scholarly journals A hybrid between TOA and levy flight trajectory for solving different cluster problems [iccicc18 #107]

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Clustering Algorithms ◽  
Optimization Technique ◽  
Poor Performance ◽  
Function Optimization ◽  
Complex Data ◽  
Lévy Flight ◽  
Flight Trajectory ◽  
Levy Flight ◽  
Major Shortcoming ◽  
Clustering Approach

In data analysis applications for extraction of useful knowledge, clustering plays an important role. The major shortcoming of traditional clustering algorithms is exhibiting poor performance in solving complex data cluster problems. This research paper introduces a novel hybrid optimization technique based clustering approach. This paper is designed with two main objectives: designing efficient function optimization algorithm and developing advanced data clustering approach. In achieving the first objective, the standard TOA is first enhanced by hybridizing with Lévy flight trajectory and benchmarked on 23 functions. A new clustering approach is developed by conjoining k-means algorithm and Lévy flight TOA. Tested the numerical complexity of the proposed novel clustering approach on 10 UCI clustering datasets and 4 web document cluster problems. Conducted several simulation experiments and done an analysis of the results. The obtained graphical and statistical analysis reveals that the proposed novel clustering approach yields better quality clusters.

scholarly journals A Hybrid Between TOA and Lévy Flight Trajectory for Solving Different Cluster Problems

2021 ◽  
Vol 15 (4) ◽  
pp. 1-25
Author(s):  
Nagaraju Devarakonda ◽  
Ravi Kumar Saidala ◽  
Raviteja Kamarajugadda
Keyword(s):  
Clustering Algorithms ◽  
Optimization Technique ◽  
Poor Performance ◽  
Function Optimization ◽  
Complex Data ◽  
Lévy Flight ◽  
Flight Trajectory ◽  
Levy Flight ◽  
Major Shortcoming ◽  
Clustering Approach

In data analysis applications for extraction of useful knowledge, clustering plays an important role. The major shortcoming of traditional clustering algorithms is exhibiting poor performance in solving complex data cluster problems. This research paper introduces a novel hybrid optimization technique based clustering approach. This paper is designed with two main objectives: designing efficient function optimization algorithm and developing advanced data clustering approach. In achieving the first objective, the standard TOA is first enhanced by hybridizing with Lévy flight trajectory and benchmarked on 23 functions. A new clustering approach is developed by conjoining k-means algorithm and Lévy flight TOA. Tested the numerical complexity of the proposed novel clustering approach on 10 UCI clustering datasets and 4 web document cluster problems. Conducted several simulation experiments and done an analysis of the results. The obtained graphical and statistical analysis reveals that the proposed novel clustering approach yields better quality clusters.

scholarly journals Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-22 ◽  
Author(s):  
Zhiming Li ◽  
Yongquan Zhou ◽  
Sen Zhang ◽  
Junmin Song
Keyword(s):  
Engineering Design ◽  
Function Optimization ◽  
Superior Performance ◽  
Local Optimum ◽  
Lévy Flight ◽  
Design Problems ◽  
Levy Flight ◽  
Spiral Path ◽  
Straight Line ◽  
Engineering Design Problems

The moth-flame optimization (MFO) algorithm is a novel nature-inspired heuristic paradigm. The main inspiration of this algorithm is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a spiral path around artificial lights. Aiming at the phenomenon that MFO algorithm has slow convergence and low precision, an improved version of MFO algorithm based on Lévy-flight strategy, which is named as LMFO, is proposed. Lévy-flight can increase the diversity of the population against premature convergence and make the algorithm jump out of local optimum more effectively. This approach is helpful to obtain a better trade-off between exploration and exploitation ability of MFO, thus, which can make LMFO faster and more robust than MFO. And a comparison with ABC, BA, GGSA, DA, PSOGSA, and MFO on 19 unconstrained benchmark functions and 2 constrained engineering design problems is tested. These results demonstrate the superior performance of LMFO.

