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About 3,260 papers for “Bat algorithm”

Bat Algorithm

Omid Bozorg‐Haddad, Mohammad Solgi, Hugo A. Loáiciga · 2017

This chapter describes the bat algorithm (BA) that is a relatively new meta-heuristic optimization algorithm. The basic concepts of the BA are inspired by the echolocation behavior of bats. The following sections present a literature review of the BA and its applications, a description of the analogy between the behavior of microbats and the BA, a detailed explanation of the BA, and introduce a pseudo code of the BA.

Cited by 0

Modified Bat Algorithm

Selim Yılmaz, Ecir Uğur Küçüksille, Yavuz Cengiz · Elektronika ir Elektrotechnika · 2014

Heuristic optimization algorithms which are inspired by nature have become very popular for solving real world problems recently. The use of these algorithms increases day by day in the literature because of their flexible structures and non-containing confusing mathematical terms. One of these algorithms is Bat Algorithm (BA). BA is a heuristic algorithm based on echolocation characteristic of bats and developed by the mimics of bats’ foraging behaviour. In this study exploration mechanism of the algorithm is improved by modifying the equation of pulse emission rate and loudness of bats. The performance of Modified Bat Algorithm (MBA) is verified by 15 benchmark functions and the results were exhibited as comparative. The results of MBA are superior in terms of solution quality on optimization problems compared to BA. DOI: http://dx.doi.org/10.5755/j01.eee.20.2.4762

Cited by 68Open access

Compact Bat Algorithm

Thi-Kien Dao, Jeng‐Shyang Pan, Trong-The Nguyen et al. · Advances in intelligent systems and computing · 2014

Cited by 23

Many-objective BAT algorithm

Uzman Perwaiz, Irfan Younas, Adeem Ali Anwar · Greater South Information System · 2020

In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. The challenging part in MaOPs is to get the Pareto approximation (PA) with high diversity and good convergence. In Literature, in order to solve the issue of diversity and convergence in MaOPs, many approaches are proposed using different multi objective evolutionary algorithms (MOEAs). Moreover, to get better results, the researchers use the sets of reference points to differentiate the solutions and to model the search process, it further evaluates and selects the non-dominating solutions by using the reference set of solutions. Furthermore, this technique is used in some of the swarm-based evolutionary algorithms. In this paper, we have used some effective adaptations of bat algorithm with the previous mentioned approach to effectively handle the many objective problems. Moreover, we have called this algorithm as many objective bat algorithm (MaOBAT). This algorithm is a biologically inspired algorithm, which uses echolocation power of micro bats. Each bat represents a complete solution, which can be evaluated based on the problem specific fitness function and then based on the dominance relationship, non-dominated solutions are selected. In proposed MaOBAT, dominance rank is used as dominance relationship (dominance rank of a solution means by how many other solutions a solution dominated). In our proposed strategy, dynamically allocated set of reference points are used, allowing the algorithm to have good convergence and high diversity pareto fronts (PF). The experimental results show that the proposed algorithm has significant advantages over several state-of-the-art algorithms in terms of the quality of the solution.

Cited by 0Open access

Multi-Swarm Bat Algorithm

Ahmed Taha, Chen Soong Der, Aida Mustapha · Research Journal of Applied Sciences Engineering and Technology · 2015

Cited by 7Open access

Many-objective BAT algorithm

Uzman Perwaiz, Irfan Younas, Adeem Ali Anwar · PLoS ONE · 2020

In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. The challenging part in MaOPs is to get the Pareto approximation (PA) with high diversity and good convergence. In Literature, in order to solve the issue of diversity and convergence in MaOPs, many approaches are proposed using different multi objective evolutionary algorithms (MOEAs). Moreover, to get better results, the researchers use the sets of reference points to differentiate the solutions and to model the search process, it further evaluates and selects the non-dominating solutions by using the reference set of solutions. Furthermore, this technique is used in some of the swarm-based evolutionary algorithms. In this paper, we have used some effective adaptations of bat algorithm with the previous mentioned approach to effectively handle the many objective problems. Moreover, we have called this algorithm as many objective bat algorithm (MaOBAT). This algorithm is a biologically inspired algorithm, which uses echolocation power of micro bats. Each bat represents a complete solution, which can be evaluated based on the problem specific fitness function and then based on the dominance relationship, non-dominated solutions are selected. In proposed MaOBAT, dominance rank is used as dominance relationship (dominance rank of a solution means by how many other solutions a solution dominated). In our proposed strategy, dynamically allocated set of reference points are used, allowing the algorithm to have good convergence and high diversity pareto fronts (PF). The experimental results show that the proposed algorithm has significant advantages over several state-of-the-art algorithms in terms of the quality of the solution.

