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.

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 An Improved Moth-Flame Optimization Algorithm for Engineering Problems

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1234
Author(s):  
Yu Li ◽  
Xinya Zhu ◽  
Jingsen Liu
Keyword(s):  
Optimization Algorithm ◽  
Local Development ◽  
Global Search ◽  
Population Diversity ◽  
Lévy Flight ◽  
Test Functions ◽  
Levy Flight ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Moth Flame Optimization Algorithm

In this paper, an improved moth-flame optimization algorithm (IMFO) is presented to solve engineering problems. Two novel effective strategies composed of Lévy flight and dimension-by-dimension evaluation are synchronously introduced into the moth-flame optimization algorithm (MFO) to maintain a great global exploration ability and effective balance between the global and local search. The search strategy of Lévy flight is used as a regulator of the moth-position update mechanism of global search to maintain a good research population diversity and expand the algorithm’s global search capability, and the dimension-by-dimension evaluation mechanism is added, which can effectively improve the quality of the solution and balance the global search and local development capability. To substantiate the efficacy of the enhanced algorithm, the proposed algorithm is then tested on a set of 23 benchmark test functions. It is also used to solve four classical engineering design problems, with great progress. In terms of test functions, the experimental results and analysis show that the proposed method is effective and better than other well-known nature-inspired algorithms in terms of convergence speed and accuracy. Additionally, the results of the solution of the engineering problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces.

scholarly journals Novel Swarm Intelligence Algorithm for Global Optimization and Multi-UAVs Cooperative Path Planning: Anas Platyrhynchos Optimizer

2020 ◽  
Vol 10 (14) ◽  
pp. 4821
Author(s):  
Yong Zhang ◽  
Pengfei Wang ◽  
Liuqing Yang ◽  
Yanbin Liu ◽  
Yuping Lu ◽  
...  
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Local Development ◽  
Anas Platyrhynchos ◽  
Intelligent Algorithm ◽  
Intelligence Algorithm ◽  
Engineering Problems ◽  
The Mathematical Model ◽  
Swarm Intelligence Algorithm

In this study, a novel type of swarm intelligence algorithm referred as the anas platyrhynchos optimizer is proposed by simulating the cluster action of the anas platyrhynchos. Starting from the core of swarm intelligence algorithm, on the premise of the use of few parameters and ease in implementation, the mathematical model and algorithm flow of the anas platyrhynchos optimizer are given, and the balance between global search and local development in the algorithm is ensured. The algorithm was applied to a benchmark function and a cooperative path planning solution for multi-UAVs as a means of testing the performance of the algorithm. The optimization results showed that the anas platyrhynchos optimizer is more superior in solving optimization problems compared with the mainstream intelligent algorithm. This study provides a new idea for solving more engineering problems.

scholarly journals An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1637
Author(s):  
Mohammad H. Nadimi-Shahraki ◽  
Ali Fatahi ◽  
Hoda Zamani ◽  
Seyedali Mirjalili ◽  
Laith Abualigah
Keyword(s):  
Mechanical Engineering ◽  
Optimization Problems ◽  
Population Diversity ◽  
Local Optimum ◽  
Friedman Test ◽  
Exploration And Exploitation ◽  
Adaptation Mechanism ◽  
Local Optima ◽  
Engineering Problems ◽  
Moth Flame Optimization Algorithm

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO’s issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to find the best optimal solutions for mechanical engineering problems is evaluated with three problems from the latest test-suite CEC 2020. The experimental and statistical results demonstrate that the proposed I-MFO is significantly superior to the contender algorithms and it successfully upgrades the shortcomings of the canonical MFO.

scholarly journals A Survey on Dragonfly Algorithm and its Applications in Engineering

2020 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Computational Design ◽  
Heuristic Optimization ◽  
Future Trends ◽  
Excellent Performance ◽  
Comprehensive Investigation ◽  
Dragonfly Algorithm ◽  
Engineering Problems ◽  
Complex Optimization

<p>Dragonfly algorithm (DA) is one of the most recently developed heuristic optimization algorithms by Mirjalili in 2016. It is now one of the most widely used algorithms. In some cases, it outperforms the most popular algorithms. However, this algorithm is not far from obstacles when it comes to complex optimization problems. In this work, along with the strengths of the algorithm in solving real-world optimization problems, the weakness of the algorithm to optimize complex optimization problems is addressed. This survey presents a comprehensive investigation of DA in the engineering area. First, an overview of the algorithm is discussed. Additionally, the different variants of the algorithm are addressed too. The combined versions of the DA with other techniques and the modifications that have been done to make the algorithm work better are shown. Besides, a survey on applications in engineering area that used DA is offered. The algorithm is compared with some other metaheuristic algorithms to demonstrate its ability to optimize problems comparing to the others. The results of the algorithm from the works that utilized the DA in the literature and the results of the benchmark functions showed that in comparison with some other algorithms DA has an excellent performance, especially for small to medium problems. Moreover, the bottlenecks of the algorithm and some future trends are discussed. Authors conduct this research with the hope of offering beneficial information about the DA to the researchers who want to study the algorithm and utilize it to optimize engineering problems.</p><p><strong><br></strong></p><p><strong> Journal of Computational Design and Engineering, 2020.</strong></p><p><strong>DOI: 10.1093/jcde/qwaa037</strong></p>

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.

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