Lévy flight trajectory-based whale optimization algorithm for engineering optimization

2018 ◽  
Vol 35 (7) ◽  
pp. 2406-2428 ◽  
Author(s):  
Yongquan Zhou ◽  
Ying Ling ◽  
Qifang Luo
Keyword(s):  
Optimization Problems ◽  
Engineering Optimization ◽  
Lévy Flight ◽  
Flight Trajectory ◽  
Levy Flight ◽  
Content Type ◽  
Search Spaces ◽  
Engineering Design Problems ◽  
Whale Optimization ◽  
Engineering Optimization Problems

Purpose This paper aims to represent an improved whale optimization algorithm (WOA) based on a Lévy flight trajectory and called the LWOA algorithm to solve engineering optimization problems. The LWOA makes the WOA faster, more robust and significantly enhances the WOA. In the LWOA, the Lévy flight trajectory enhances the capability of jumping out of the local optima and is helpful for smoothly balancing exploration and exploitation of the WOA. It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions. Design/methodology/approach In this paper, an improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is represented to solve engineering optimization problems. Findings It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions. Originality value An improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is first proposed.

Modified Selfish Herd Optimizer for Function Optimization

2020 ◽  
Vol 19 (01) ◽  
pp. 2050003
Author(s):  
Ruxin Zhao ◽  
Yongli Wang ◽  
Chang Liu ◽  
Peng Hu ◽  
Yanchao Li ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Global Search ◽  
Function Optimization ◽  
Test Cases ◽  
Lévy Flight ◽  
Levy Flight ◽  
Distribution Strategy ◽  
Selfish Herd

Selfish herd optimizer (SHO) is a new optimization algorithm. However, its optimization performance is not satisfactory. The main reason for this phenomenon is the weak global search ability of SHO. In this paper, in order to increase the global search ability of SHO, we add Levy-flight distribution strategy. To verify the performance of the proposed algorithm, we use 10 benchmark functions as test cases. Experiment results show that our algorithm is more competitive.

scholarly journals An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets

2021 ◽  
Vol 11 (2) ◽  
pp. 216-226
Author(s):  
Ayşe Nagehan Mat ◽  
Onur İnan ◽  
Murat Karakoyun
Keyword(s):  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Heuristic Algorithms ◽  
Clustering Algorithms ◽  
Whale Optimization Algorithm ◽  
Lévy Flight ◽  
Hunting Behavior ◽  
Levy Flight ◽  
Whale Optimization ◽  
Clustering Problems

Clustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be developed today. In this study, a new and effective clustering algorithm was developed by using the Whale Optimization Algorithm (WOA) and Levy flight (LF) strategy that imitates the hunting behavior of whales. With the developed WOA-LF algorithm, clustering was performed using ten medical datasets taken from the UCI Machine Learning Repository database. The clustering performance of the WOA-LF was compared with the performance of k-means, k-medoids, fuzzy c-means and the original WOA clustering algorithms. Application results showed that WOA-LF has more successful clustering performance in general and can be used as an alternative algorithm in clustering problems.

scholarly journals A Novel Modified Lévy Flight Distribution Algorithm based on Nelder-Mead Method for Function Optimization

2021 ◽  
pp. 487-496
Author(s):  
Ahmet DÜNDAR ◽  
Davut İZCİ ◽  
Serdar EKİNCİ ◽  
Erdal EKER
Keyword(s):  
Function Optimization ◽  
Lévy Flight ◽  
Levy Flight

Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

2019 ◽  
Vol 12 (4) ◽  
pp. 329-337 ◽  
Author(s):  
Venubabu Rachapudi ◽  
Golagani Lavanya Devi
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Feature Selection Method ◽  
Selection Method ◽  
Lévy Flight ◽  
Levy Flight ◽  
Local Optima ◽  
Histopathological Image ◽  
Surf Features ◽  
Histopathological Image Classification

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

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