Cited by 22Open access

Many-objective BAT algorithm

Uzman Perwaiz, Irfan Younas, Adeem Ali Anwar · Greater South Information System · 2020

In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. The challenging part in MaOPs is to get the Pareto approximation (PA) with high diversity and good convergence. In Literature, in order to solve the issue of diversity and convergence in MaOPs, many approaches are proposed using different multi objective evolutionary algorithms (MOEAs). Moreover, to get better results, the researchers use the sets of reference points to differentiate the solutions and to model the search process, it further evaluates and selects the non-dominating solutions by using the reference set of solutions. Furthermore, this technique is used in some of the swarm-based evolutionary algorithms. In this paper, we have used some effective adaptations of bat algorithm with the previous mentioned approach to effectively handle the many objective problems. Moreover, we have called this algorithm as many objective bat algorithm (MaOBAT). This algorithm is a biologically inspired algorithm, which uses echolocation power of micro bats. Each bat represents a complete solution, which can be evaluated based on the problem specific fitness function and then based on the dominance relationship, non-dominated solutions are selected. In proposed MaOBAT, dominance rank is used as dominance relationship (dominance rank of a solution means by how many other solutions a solution dominated). In our proposed strategy, dynamically allocated set of reference points are used, allowing the algorithm to have good convergence and high diversity pareto fronts (PF). The experimental results show that the proposed algorithm has significant advantages over several state-of-the-art algorithms in terms of the quality of the solution.

Cited by 0Open access

Advancements in Bat algorithm

Arvinder Kaur, Yugal Kumar, Jagpreet Sidhu · Computational Methods in Science and Technology · 2024

The modern times have led to the adoption of distinctive meta-heuristic procedures for solving distinct class of optimization-problems. The meta-heuristics procedures have benefit above conventional algorithms because of strong capabilities of exploration and exploitation. In a similar vein, a Bat algorithm was created based on the echolocation habits of bats to address optimisation difficulties. Several advancements have been made in bat algorithm since then to achieve the state-of art results. The purpose of the study is to investigate bat algorithm for the various advancements done. Henceforth, the study presents the miniature analysis on advancements in bat algorithm. The bat algorithm applications in different areas are also investigated.

Cited by 0

A Comparative Study of Improved Bat Algorithm and Bat Algorithm on Numerical Benchmarks

Mehmet Beşkirli, İsmail Koç · 2015

Optimization is employed in solutions of many problems today. Optimization is described as finding the most suitable alternative among many others under the given constraints. Meta-heuristic algorithms used in solutions of the problems are developed upon the behaviors of living creatures in the nature. One of these is Bat Algorithm (BA), an optimization method based on swarm intelligence. BA is a numerical optimization technique developed in recent times. In this paper, it is aimed at improving Bat Algorithm (IBA) by using Differential Evolution Algorithm population strategy instead of population generation method of BA. IBA was tested on 17 benchmark functions with different characteristics. Suggested method has been seen to exhibit better results compared to the original BA.

Cited by 8

BAT-CLARA: BAT-inspired algorithm for Clustering LARge Applications

Yasmine Aboubi, Habiba Drias, Nadjet Kamel · IFAC-PapersOnLine · 2016

Bat algorithm is a new nature-inspired metaheuristic optimization algorithm introduced by Yang in 2010, especially based on echolocation behavior of microbats when searching their prey. Firstly, this algorithm is used to solve various continuous optimization problems. Clustering remains one of the most difficult challenges in data mining. In this paper, an overview of literature methods is undertaken followed by the presentation of a new algorithm called BAT-CLARA for clustering large data sets. It is based on bat behavior and k-medoids partitioning. The new technique is compared to the well-know partitioning algorithms PAM, CLARA, CLARANS and CLAM, a recent algorithm found in the literature. Experimental results show that, for the same tested datasets, BAT-CLARA is more effective and more efficient than previous clustering methods.

Cited by 29Open access