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Improved frog leaping algorithm Using Cellular Learning Automata

INTERNATIONAL JOURNAL OF ENGINEERING 2014年第1期27卷 1-6页

作者： Ranjkesh, S. Islamic Azad Univ Roudsar Amlash Branch Roudsar Iran

In this paper, a new algorithm which is the result of combination of cellular learning automata (CLA) and shuffled frog leap algorithm (SFLA) is proposed for optimization of functions in continuous, static environments. In the frog leaping algorithm, every frog represents a feasible solution within the problem space. In the proposed algorithm, each memeplex of frogs is placed in a cell of CLA. Learning automata in each cell acts as the brain of memeplex and will determine the strategy of motion and search. The proposed algorithm along with the standard SFLA and two global and local versions of particle swarm optimization algorithm have been tested in 30-dimensional space on five standard merit functions. Experimental results show that the proposed algorithm has a performance of the introduced algorithm is due to the control of search behavior of frogs during the optimization process.

关键词： frog leaping algorithm Optimization Cellular Learning Automata

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TLP: Towards three-level loop parallelisation

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IET COMPUTERS AND DIGITAL TECHNIQUES 2022年第5-6期16卷 159-171页

作者： Mahjoub, Shabnam Golsorkhtabaramiri, Mehdi Amiri, Seyed Sadegh Salehi Islamic Azad Univ Dept Comp Engn Babol Branch 3th Kilometer Qaemshahr Rd Babol *** Mazandaran Iran Islamic Azad Univ Dept Math Babol Branch Babol Iran

Due to the design of computer systems in the multi-core and/or multi-processor form, it is possible to use the maximum capacity of processors to run an application with the least time consumed through parallelisation. This is the responsibility of parallel compilers, which perform parallelisation in several steps by distributing iterations between different processors and executing them simultaneously to achieve lower runtime. The present paper focuses on the uniformisation of three-level perfect nested loops as an important step in parallelisation and proposes a method called Towards Three-Level Loop Parallelisation (TLP) that uses a combination of a frog leaping algorithm and Fuzzy to achieve optimal results because in recent years, many algorithms have worked on volumetric data, that is, three-dimensional spaces. Results of the implementation of the TLP algorithm in comparison with existing methods lead to a wide variety of optimal results at desired times, with minimum cone size resulting from the vectors. Besides, the maximum number of input dependence vectors is decomposed by this algorithm. These results can accelerate the process of generating parallel codes and facilitate their development for High-Performance Computing purposes.

关键词： frog leaping algorithm fuzzy parallelization three-level loops uniformisation

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A New Combination Method for Improving Parallelism in Two and Three Level Perfect Nested Loops

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IEEE ACCESS 2022年 10卷 74542-74554页

作者： Mahjoub, Shabnam Golsorkhtabaramiri, Mehdi Amiri, Seyed Sadegh Salehi Hosseinzadeh, Mehdi Mosavi, Amir Islamic Azad Univ Babol Branch Dept Comp Engn Babol Iran Islamic Azad Univ Babol Branch Dept Math Babol Iran Iran Univ Med Sci Psychosocial Hlth Res Inst Mental Hlth Res Ctr Tehran Iran Univ Human Dev Dept Comp Sci Sulaymaniyah Iraq Obuda Univ John Von Neumann Fac Informat H-1034 Budapest Hungary Univ Publ Serv Inst Informat Soc H-1083 Budapest Hungary Slovak Univ Technol Bratislava Inst Informat Engn Automat & Math Bratislava 81107 Slovakia

The growth of software techniques for implementing applications must go hand in hand with the growth of computer system hardware in the design of multi-core and multi-processor systems;otherwise, we cannot expect to be able to use maximum hardware capacities. One of the most important and challenging techniques for running applications is to run them in parallel with a focus on loop parallelism to reduce execution time. On the other hand, in recent years, many algorithms have been working on volumetric data, i.e., three-dimensional spaces;therefore, parallelization must be possible for all types of two-dimensional and three-dimensional loops. Uniformization is an important part of loop parallelism, and also the present paper's focus. The proposed algorithm in the present paper performed uniformization with a combination of the frog leaping algorithm and the fuzzy system for two- and three-dimensional loops on a wide range of input dependence vectors and achieved a considerable variety of results in the desired time. The results of this study can be used to facilitate the development of parallel codes.

关键词： Parallel processing Codes Program processors Runtime Task analysis Pluto Hardware frog leaping algorithm artificial intelligence parallelization loops uniformization parallel compilers

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IoT-enabled product development method to support rapid manufacturing using a nature-inspired algorithm

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JOURNAL OF MANAGEMENT & ORGANIZATION 2023年第4期29卷 632-654页

作者： Chen, Yu Hao, Shengbin Nazif, Habibeh Harbin Inst Technol Sch Management Harbin Heilongjiang Peoples R China Payame Noor Univ PNU Dept Math POB 19395-4697 Tehran Iran

Investigations illustrate that the Internet of Things (IoT) can save costs, increase efficiency, improve quality, and provide data-driven preventative maintenance services. Intelligent sensors, dependable connectivity, and complete integration are essential for gathering real-time information. IoT develops home appliances for improved customer satisfaction, personalization, and enhanced big data analytics as a crucial Industry 4.0 enabler. Because the product design process is an important part of controlling manufacturing, there are constant attempts to improve and minimize product design time. Utilizing a hybrid algorithm, this research provides a novel method to schedule design products in production management systems to optimize energy usage and design time (combined particle optimization algorithm and shuffled frog leaping algorithm). The issue with particle optimization algorithms is that they might become stuck in local optimization and take a long time to converge to global optimization. The strength of the combined frog leaping algorithm local searching has been exploited to solve these difficulties. The MATLAB programming tool is used to simulate the suggested technique. The simulation findings were examined from three perspectives: energy usage, manufacturing time, and product design time. According to the findings, the recommended strategy performed better in minimizing energy use and product design time. These findings also suggest that the proposed strategy has a higher degree of convergence when discovering optimal solutions.

