Automatic clustering using quantum-based multi-objective emperor penguin optimizer and its applications to image segmentation

2019 ◽  
Vol 34 (24) ◽  
pp. 1950193 ◽  
Author(s):  
Dinesh Kumar ◽  
Vijay Kumar ◽  
Rajani Kumari
Keyword(s):  
Image Segmentation ◽  
Fitness Function ◽  
Emperor Penguin ◽  
Schrödinger Equations ◽  
Cluster Centroid ◽  
Multi Objective ◽  
Automatic Clustering ◽  
Quantum Approach ◽  
Function Strategy ◽  
Threshold Setting

In this study, a novel quantum-based multi-objective is proposed using Schrödinger equations. The two new operations namely weighted cluster centroid computation and threshold setting are also introduced to refine cluster centroids. A novel fitness function strategy is also proposed for efficient searching. The proposed technique is compared with various well-known approaches. Experimental outcomes show that the proposed quantum approach outperforms other existing approaches.

Application of Synergistic Optimization Method by Maximin Fitness Function Strategy Based Multi-Objective Particle Swarm Optimization Algorithm in Metal Bellows Structural Design

2013 ◽  
Vol 722 ◽  
pp. 550-556
Author(s):  
Xiao Mo Yu ◽  
Ai Ling Qin ◽  
Jia Hai Xue ◽  
Jun Ke Ye ◽  
Wen Jing Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Structure Design ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Metal Bellows ◽  
Function Strategy

In this paper, the forming process is applied to the structure design of the metal bellows for the synergistic optimization.With bellows minimum overall stiffness and minimum weight for the optimization objectives to establish multi-objective optimization design model, using the Maximin fitness function strategy based multi-objective particle swarm optimization algorithm and introduce the multiple subgroup cooperative search strategy by master-slave clustering to get the optimized solution at the same time. The algorithm is applied to the synergistic optimization of the metal bellows structure design. The results show that the convergent speed of the algorithm is fast and can effectively approximate the actual bellows structure design, and provide users with more practical and intuitive effectively design scheme.

Shape automatic clustering-based multi-objective optimization with decomposition

2017 ◽  
Vol 28 (5-6) ◽  
pp. 497-508 ◽  
Author(s):  
Ruochen Liu ◽  
Ruinan Wang ◽  
Xin Yu ◽  
Lijia An
Keyword(s):  
Multi Objective Optimization ◽  
Multi Objective ◽  
Automatic Clustering

A population-based automatic clustering algorithm for image segmentation

2021 ◽  
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Gerald Schaefer ◽  
Mahshid Helali Moghadam ◽  
Mehrdad Saadatmand ◽  
Mahdi Pedram
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Population Based ◽  
Automatic Clustering

Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

2017 ◽  
Vol 8 (4) ◽  
pp. 58-83 ◽  
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Kapur’S Entropy ◽  
Moth Flame Optimization Algorithm ◽  
Popular Image ◽  
Better Than

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

Meta-Heuristic MOALO Algorithm for Energy-Aware Clustering in the Internet of Things

2021 ◽  
Vol 12 (2) ◽  
pp. 74-93
Author(s):  
Ravi Kumar Poluru ◽  
R. Lokeshkumar
Keyword(s):  
Energy Consumption ◽  
Data Transmission ◽  
Transmission Rate ◽  
Fitness Function ◽  
Shortest Paths ◽  
Cluster Head ◽  
Energy Aware ◽  
Energy Efficient Routing ◽  
Multi Objective ◽  
Data Transmission Rate

Boosting data transmission rate in IoT with minimized energy is the research issue under consideration in recent days. The main motive of this paper is to transmit the data in the shortest paths to decrease energy consumption and increase throughput in the IoT network. Thus, in this paper, the authors consider delay, traffic rate, and density in designing a multi-objective energy-efficient routing protocol to reduce energy consumption via the shortest paths. First, the authors propose a cluster head picking approach that elects optimal CH. It increases the effective usage of nodes energy and eventually results in prolonged network lifetime with enhanced throughput. The data transmission rate is posed as a fitness function in the multi-objective ant lion optimizer algorithm (MOALOA). The performance of the proposed algorithm is investigated using MATLAB and achieved high convergence, extended lifetime, as well as throughput when compared to representative approaches like E-LEACH, mACO, MFO-ALO, and ALOC.

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

2018 ◽  
pp. 771-797
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Kapur’S Entropy ◽  
Moth Flame Optimization Algorithm ◽  
Popular Image

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

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