced convergence speeds under some conditions. So, a recently proposed enhanced bat algorithm (ERA) is augmented with gradient search to create a gradient enhanced bat algorithm (GEBA). This paper presents GEBA for optimization which involves post-hybridization of EBA with a gradient based local search algorithm to ensure accurate local exploration at final stages of GEBA. GEBA is tested with several test functions and found to perform very well. An automated gradient enhanced bat algorithm (AGEBA) is developed to addresses the problem of selecting good initial guess for gradient based algorithm. AGEBA is found to be an efficient algorithm requiring only one tuning error parameter thereby saving considerable manual effort.

Cited by 0

The stability analysis of Bat Algorithm

Janusz P. Papliński, M. Łazoryszczak · 2017

The equations describing the position and movement of the new individuals in Bath Algorithm have the form of difference equations. The analysis of the behavior of solutions of this equation and in particular its stability is possible, after omitting of randomness in parameters and treatment of the algorithm as stationary. The simplify analysis of the choice of parameters of the bat algorithm based on the linear stability and behavior of the algorithm is presented in the paper. The study indicates the recommended areas of the location of the parameters, and it shows how the different parameters affect the behavior of the algorithm. A simple tool to speed up the tuning of the algorithm was obtained.

Cited by 2

Load frequency control using BAT algorithm

Suneet Kumar Gupta, Gauri Shankar, K. Kumari et al. · 2016

In this paper a metaheuristic optimization technique known as BAT algorithm is used for optimal tuning of controller parameters for obtaining the optimal performance of the load frequency control. This optimization algorithm is inspired by the echolocation behavior of natural bats. To validate the potency of the proposed method, a linear model of two-area thermal power system is examined as a tested system for the applied disturbances. The simulation results are provided to reveal the perfection of BAT algorithm over various optimization techniques in tuning of Proportional Integral (PI) and Proportional Integral Derivative (PID) controller parameters. Evaluation of simulation results showed that the suggested algorithm achieves good performance for load changes compared to other optimization techniques.

Cited by 3

Enhancement structures for the bat algorithm

M. Prashanth Reddy, Ranjan Ganguli · 2018

Bat algorithm is a recently developed swarm optimization technique which has been found to be a powerful method for multimodal optimization problems. Some of the drawbacks in standard BA are reduced convergence speed due to lack of inertia weight factor and premature convergence due to low diversification capability of the algorithm as the problem's dimension increases. This paper presents an enhanced bat algorithm (EBA) for optimization which employs five different enhancement structures to improve the standard bat algorithm for better global exploration. EBA is tested on several standard test functions and correlated with recently published studies in the literature using non-parametric statistical tests. It shown from statistical test results that EBA is often superior to the standard BA and is also computationally efficient compared to other published algorithms such as GA and PSO.

Cited by 5

Microstrip Coupler Design Using Bat Algorithm

EzgiDeniz Ulker, Sadik Ulker · International Journal of Artificial Intelligence & Applications · 2014

Evolutionary and swarm algorithms have found many applications in design problems since todays computing power enables these algorithms to find solutions to complicated design problems very fast. Newly proposed hybrid algorithm, bat algorithm, has been applied for the design of microwave microstrip couplers for the first time. Simulation results indicate that the bat algorithm is a very fast algorithm and it produces very reliable results.

Cited by 2Open access

Economic Dispatch Problem using Bat Algorithm

Yamina Ahlem Gherbi, Hamid Bouzeboudja, Fatiha Lakdja · SHILAP Revista de lepidopterología · 2014

A BAT algorithm to solve the economic dispatch problem is presented in this paper. The modern power system has become very complex in nature with consumption of the electric power which is in rise progression and this tendency increases with the industrialization and the growth of the population. Moreover, one loss of alimentation can cause the interruption of the various production processes, and in front of consumers who become increasingly demanding by wanting more energy and better quality, the companies of production of electrical energy must thus ensure the regular provisioning of this request, and without interruption, but these requirements compromise the cost of generation. Application of BAT algorithm in this paper is based on mathematical modeling to solve economic dispatch problems by a single equivalent objective function. The algorithm is tested on 6-unit system. BAT algorithm is easy to implement and better than other algorithms in terms of accuracy and efficiency.

Cited by 26Open access