Bat Algorithm: Literature Review and Applications
---
paper_title: Cuckoo Search via Levy Flights

paper_content:

In this paper, we intend to formulate a new metaheuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.
---
paper_title: Bat algorithm for constrained optimization tasks

paper_content:

In this study, we use a new metaheuristic optimization algorithm, called bat algorithm (BA), to solve constraint optimization tasks. BA is verified using several classical benchmark constraint problems. For further validation, BA is applied to three benchmark constraint engineering problems reported in the specialized literature. The performance of the bat algorithm is compared with various existing algorithms. The optimal solutions obtained by BA are found to be better than the best solutions provided by the existing methods. Finally, the unique search features used in BA are analyzed, and their implications for future research are discussed in detail.
---
paper_title: Eagle Strategy Using L\'evy Walk and Firefly Algorithms For Stochastic Optimization

paper_content:

Most global optimization problems are nonlinear and thus difficult to solve, and they become even more challenging when uncertainties are present in objective functions and constraints. This paper provides a new two-stage hybrid search method, called Eagle Strategy, for stochastic optimization. This strategy intends to combine the random search using L\'evy walk with the firefly algorithm in an iterative manner. Numerical studies and results suggest that the proposed Eagle Strategy is very efficient for stochastic optimization. Finally practical implications and potential topics for further research will be discussed.
---
paper_title: Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization

paper_content:

The performance of an optimization tool is largely determined by the efficiency of the search algorithm used in the process. The fundamental nature of a search algorithm will essentially determine its search efficiency and thus the types of problems it can solve. Modern metaheuristic algorithms are generally more suitable for global optimization. This paper carries out extensive global optimization of unconstrained and constrained problems using the recently developed eagle strategy by Yang and Deb in combination with the efficient differential evolution. After a detailed formulation and explanation of its implementation, the proposed algorithm is first verified using twenty unconstrained optimization problems or benchmarks. For the validation against constrained problems, this algorithm is subsequently applied to thirteen classical benchmarks and three benchmark engineering problems reported in the engineering literature. The performance of the proposed algorithm is further compared with various, state-of-the-art algorithms in the area. The optimal solutions obtained in this study are better than the best solutions obtained by the existing methods. The unique search features used in the proposed algorithm are analyzed, and their implications for future research are also discussed in detail.
---
paper_title: A Chaotic Levy Flight Bat Algorithm for Parameter Estimation in Nonlinear Dynamic Biological Systems

paper_content:

We propose a synergistic approach to meta-heuristic search optimization algorithm. The fine balance between intensification (exploitation) and diversification (exploration) is very important to the overall efficiency and performance of a meta-heuristic search algorithm. Too little exploration and too much exploitation could cause the system to be trapped in local optima, which makes it very difficult or even impossible to find the global optimum. The diversification via randomization provides a good way to move away from local search to the search on the global scale and avoids the solutions being trapped at local optima, while increases the diversity of the solutions. The good combination of these two major components will usually ensure that the global optimality is achievable. It is worth pointing that the use of a uniform distribution is not the only way to achieve randomization. In fact, random walks such as Levy flights on a global scale are more efficient. The track of chaotic variable can travel ergodically over the whole search space. In general, the chaotic variable has special characters, i.e., ergodicity, pseudo-randomness and irregularity. To enrich the searching behavior and to avoid being trapped into local optimum, chaotic sequence and a chaotic Levy flight are incorporated in the metaheuristic search for efficiently generating new solutions. We presented synergistic strategies for meta-heuristic optimization learning, with an emphasis on the balance between intensification and diversification. In this paper, we apply the proposed search optimization algorithm and describe a general methodology to adaptively select the values of the model parameters for the reconstruction of biological system dynamics. We illustrate the application of the method by jointly estimating the parameter vector of the dynamics of endocytosis.
---
paper_title: A Fuzzy Bat Clustering Method for Ergonomic Screening of Office Workplaces

paper_content:

