Lèvy Flight Based Local Search in Differential Evolution

2013 ◽  
pp. 248-259 ◽  
Author(s):  
Harish Sharma ◽  
Shimpi Singh Jadon ◽  
Jagdish Chand Bansal ◽  
K. V. Arya
Keyword(s):  
Local Search ◽  
Differential Evolution ◽  
Lévy Flight ◽  
Levy Flight

scholarly journals Lévy flights in binary optimization

2013 ◽  
Vol 23 (4) ◽  
pp. 447-454 ◽  
Author(s):  
Martin Klimt ◽  
Jaromír Kukal ◽  
Matej Mojzeš
Keyword(s):  
Statistical Analysis ◽  
Local Search ◽  
Quantum Tunneling ◽  
Mutation Operator ◽  
Lévy Flight ◽  
Lévy Flights ◽  
Levy Flight ◽  
Binary Optimization ◽  
Levy Flights ◽  
Optimization Heuristics

Abstract There are many optimization heuristics which involves mutation operator. Reducing them to binary optimization allows to study properties of binary mutation operator. Modern heuristics yield from Lévy flights behavior, which is a bridge between local search and random shooting in binary space. The paper is oriented to statistical analysis of binary mutation with Lévy flight inside and Quantum Tunneling heuristics.

HSCWMA: A New Hybrid SCA-WMA Algorithm for Solving Optimization Problems

2021 ◽  
Vol 20 (02) ◽  
pp. 775-808
Author(s):  
Morteza Karimzadeh Parizi ◽  
Farshid Keynia ◽  
Amid Khatibi Bardsiri
Keyword(s):  
Local Search ◽  
Optimization Problems ◽  
High Capacity ◽  
Metaheuristic Algorithms ◽  
Development Effort ◽  
Effort Estimation ◽  
Lévy Flight ◽  
Levy Flight ◽  
Hybrid Metaheuristic ◽  
Real World Datasets

Hybrid metaheuristic algorithms have recently become an interesting topic in solving optimization problems. The woodpecker mating algorithm (WMA) and the sine cosine algorithm (SCA) have been integrated in this paper to propose a hybrid metaheuristic algorithm for solving optimization problems called HSCWMA. Despite the high capacity of the WMA algorithm for exploration, this algorithm needs to augment exploitation especially in initial iterations. Also, the sine and cosine relations used in the SCA provide the good exploitation for this algorithm, but SCA suffers the lack of an efficient process for the implementation of effective exploration. In HSCWMA, the modified mathematical search functions of SCA by Levy flight mechanism is applied to update the female woodpeckers in WMA. Moreover, the local search memory is used for all search elements in the proposed hybrid algorithm. The goal of proposing the HSCWMA is to use exploration capability of WMA and Levy flight, utilize exploitation susceptibility of the SCA and the local search memory, for developing exploration and exploitation qualification, and providing the dynamic balance between these two phases. For efficiency evaluation, the proposed algorithm is tested on 28 mathematical benchmark functions. The HSCWMA algorithm has been compared with a series of the most recent and popular metaheuristic algorithms and it outperforms them for solving nonconvex, inseparable, and highly complex optimization problems. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the software development effort estimation (SDEE) problem on three real-world datasets. The simulation results proved the superior and promising performance of the HSCWMA algorithm in the majority of evaluations.

Performance Enhancement of Differential Evolution by Incorporating Lévy Flight and Chaotic Sequence for the Cases of Satellite Images

2015 ◽  
Vol 6 (3) ◽  
pp. 69-81 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Md. Iqbal Quraishi ◽  
Sanjoy Das
Keyword(s):  
Differential Evolution ◽  
Satellite Image ◽  
Chaotic Sequence ◽  
Population Diversity ◽  
Lévy Flight ◽  
Step Size ◽  
Levy Flight ◽  
De Algorithm ◽  
Image Contrast Enhancement ◽  
Modified Algorithm

Differential Evolution (DE) is a simple but powerful evolutionary algorithm. Crossover Rate (CR) and Mutation Factor (F) are the most important control parameters in DE. Mutation factor controls the diversification. In traditional DE algorithm CR and F are kept constant. In this paper, the values of CR and F are modified to enhance the capability of traditional DE algorithm. In the first modified algorithm chaotic sequence is used to perform this modification. In the next modified algorithm Lévy Flight with chaotic step size is used for such enhancement. In the second modified DE, population diversity has been used to build population in every generation. As a result the algorithm does not converge prematurely. Both modified algorithms have been applied to optimize parameters of the parameterized contrast stretching function. The algorithms are tested for satellite image contrast enhancement and the results are compared, which show that DE via chaotic Lévy and population diversity information outperforms the traditional and chaotic DE in the image enhancement domain.

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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