关键词： frog leaping algorithm IoT manufacturing particle optimization algorithm product development

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The Study on Mobile Robot Path Planning based on frog leaping algorithm

The Study on Mobile Robot Path Planning based on Frog Leapin...

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2nd International Conference on Mechatronics and Intelligent Materials (MIM 2012)

作者： Zhang, Zhengran Yin, Jiying North China Univ Technol Coll Mech & Elect Engn Beijing Peoples R China

ISBN: (纸本)9783037853849

We have proposed a method of robot path planning in a partially unknown environment in this paper. We regard the problem of robot path planning as an optimization problem and solve it with the SFL algorithm. The position of globally best frog in each iterative is selected, and reached by the robot in sequence. The obstacles are detected by the robot sensors are applied to update the information of the environment. The optimal path is generated until the robot reaches its target. The simulation results validate the feasibility of the proposed method.

关键词： Path planning frog leaping algorithm Mobile robot

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Cuckoo search algorithm based on frog leaping local search and chaos theory

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APPLIED MATHEMATICS AND COMPUTATION 2015年 266卷 1083-1092页

作者： Liu, Xueying Fu, Meiling Shanghai Maritime Univ Coll Arts & Sci Shanghai 201306 Peoples R China Inner Mongolia Univ Technol Coll Sci Hohhot 010051 Peoples R China

Cuckoo algorithm is a novel optimization algorithm in the field of heuristic intelligence algorithms. Given the strong random leaping in solution space search, careful local searches are susceptible to falling into the local optimum. Thus, the latter phase of the optimization slows down and the accuracy diminishes. To improve the performance of the algorithm, this paper proposes an improved cuckoo search that utilizes chaos theory to enhance the variety of the initial population. Then, this study introduces inertia weight into the Levy flight random search to improve global searching capability. Finally, it applies the local search mechanism of the frog leaping algorithm to enhance local search and further improve the search speed and convergence precision of the algorithm. Typical test functions are employed to verify the performance of the improved algorithm. Comparison results with other algorithms indicate that the improved algorithm displays strong optimizing accuracy and high speed. Furthermore, this algorithm is confirmed to be convergent. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Cuckoo search Chaos theory frog leaping algorithm Inertia weight

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The UTFLA: uniformization of non-uniform iteration spaces in two-level perfect nested loops using SFLA

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JOURNAL OF SUPERCOMPUTING 2016年第6期72卷 2221-2234页

作者： Mahjoub, Shabnam Vojoudi, Hakimeh Islamic Azad Univ Dept Comp Engn Langaroud Branch Langaroud Iran

One of the factors increasing the execution time of computational programs is the loops, and parallelization of the loops is used to decrease this time. One of the steps of parallelizing compilers is uniformization of non-uniform loops in wavefront method which is considered as a NP-hard problem. In this paper, a new method has been presented to make uniform the non-uniform two-level perfect nested loops using the frog-leaping algorithm, called UTFLA, which is a combination of deterministic and stochastic methods, because the challenge most of loop paralleling methods, old or dynamic or new ones, face is the high algorithm execution time. UTFLA has been designed in a way to find the best results with the lowest amount of basic dependency cone size in the minimum possible time and gives more appropriate results in a more reasonable time compared to other methods.

关键词： Parallelizing compilers Uniformization Loops frog leaping algorithm

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Cancer Data Classification using a Fuzzy Classifier Based on Bio-Inspired algorithms

Cancer Data Classification using a Fuzzy Classifier Based on...

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IEEE International Conference on Fuzzy Systems

作者： Jamshid Pirgazi Ali Reza Khanteymoori Ali Amiri PhD Student Department of Computer Engineering University of Zanjan Zanjan Iran Assistant Professor Department of Computer Engineering University of Zanjan Zanjan Iran

ISBN: (纸本)9781467374293

Build classifier based on fuzzy rules for high-dimensional data sets, such as genetic data, are faced with great difficulties. An effective approach to this problem using feature selection techniques and dimension reduction methods. Hence, in this paper, using five different feature selection methods, size of data is reduced and the based on accuracy of the support vector machines classifier to this data a five dimensional feature vector extracted .then using frog leaping algorithm and genetic algorithm, With the aim of minimizing the number of rules and optimize the parameters of its a set of fuzzy rules for data classification are extracted. The proposed method was tested on five gene expression datasets. The experiments results show that the proposed method achieves higher accuracy than existing.

关键词： gene expression data fuzzy classifier frog leaping algorithm genetic algorithm Fuzzy rules Genetic algorithm (GA) data classification Classifiers Support Vector Network Feature extraction Eigenvector genetic information

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