scholarly journals Improved Bat Algorithm Applied to Multilevel Image Thresholding

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Adis Alihodzic ◽  
Milan Tuba
Keyword(s):  
Swarm Intelligence ◽  
State Of The Art ◽  
Bat Algorithm ◽  
Optimization Methods ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Thresholding ◽  
Bee Colony ◽  
Intractable Problems

Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

scholarly journals A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 786
Author(s):  
Yenny Villuendas-Rey ◽  
Eley Barroso-Cubas ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Swarm Intelligence ◽  
Data Clustering ◽  
Incomplete Data ◽  
Missing Values ◽  
Clustering Algorithms ◽  
Bat Algorithm ◽  
Hybrid Features ◽  
Bee Colony ◽  
Learning Tasks ◽  
Clustering Data

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

Comparison of Selected Swarm Intelligence Algorithms in Student Courses Recommendation Application

2014 ◽  
Vol 24 (01) ◽  
pp. 91-109 ◽  
Author(s):  
Janusz Sobecki
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Problem Space ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Grade Prediction ◽  
Bee Colony Optimization ◽  
Swarm Intelligence Algorithm

In this paper a comparison of a few swarm intelligence algorithms applied in recommendation of student courses is presented. Swarm intelligence algorithms are nowadays successfully used in many areas, especially in optimization problems. To apply each swarm intelligence algorithm in recommender systems a special representation of the problem space is necessary. Here we present the comparison of efficiency of grade prediction of several evolutionary algorithms, such as: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Intelligent Weed Optimization (IWO), Bee Colony Optimization (BCO) and Bat Algorithm (BA).

Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems

2011 ◽  
Vol 148-149 ◽  
pp. 134-137 ◽  
Author(s):  
Pei Wei Tsai ◽  
Jeng Shyang Pan ◽  
Bin Yih Liao ◽  
Ming Jer Tsai ◽  
Vaci Istanda
Keyword(s):  
Swarm Intelligence ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Computational Cost ◽  
Bat Algorithm ◽  
Experimental Results ◽  
Computational Time ◽  
Test Functions ◽  
Novel Algorithm

Inspired by Bat Algorithm, a novel algorithm, which is called Evolved Bat Algorithm (EBA), for solving the numerical optimization problem is proposed based on the framework of the original bat algorithm. By reanalyzing the behavior of bats and considering the general characteristics of whole species of bat, we redefine the corresponding operation to the bats’ behaviors. EBA is a new method in the branch of swarm intelligence for solving numerical optimization problems. In order to analyze the improvement on the accuracy of finding the near best solution and the reduction in the computational cost, three well-known and commonly used test functions in the field of swarm intelligence for testing the accuracy and the performance of the algorithm, are used in the experiments. The experimental results indicate that our proposed method improves at least 99.42% on the accuracy of finding the near best solution and reduces 6.07% in average, simultaneously, on the computational time than the original bat algorithm.

scholarly journals A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2718
Author(s):  
M. A. Elmagzoub ◽  
Darakhshan Syed ◽  
Asadullah Shaikh ◽  
Noman Islam ◽  
Abdullah Alghamdi ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Swarm Intelligence ◽  
Bat Algorithm ◽  
Quality Parameters ◽  
Smart Devices ◽  
The Internet ◽  
Time Data ◽  
Shared Resources ◽  
Bee Colony

Cloud computing offers flexible, interactive, and observable access to shared resources on the Internet. It frees users from the requirements of managing computing on their hardware. It enables users to not only store their data and computing over the internet but also can access it whenever and wherever it is required. The frequent use of smart devices has helped cloud computing to realize the need for its rapid growth. As more users are adapting to the cloud environment, the focus has been placed on load balancing. Load balancing allocates tasks or resources to different devices. In cloud computing, and load balancing has played a major role in the efficient usage of resources for the highest performance. This requirement results in the development of algorithms that can optimally assign resources while managing load and improving quality of service (QoS). This paper provides a survey of load balancing algorithms inspired by swarm intelligence (SI). The algorithms considered in the discussion are Genetic Algorithm, BAT Algorithm, Ant Colony, Grey Wolf, Artificial Bee Colony, Particle Swarm, Whale, Social Spider, Dragonfly, and Raven roosting Optimization. An analysis of the main objectives, area of applications, and targeted issues of each algorithm (with advancements) is presented. In addition, performance analysis has been performed based on average response time, data center processing time, and other quality parameters.

