Data Clustering Method Based on Improved Bat Algorithm With Six Convergence Factors and Local Search Operators

Abstract

Clustering as an unsupervised learning method is a process of dividing a data object or observation object into a subset, that is to classify the data through observation learning instead of example learning without the guidance of the prior class label information. Bat algorithm (BA) is a swarm intelligence optimization algorithm inspired by bat's ultrasonic echo localization foraging behavior, but it has the disadvantages of being easily trapped into local minima and not being highly accurate. So an improved bat algorithm was proposed. In the global search, a Gaussian-like convergence factor is added, and five different convergence factors are proposed to improve the global optimization ability of the algorithm. In the local search, the hunting mechanism of the whale optimization algorithm (WOA) and the sine position updating strategy are adopted to improve the local optimization ability of the algorithm. This paper compares the clustering effect of the improved bat algorithm with bat algorithm, flower pollination algorithm (FPA), harmony search (HS) algorithm, whale optimization algorithm and particle swarm optimization (PSO) algorithm on seven real data sets under six different convergence factors. The simulation results show that the clustering effect of the improved bat algorithm is superior to other intelligent optimization algorithms.

Tobacco Leaves Disease Identification and Spot Segmentation Based on the Improved ORB Algorithm

“…If the traditional FAST9-16 algorithm is used for detection, it meets the requirement that the gray value of more than 9 continuous pixels in the neighborhood of 16 pixels is sufficiently different. So, the system will identify it as a corner point, and the point p is only an edge point [13,14]. erefore, in order to exclude the interference of such edge points on the detection results, the FAST algorithm is improved as follows: 24-pixel points around the pixel point p are taken as the detection template, the gray value of the point p is I p , and a threshold T is set.…”

Recent advances of bat-inspired algorithm, its versions and applications

“…In the classification, clustering, and forecasting domain, the BA is successfully applicable in several problems such as prediction and classification [ 18 , 20 , 74 , 123 , 125 , 183 , 185 , 195 , 205 , 212 , 245 , 285 , 294 , 307 ], data clustering [ 17 , 27 , 46 , 98 , 223 , 234 , 238 , 295 , 301 , 345 , 346 ], data forecasting [ 39 , 168 , 243 , 335 ], and train the feed forward neural network [ 75 , 171 , 213 ]. These applications are shown in Fig 15 .…”

A review of swarm-based metaheuristic optimization techniques and their application to doubly fed induction generator

“…Echolocation varies greatly, and depends on factors such as frequency, wavelength, loudness, and rate of sonic pulses. The bat algorithm uses a few assumptions of the echolocation used by bats [113] , [114] . The first assumption is that every bat utilizes echolocation to determine distance and are able to distinguish between prey and objects.…”

“…The pulse emission rate is based on the current iteration number and decreases exponentially from the initial specified pulse emission rate. If the random number is greater, the position of the best bat is updated as follows [113] , [114] , [115] , [116] : Where ∈ is a randomized number in the range [ ], is the current loudness of the bat and is based on the current iteration number. Initially, the required parameters are defined.…”

“…Although the BA has the merit of a fast convergence rate [118] , it suffers the demerits of a poor search accuracy and being easily trapped in local minima [114] . The authors in [114] proposed five unique factors of convergence to enhance the global search capability of the algorithm. These convergence factors are of cosine form, sine form, tangent form, power function form and exponential form.…”