A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems

2020 ◽  
Author(s):  
Saeid Barshandeh ◽  
Maryam Haghzadeh
Keyword(s):  
Optimization Problems ◽  
Lévy Flight ◽  
Levy Flight ◽  
Search Optimization ◽  
Tree Seed

A Levy Flight Sine Cosine Algorithm for Global Optimization Problems

2021 ◽  
Vol 12 (1) ◽  
pp. 49-66
Author(s):  
Yu Li ◽  
Yiran Zhao ◽  
Jingsen Liu
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Population Diversity ◽  
Lévy Flight ◽  
Simulation Experiments ◽  
Mathematical Functions ◽  
Levy Flight ◽  
Engineering Problems ◽  
Sine Cosine Algorithm ◽  
Swarm Intelligence Algorithm

The sine cosine algorithm (SCA) is a recently proposed global swarm intelligence algorithm based on mathematical functions. This paper proposes a Levy flight sine cosine algorithm (LSCA) to solve optimization problems. In the update equation, the levy flight is introduced to improve optimization ability of SCA. By generating a random walk to update the position, this strategy can effectively search for particles to maintain better population diversity. LSCA has been tested 15 benchmark functions and real-world engineering design optimization problems. The result of simulation experiments with LSCA, SCA, PSO, FPA, and other improvement SCA show that the LSCA has stronger robustness and better convergence accuracy. The engineering problems are also shown that the effectiveness of the levy flight sine cosine algorithm to ensure the efficient results in real-world optimization problem.

Insulin DNA Sequence Classification Using Levy Flight Bat With Back Propagation Algorithm

2020 ◽  
pp. 232-252
Author(s):  
Siyab Khan ◽  
Abdullah Khan ◽  
Rehan Ullah ◽  
Maria Ali ◽  
Rahat Ullah
Keyword(s):  
Dna Sequence ◽  
Dna Sequences ◽  
Optimization Problems ◽  
Back Propagation ◽  
Bat Algorithm ◽  
Hybrid Models ◽  
Lévy Flight ◽  
Healthy Human ◽  
Levy Flight ◽  
Nature Inspired Algorithms

Various nature-inspired algorithms are used for optimization problems. Recently, one of the nature-inspired algorithms became famous because of its optimality. In order to solve the problem of low accuracy, famous computational methods like machine learning used levy flight Bat algorithm for the problematic classification of an insulin DNA sequence of a healthy human, one variant of the insulin DNA sequence is used. The DNA sequence is collected from NCBI. Preprocessing alignment is performed in order to obtain the finest optimal DNA sequence with a greater number of matches between base pairs of DNA sequences. Further, binaries of the DNA sequence are made for the aim of machine readability. Six hybrid algorithms are used for the classification to check the performance of these proposed hybrid models. The performance of the proposed models is compared with the other algorithms like BatANN, BatBP, BatGDANN, and BatGDBP in term of MSE and accuracy. From the simulations results it is shown that the proposed LFBatANN and LFBatBP algorithms perform better compared to other hybrid models.

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.

PSOSCALF: A new hybrid PSO based on Sine Cosine Algorithm and Levy flight for solving optimization problems

2018 ◽  
Vol 73 ◽  
pp. 697-726 ◽  
Author(s):  
Saeed Nezamivand Chegini ◽  
Ahmad Bagheri ◽  
Farid Najafi
Keyword(s):  
Optimization Problems ◽  
Lévy Flight ◽  
Levy Flight ◽  
Sine Cosine Algorithm

scholarly journals Application Mapping using Cuckoo Search Optimization with Lévy Flight for NoC based system

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Muhammad Junaid Mohiz ◽  
Naveed Khan Baloch ◽  
Fawad Hussain ◽  
Sharoon Saleem ◽  
Yousaf Bin Zikria ◽  
...  
Keyword(s):  
Cuckoo Search ◽  
Lévy Flight ◽  
Levy Flight ◽  
Application Mapping ◽  
Search Optimization ◽  
Cuckoo Search Optimization

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.

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