scholarly journals Role of Swarm Intelligence based Algorithms and their Applications for optimization in Software Reliability

2019 ◽  
Vol 8 (2) ◽  
pp. 3323-3327
Keyword(s):  
Swarm Intelligence ◽  
Software Reliability ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Particle Swarm Optimizer ◽  
Quality Performance ◽  
Reliability Models ◽  
Bee Colony ◽  
Software Reliability Models

The software has many features like functionality, maintainability, serviceability, usability, quality, performance. The reliability of the software is an imperative characteristic of software that leads to the eminence of the software. Software reliability is a great concern for software producers as well as users of the software. Keeping this concern in mind, there are already hundreds of software reliability models developed in the last four decades. This paper evaluates different algorithms based on Swarm intelligence in the way of optimization in software reliability. There are a number of swarm intelligence based algorithms that already have been used to improve the efficiency of the reliability of the software. Some of them are ant colony optimizer method (ACO), particle swarm optimizer method (PSO), artificial bee colony optimizer (ABC), bat algorithm, fish swarm algorithm, cuckoo search, bird flock algorithm. Still, there are so many algorithms based on Swarm intelligence that has not been used in this area. This paper investigates some known swarm intelligence based algorithms and their applications for optimizing software reliability.

An improved preprocessing algorithm for haplotype inference by pure parsimony

2014 ◽  
Vol 12 (04) ◽  
pp. 1450020 ◽  
Author(s):  
Mun-Ho Choi ◽  
Seung-Ho Kang ◽  
Hyeong-Seok Lim
Keyword(s):  
State Of The Art ◽  
Optimization Methods ◽  
Haplotype Inference ◽  
Computational Time ◽  
Single Chromosome ◽  
Wide Range ◽  
Inclusion Relations ◽  
Benchmark Datasets ◽  
Memory Reduction ◽  
Pure Parsimony

The identification of haplotypes, which encode SNPs in a single chromosome, makes it possible to perform a haplotype-based association test with disease. Given a set of genotypes from a population, the process of recovering the haplotypes, which explain the genotypes, is called haplotype inference (HI). We propose an improved preprocessing method for solving the haplotype inference by pure parsimony (HIPP), which excludes a large amount of redundant haplotypes by detecting some groups of haplotypes that are dispensable for optimal solutions. The method uses only inclusion relations between groups of haplotypes but dramatically reduces the number of candidate haplotypes; therefore, it causes the computational time and memory reduction of real HIPP solvers. The proposed method can be easily coupled with a wide range of optimization methods which consider a set of candidate haplotypes explicitly. For the simulated and well-known benchmark datasets, the experimental results show that our method coupled with a classical exact HIPP solver run much faster than the state-of-the-art solver and can solve a large number of instances that were so far unaffordable in a reasonable time.

Bio-Inspired Computational Intelligence and Its Application to Software Testing

2017 ◽  
pp. 429-444 ◽  
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Software Testing ◽  
Swarm Intelligence ◽  
Evolutionary Process ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Computational Procedure ◽  
Evolutionary Strategies ◽  
Test Case ◽  
Bee Colony ◽  
Selection Test

Bio inspired algorithms are computational procedure inspired by the evolutionary process of nature and swarm intelligence to solve complex engineering problems. In the recent times it has gained much popularity in terms of applications to diverse engineering disciplines. Now a days bio inspired algorithms are also applied to optimize the software testing process. In this chapter authors will discuss some of the popular bio inspired algorithms and also gives the framework of application of these algorithms for software testing problems such as test case generation, test case selection, test case prioritization, test case minimization. Bio inspired computational algorithms includes genetic algorithm (GA), genetic programming (GP), evolutionary strategies (ES), evolutionary programming (EP) and differential evolution(DE) in the evolutionary algorithms category and Ant colony optimization(ACO), Particle swarm optimization(PSO), Artificial Bee Colony(ABC), Firefly algorithm(FA), Cuckoo search(CS), Bat algorithm(BA) etc. in the Swarm Intelligence category(SI).

