ARAZ: A software modules clustering method using the combination of particle swarm optimization and genetic algorithms

2020 ◽  
pp. 1-14
Author(s):  
Bahman Arasteh ◽  
Razieh Sadegi ◽  
Keyvan Arasteh
Keyword(s):  
Particle Swarm Optimization ◽  
Success Rate ◽  
Source Code ◽  
Particle Swarm ◽  
Program Comprehension ◽  
Convergence Speed ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Clustering Quality ◽  
Software Modules

A considerable percentage of software costs are usually related to its maintenance. Program comprehension is a prerequisite of the software maintenance and a considerable time of maintainers is spent to comprehend the structure and behavior of the software when the source code is the only product available. Program comprehension is one of difficult and challenging task especially in the absence of design documents of the software system. Clustering of software modules is an effective reverse-engineering method for extracting the software architecture and structural model from the source code. Finding the best clustering is considered to be a multi-objective NP hard optimization-problem and different meta-heuristic algorithms have been used for solving this problem. Local optimum, insufficient quality, insufficient performance and insufficient stability are the main shortcomings of the previous methods. Attaining higher values for software clustering quality, attaining higher success rate in clustering of software modules, attaining higher stability of the obtained results and attaining the higher convergence (speed) to generate optimal clusters are the main goals of this study. In this study, a hybrid meta heuristic method (ARAZ)1 includes particle swarm optimization algorithm and genetic algorithm (PSO-GA) is proposed to find the best clustering of software modules. An extensive series of experiments on 10 standard benchmark programs have been conducted. Regarding the results of experiments, the proposed method outperforms the other methods in terms of clustering quality, stability, success rate and convergence speed.

Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means

2013 ◽  
Vol 760-762 ◽  
pp. 2194-2198 ◽  
Author(s):  
Xue Mei Wang ◽  
Yi Zhuo Guo ◽  
Gui Jun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Inertia Weight ◽  
Clustering Quality ◽  
Mutation Operation

Adaptive Particle Swarm Optimization algorithm with mutation operation based on K-means is proposed in this paper, this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization, the algorithm self-adaptively adjusted inertia weight according to fitness variance of population. Mutation operation was peocessed for the poor performative particle in population. The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy falling into the local optimum of K-Means, and more effectively improved clustering quality.

A Hybrid Meta-Heuristic Approach for QoS-Aware Cloud Service Composition

2018 ◽  
Vol 15 (2) ◽  
pp. 1-20 ◽  
Author(s):  
S. Bharath Bhushan ◽  
Pradeep C. H. Reddy
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fruit Fly ◽  
Convergence Speed ◽  
Optimization Techniques ◽  
Cloud Services ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Fitness Value

Cloud is evolving as an outstanding platform to deliver cloud services on a pay-as-you-go basis. The selection and composition of cloud services based on QoS criteria is formulated as NP hard optimization problem. Traditionally, many optimization techniques are applied to solve it, but it suffers from slow convergence speed, large number of calculations, and falling into local optimum. This article proposes a hybrid particle swarm optimization (HPSO) technique that combines particle swarm optimization (PSO) and fruit fly (FOA) to perform the evolutionary search process. The following determines a pareto optimal service set which is non-dominated solution set as input to the proposed HPSO. In the proposed HPSO, the parameters such as position and velocity are redefined, and while updating, the smell operator of fruit fly is used to overcome the prematurity of PSO. The FOA enhances the convergence speed with good fitness value. The experimental results show that the proposed HPSO outperforms the simple particle swarm optimization in terms of fitness value, execution time, and error rate.

scholarly journals An Improved Particle Swarm Optimization with Gaussian Disturbance

2018 ◽  
Vol 232 ◽  
pp. 03015
Author(s):  
Changjun Wen ◽  
Changlian Liu ◽  
Heng Zhang ◽  
Hongliang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Local Optimal Solution ◽  
Gaussian Disturbance ◽  
Griewank Function

The particle swarm optimization (PSO) is a widely used tool for solving optimization problems in the field of engineering technology. However, PSO is likely to fall into local optimum, which has the disadvantages of slow convergence speed and low convergence precision. In view of the above shortcomings, a particle swarm optimization with Gaussian disturbance is proposed. With introducing the Gaussian disturbance in the self-cognition part and social cognition part of the algorithm, this method can improve the convergence speed and precision of the algorithm, which can also improve the ability of the algorithm to escape the local optimal solution. The algorithm is simulated by Griewank function after the several evolutionary modes of GDPSO algorithm are analyzed. The experimental results show that the convergence speed and the optimization precision of the GDPSO is better than that of PSO.

