scholarly journals An improved form of the ant lion optimization algorithm for image clustering problems

2019 ◽  
pp. 1445-1460 ◽  
Author(s):  
METİN TOZ
Keyword(s):  
Objective Function ◽  
Artificial Bee Colony ◽  
Image Clustering ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
Objective Function Values ◽  
Lion Optimization Algorithm ◽  
Clustering Problems

Thispaperproposesanimprovedformoftheantlionoptimizationalgorithm(IALO)tosolveimageclustering problem. The improvement of the algorithm was made using a new boundary decreasing procedure. Moreover, a recently proposed objective function for image clustering in the literature was also improved to obtain well-separated clusters while minimizing the intracluster distances. In order to accurately demonstrate the performances of the proposed methods, firstly, twenty-three benchmark functions were solved with IALO and the results were compared with the ALO and a chaos-based ALO algorithm from the literature. Secondly, four benchmark images were clustered by IALO and the obtained results were compared with the results of particle swarm optimization, artificial bee colony, genetic, and K- means algorithms. Lastly, IALO, ALO, and the chaos-based ALO algorithm were compared in terms of image clustering by using the proposed objective function for three benchmark images. The comparison was made for the objective function values, the separateness and compactness properties of the clusters and also for two clustering indexes Davies– Bouldin and Xie–Beni. The results showed that the proposed boundary decreasing procedure increased the performance of the IALO algorithm, and also the IALO algorithm with the proposed objective function obtained very competitive results in terms of image clustering.

Test scheduling of System-on-Chip using Dragonfly and Ant Lion optimization algorithms

2020 ◽  
pp. 1-13
Author(s):  
Gokul Chandrasekaran ◽  
P.R. Karthikeyan ◽  
Neelam Sanjeev Kumar ◽  
Vanchinathan Kumarasamy
Keyword(s):  
Ant Colony Optimization ◽  
Artificial Bee Colony ◽  
Optimization Algorithms ◽  
System On Chip ◽  
Test Time ◽  
Test Scheduling ◽  
Bee Colony ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
On Chip

Test scheduling of System-on-Chip (SoC) is a major problem solved by various optimization techniques to minimize the cost and testing time. In this paper, we propose the application of Dragonfly and Ant Lion Optimization algorithms to minimize the test cost and test time of SoC. The swarm behavior of dragonfly and hunting behavior of Ant Lion optimization methods are used to optimize the scheduling time in the benchmark circuits. The proposed algorithms are tested on p22810 and d695 ITC’02 SoC benchmark circuits. The results of the proposed algorithms are compared with other algorithms like Ant Colony Optimization, Modified Ant Colony Optimization, Artificial Bee Colony, Modified Artificial Bee Colony, Firefly, Modified Firefly, and BAT algorithms to highlight the benefits of test time minimization. It is observed that the test time obtained for Dragonfly and Ant Lion optimization algorithms is 0.013188 Sec for D695, 0.013515 Sec for P22810, and 0.013432 Sec for D695, 0.013711 Sec for P22810 respectively with TAM Width of 64, which is less as compared to the other well-known optimization algorithms.

Research on intelligent clustering of male upper body

2021 ◽  
pp. 004051752110001
Author(s):  
Pengpeng Cheng ◽  
Xianyi Zeng ◽  
Pascal Bruniaux ◽  
Jianping Wang ◽  
Daoling Chen
Keyword(s):  
Artificial Bee Colony ◽  
Young Men ◽  
Cuckoo Search ◽  
The Body ◽  
Upper Body ◽  
Intelligent Algorithm ◽  
Density Peak ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Length Width

To study the upper body characteristics of young men, the body circumference, length, width, thickness, and angle of young men aged 18–25 and 26–35 years were collected to comprehensively characterize the concave and convex features of the front, back, and side of the human body. The Cuckoo Search-Density Peak intelligent algorithm was used to extract the feature factors of the upper body of men, and to cluster them. To verify the effectiveness of the intelligent algorithm, the clustering results of Cuckoo Search-Density Peak, Density Peak, Particle Swarm Optimization-Density Peak algorithm, Ant Colony Optimization-Density Peak algorithm, Genetic Algorithm-Density Peak algorithm, and Artificial Bee Colony-Density Peak algorithm were evaluated by Silouette and F-measures, respectively. The results show that the Cuckoo Search-Density Peak algorithm has the best clustering results and is superior to other algorithms. There are some differences in somatotype characteristics and somatotype indexes between young men aged 18–25 and 26–35 years.

A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system

2021 ◽  
Vol 63 (5) ◽  
pp. 442-447
Author(s):  
Hammoudi Abderazek ◽  
Ferhat Hamza ◽  
Ali Riza Yildiz ◽  
Liang Gao ◽  
Sadiq M. Sait
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Spur Gear ◽  
Ant Lion Optimization ◽  
Lion Algorithm ◽  
Weight Design ◽  
Sine Cosine Algorithm ◽  
Ant Lion ◽  
Teaching Learning ◽  
Lion Optimization Algorithm

Abstract Metaheuristic optimization algorithms have gained relevance and have effectively been investigated for solving complex real design problems in diverse fields of science and engineering. In this paper, a recent meta-heuristic approach inspired by human social concepts, namely the queuing search algorithm (QSA), is implemented for the first time to optimize the main parameters of the spur gear, in particular, to minimize the weight of a single-stage spur gear. The effectiveness of the algorithm introduced is examined in two steps. First, the algorithm used is compared with descriptions in previous studies and indicates that the final results obtained by QSA lead to a reduction in gear weight by 7.5 %. Furthermore, the outcomes obtained are compared with those for the other five algorithms. The results reveal that the QSA outperforms the techniques with which it is compared such as the sine-cosine optimization algorithm, the ant lion optimization algorithm, the interior search algorithm, the teaching-learning-based algorithm, and the jaya algorithm in terms of robustness, success rate, and convergence capability.

Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jasleen Kaur ◽  
Punam Rani ◽  
Brahm Prakash Dahiya
Keyword(s):  
Artificial Bee Colony ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Swarm Optimization ◽  
Content Type ◽  
Bee Colony ◽  
Glowworm Swarm Optimization ◽  
Optimal Cluster

Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency. Design/methodology/approach The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB. Findings The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay. Originality/value This research work is original.

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