Bio-Inspired Computational Intelligence and Its Application to Software Testing

2021 ◽  
pp. 883-898
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Software Testing ◽  
Swarm Intelligence ◽  
Evolutionary Process ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Computational Procedure ◽  
Evolutionary Strategies ◽  
Test Case ◽  
Bee Colony ◽  
Selection Test

Bio inspired algorithms are computational procedure inspired by the evolutionary process of nature and swarm intelligence to solve complex engineering problems. In the recent times it has gained much popularity in terms of applications to diverse engineering disciplines. Now a days bio inspired algorithms are also applied to optimize the software testing process. In this chapter authors will discuss some of the popular bio inspired algorithms and also gives the framework of application of these algorithms for software testing problems such as test case generation, test case selection, test case prioritization, test case minimization. Bio inspired computational algorithms includes genetic algorithm (GA), genetic programming (GP), evolutionary strategies (ES), evolutionary programming (EP) and differential evolution(DE) in the evolutionary algorithms category and Ant colony optimization(ACO), Particle swarm optimization(PSO), Artificial Bee Colony(ABC), Firefly algorithm(FA), Cuckoo search(CS), Bat algorithm(BA) etc. in the Swarm Intelligence category(SI).

scholarly journals Bičių spiečių imitavimas sprendžiant optimizavimo uždavinius

2011 ◽  
Vol 56 ◽  
pp. 163-173
Author(s):  
Alfonsas Misevičius ◽  
Jonas Blonskis ◽  
Vytautas Bukšnaitis
Keyword(s):  
Swarm Intelligence ◽  
Honey Bees ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Optimization Methods ◽  
Quadratic Assignment ◽  
Optimization Approach ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

Straipsnyje nagrinėjami klausimai, susiję su naujoviškų metodų taikymu sprendžiant optimizavimo uždavinius. Šiuo konkrečiu atveju diskutuojama apie bičių spiečių elgsenos imitavimą ir galimą jo taikymą kombinatorinio (diskretinio) tipo optimizavimo uždaviniams. Straipsnio pradžioje aptariami konceptualūs aspektai ir bendroji bičių spiečių imitavimo algoritmų idėja. Aprašoma bičių spiečiaus imitavimo algoritmo realizacija atskiram nagrinėjamam atvejui – kvadratinio paskirstymo uždaviniui, kuris yra vienas iš aktualių ir sudėtingų kombinatorinio optimizavimo uždavinių pavyzdžių. Straipsnyje pateikiami ir su realizuotu algoritmu atliktų eksperimentų rezultatai, kurie iliustruoja skirtingų veiksnių (parametrų) įtaką gaunamų sprendinių kokybei ir patvirtina aukštą algoritmo efektyvumo lygį.Bee Swarm Intelligence in (Combinatorial) OptimizationAlfonsas Misevičius, Jonas Blonskis, Vytautas Bukšnaitis SummaryIn this paper, we discuss some issues related to the innovative intelligent optimization methods. More precisely, we are concerned with the bee colony optimization approach, which is inspired by the behaviour of natural swarms of honey bees. Both the conceptual methodological facets of the swarm intelligence paradigm and the aspects of implementation of the artificial bee colony algorithms are considered. In particular, we introduce an implementation of the artificial bee colony optimization algorithm for the well-known combinatorial optimization problem of quadratic assignment (QAP). The results of computational experiments with different variants of the implemented algorithm are also presented and discussed. Based on the obtained results, it is concluded that the proposed algorithm may compete with other efficient heuristic techniques. 

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