A Novel Hybrid Optimization Method of Shuffled Frog Leaping Algorithm and Particle Swarm Optimization

2013 ◽  
Vol 717 ◽  
pp. 433-438 ◽  
Author(s):  
Mei Jin Lin ◽  
Fei Luo ◽  
Yu Ge Xu ◽  
Long Luo
Keyword(s):  
Particle Swarm Optimization ◽  
Information Exchange ◽  
Search Strategy ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Hybrid Optimization ◽  
Local Optimum ◽  
Shuffled Frog Leaping Algorithm ◽  
Swarm Optimization ◽  
Shuffled Frog Leaping

Shuffled frog leaping algorithm (SFLA) is a meta-heuristic algorithm, which combines the social behavior technique and the global information exchange of memetic algorithms. But the SFLA has the shortcoming of low convergence speed while solving complex optimization problems. Particle swarm optimization (PSO) is a fast searching algorithms, but easily falls into the local optimum for the diversity scarcity of particles. In the paper, a new hybrid optimization called SFLA-PSO is proposed, which introduced PSO to SFLA by combining the fast search strategy of PSO and global search strategy of SFLA. Six benchmark functions are selected to compare the performance of SFLA-PSO, basic PSO, wPSO and SFLA. The simulation results show that the proposed algorithm SFLA-PSO possesses outstanding performance in the convergence speed and the precision of the global optimum solution.

scholarly journals An improved atomic search algorithm for optimization and application in ML DOA estimation of vector hydrophone array

2022 ◽  
Vol 7 (4) ◽  
pp. 5563-5593
Author(s):  
Peng Wang ◽  
◽  
Weijia He ◽  
Fan Guo ◽  
Xuefang He ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Estimation Accuracy ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Search Optimization ◽  
Ml Estimator

<abstract><p>The atom search optimization (ASO) algorithm has the characteristics of fewer parameters and better performance than the traditional intelligent optimization algorithms, but it is found that ASO may easily fall into local optimum and its accuracy is not higher. Therefore, based on the idea of speed update in particle swarm optimization (PSO), an improved atomic search optimization (IASO) algorithm is proposed in this paper. Compared with traditional ASO, IASO has a faster convergence speed and higher precision for 23 benchmark functions. IASO algorithm has been successfully applied to maximum likelihood (ML) estimator for the direction of arrival (DOA), under the conditions of the different number of signal sources, different signal-to-noise ratio (SNR) and different population size, the simulation results show that ML estimator with IASO algorithum has faster convergence speed, fewer iterations and lower root mean square error (RMSE) than ML estimator with ASO, sine cosine algorithm (SCA), genetic algorithm (GA) and particle swarm optimization (PSO). Therefore, the proposed algorithm holds great potential for not only guaranteeing the estimation accuracy but also greatly reducing the computational complexity of multidimensional nonlinear optimization of ML estimator.</p></abstract>

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

scholarly journals Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History

2017 ◽  
Vol 2017 ◽  
pp. 1-22
Author(s):  
Danping Wang ◽  
Kunyuan Hu ◽  
Lianbo Ma ◽  
Maowei He ◽  
Hanning Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Statistical Tests ◽  
Particle Swarm ◽  
Local Optimum ◽  
Space Partitioning ◽  
Swarm Optimization ◽  
Real World Problem ◽  
Binary Space Partitioning ◽  
Solution Accuracy ◽  
Search History

A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.

Local Best Particle Swarm Optimization Using Crown Jewel Defense Strategy

2018 ◽  
pp. 27-52 ◽  
Author(s):  
Jiarui Zhou ◽  
Junshan Yang ◽  
Ling Lin ◽  
Zexuan Zhu ◽  
Zhen Ji
Keyword(s):  
Particle Swarm Optimization ◽  
High Efficiency ◽  
Particle Swarm ◽  
Global Optimum ◽  
Population Diversity ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Local Optima ◽  
Von Neumann ◽  
Swarm Intelligence Algorithm

Particle swarm optimization (PSO) is a swarm intelligence algorithm well known for its simplicity and high efficiency on various problems. Conventional PSO suffers from premature convergence due to the rapid convergence speed and lack of population diversity. It is easy to get trapped in local optima. For this reason, improvements are made to detect stagnation during the optimization and reactivate the swarm to search towards the global optimum. This chapter imposes the reflecting bound-handling scheme and von Neumann topology on PSO to increase the population diversity. A novel crown jewel defense (CJD) strategy is introduced to restart the swarm when it is trapped in a local optimum region. The resultant algorithm named LCJDPSO-rfl is tested on a group of unimodal and multimodal benchmark functions with rotation and shifting. Experimental results suggest that the LCJDPSO-rfl outperforms state-of-the-art PSO variants on most of the functions.

scholarly journals Power load forecasting algorithm based on nonlinear inertial factor change pattern particle swarm optimization algorithm

2018 ◽  
Vol 173 ◽  
pp. 02016
Author(s):  
Jin Liang ◽  
Wang Yongzhi ◽  
Bao Xiaodong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Support Vector ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Power Load ◽  
Selection Of

The common method of power load forecasting is the least squares support vector machine, but this method is very dependent on the selection of parameters. Particle swarm optimization algorithm is an algorithm suitable for optimizing the selection of support vector parameters, but it is easy to fall into the local optimum. In this paper, we propose a new particle swarm optimization algorithm, it uses non-linear inertial factor change that is used to optimize the algorithm least squares support vector machine to avoid falling into the local optimum. It aims to make the prediction accuracy of the algorithm reach the highest. The experimental results show this method is correct and effective.

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