A method for screening of company workplaces with high ergonomic risk is developed. For clustering of company workplaces a fuzzy modification of bat algorithm is proposed. Using data gathered by a checklist from workplaces, information for ergonomic related health risks is extracted. Three clusters of workplaces with low, moderate and high ergonomic risk are determined. Using these clusters, workplaces with moderate and high ergonomic risk levels are screened and relevant solutions are proposed. By a case study this method is illustrated and validated. Important advantages of the method are reduction of computational effort and fast screening of workplaces with major ergonomic problems within a company.
---
paper_title: Use of fuzzy systems and bat algorithm for exergy modeling in a gas turbine generator

paper_content:

Exergy analysis plays a major role in thermal systems. Using exergy, apart from finding components for further improvement, fault detection and diagnosis can be conducted. This paper demonstrates the use of fuzzy systems for capturing exergy destruction variations in the main components of an industrial gas turbine. The models cover part load operating conditions. The fuzzy models are trained applying locally linear model tree algorithm followed by a meta-heuristic nature inspired algorithm called bat algorithm. The data for model training and validation are generated using semi-empirical models developed by the authors. However, the inputs to the model are kept the same as the inputs as experienced by a real gas turbine generator. The comparison between actual data from a different day and the prediction by the proposed method showed a match that is close enough to be considered as reliable. The models could be used for performance optimization and condition monitoring.
---
paper_title: Bat algorithm with global convergence for solving large-scale optimization problem

paper_content:

To solve large-scale optimization problems(OP),this paper constructed a bat algorithm with global convergence.In the algorithm,each bat was just an alternative solution of OP,used the principle of orthogonal Latin squares to construct initial positions of bat group so as to cover search space with balance dispersion and neat comparability,used the following behavior,autonomous behavior,averting-danger behavior and conformability behavior of bats to construct space positions trasnsfering strategies;used the loudness and rate of plus emission to ensure the bat groups keep either to stay unchanged or to transfer toward better positions,but never to transfer worse positions.During evoluation process,a bat's transferring from one position to another realized the search for the optimium solution.The result shows that the reducible stochastic matrix stability theorem can be applied to prove the global convergence of the algorithm.The case studys show that the algorithm has advantages of good suitability for different types of OPs and high convergence speed when applied to solve large-scale optimization problems.
---

Title: Bat Algorithm: Literature Review and Applications

Section 1: Introduction
Description 1: Introduce the topic of nature-inspired optimization algorithms, particularly focusing on the bat algorithm, its inspiration, and the purpose of this literature review.

Section 2: The Standard Bat Algorithm
Description 2: Describe the basic behavior of echolocation in microbats and explain the standard formulation of the bat algorithm.

Section 3: Echolocation of Microbats
Description 3: Provide detailed information on the echolocation process in microbats and its relevance to the bat algorithm.

Section 4: Bat Algorithm
Description 4: Outline the idealized rules and implementation details of the bat algorithm.

Section 5: Bat Motion
Description 5: Explain the movement dynamics of bats in the algorithm, including updating equations for position and velocity.

Section 6: Variations of Loudness and Pulse Rates
Description 6: Discuss the mechanisms that control the variation in loudness and pulse emission rates during the optimization process.

Section 7: Variants of Bat Algorithm
Description 7: Provide an overview of different variants of the bat algorithm developed to enhance performance and adapt to various applications.

Section 8: Applications of Bat Algorithm
Description 8: Highlight the diverse applications of the bat algorithm and its variants in fields like continuous optimization, combinatorial optimization, classification, clustering, data mining, image processing, and more.

Section 9: Continuous Optimization
Description 9: Review the applications of the bat algorithm in continuous optimization problems, particularly in engineering design.

Section 10: Combinatorial Optimization and Scheduling
Description 10: Examine how the bat algorithm is applied to solve combinatorial problems and scheduling tasks.

Section 11: Classifications, Clustering and Data Mining
Description 11: Discuss the use of the bat algorithm in classification, clustering, and data mining, including specific studies and comparative results.

Section 12: Image Processing
Description 12: Describe the effectiveness of the bat algorithm in image processing applications.

Section 13: Fuzzy Logic and Other Applications
Description 13: Review the applications of the bat algorithm in fuzzy logic systems and other specific fields.

Section 14: Discussions and Conclusions
Description 14: Summarize why the bat algorithm is efficient, its key features, and offer conclusions on its overall performance.

Section 15: Further Research Topics
Description 15: Suggest areas for future research, including parameter-tuning, parameter control, and improving convergence speed.