Bölen: software module clustering method using the combination of shuffled frog leaping and genetic algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bahman Arasteh ◽  
Razieh Sadegi ◽  
Keyvan Arasteh
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithms ◽  
Structural Information ◽  
Convergence Speed ◽  
Software Module ◽  
Content Type ◽  
Clustering Quality ◽  
Software Modules ◽  
Shuffled Frog Leaping ◽  
Software Module Clustering

PurposeSoftware module clustering is one of the reverse engineering techniques, which is considered to be an effective technique for presenting software architecture and structural information. The objective of clustering software modules is to achieve minimum coupling among different clusters and create maximum cohesion among the modules of each cluster. Finding the best clustering is considered to be a multi-objective N-P hard optimization-problem, and for solving this problem, different meta-heuristic algorithms have been previously proposed. Achieving higher module lustering quality (MQ), obtaining higher success rate for achieving the best clustering quality and improving convergence speed are the main objectives of this study.Design/methodology/approachIn this study, a method (Bölen) is proposed for clustering software modules which combines the two algorithms of shuffled frog leaping and genetic algorithm.FindingsThe results of conducted experiments using traditional data sets confirm that the proposed method outperforms the previous methods in terms of convergence speed, module clustering quality and stability of the results.Originality/valueThe study proposes SFLA_GA algorithm for optimizing software module clustering, implementing SFLA algorithm in a discrete form by two operators of the genetic algorithm and achieving the above-mentioned purposes in this study. The aim is to achieve higher performance of the proposed algorithm in comparison with other algorithms.

An improved sparrow search algorithm based on levy flight and opposition-based learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Danni Chen ◽  
JianDong Zhao ◽  
Peng Huang ◽  
Xiongna Deng ◽  
Tingting Lu
Keyword(s):  
Parameter Optimization ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Support Vector ◽  
Lévy Flight ◽  
Levy Flight ◽  
Content Type ◽  
Opposition Based Learning

Purpose Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability. Design/methodology/approach To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems. Findings First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated. Originality/value An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

Search-Based Approaches for Software Module Clustering Based on Multiple Relationship Factors

2017 ◽  
Vol 27 (07) ◽  
pp. 1033-1062 ◽  
Author(s):  
Jimin Hwa ◽  
Shin Yoo ◽  
Yeong-Seok Seo ◽  
Doo-Hwan Bae
Keyword(s):  
Complex Systems ◽  
Open Source ◽  
Entire System ◽  
Single Factor ◽  
Software Module ◽  
Factor Module ◽  
Software Modules ◽  
Software Module Clustering ◽  
Relationship Factors ◽  
Or History

Software remodularization seeks to cluster software modules with high cohesion and low coupling: such a structure can help the comprehension and maintenance of complex systems. The modularization quality is usually captured using either structural, semantic, or history-based factors. All existing techniques apply a single factor to the entire system, which raises the following issues. First, a single factor may fail to capture the quality across the entire project: some modules may form semantic bondings, while others may form more structural ones. Second, the user of the technique has to choose a factor without knowing which one would perform the best. To resolve these issues, we propose a multi-factor module clustering, in which module clusters can be formed based on different factors. Our technique not only allows module clusters of different natures, but also relieve users from having to select a single factor. The paper introduces two different search-based formulations of multi-factor remodularization, and compares these against single-factor remodularization using four heterogeneous factors and six open source projects. The evaluation results show that the multi-factor remodularization can produce solutions that are 10.69% closer to the actual modularization adopted by the developers as compared with those produced by single-factor remodularization on average.

Tasks mapping in the network on a chip using an improved optimization algorithm

2020 ◽  
Vol 16 (2) ◽  
pp. 165-182
Author(s):  
Mehdi Darbandi ◽  
Amir Reza Ramtin ◽  
Omid Khold Sharafi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Content Type ◽  
Better Than

Purpose A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose. Design/methodology/approach In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC. Findings The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm. Originality/value As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

A hybrid genetic algorithm for multi-depot vehicle routing problem with considering time window repair and pick-up

2018 ◽  
Vol 13 (3) ◽  
pp. 698-717 ◽  
Author(s):  
Masoud Rabbani ◽  
Pooya Pourreza ◽  
Hamed Farrokhi-Asl ◽  
Narjes Nouri
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Heuristic Algorithms ◽  
Time Window ◽  
Hybrid Genetic Algorithm ◽  
Variable Cost ◽  
Routing Problem ◽  
Content Type ◽  
Simple Genetic Algorithm

Purpose This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW). Design/methodology/approach The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms, namely, simple genetic algorithm (GA) and hybrid genetic algorithm (HGA) are used to find the best solution for this problem. A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA. Findings A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA. Originality/value This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW). The defined problem is a practical problem in the supply management and logistic. The repair vehicle services the customers who have goods, while the pickup vehicle visits the customer with nonrepaired goods. All the vehicles belong to an internal fleet of a company and have different capacities and fixed/variable cost. Moreover, vehicles have different limitations in their time of traveling. The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms (simple genetic algorithm and hybrid one) are used to find the best solution for this problem.

scholarly journals Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem

2020 ◽  
Vol 2 (3) ◽  
pp. 161-177 ◽  
Author(s):  
Ashish Dwivedi ◽  
Ajay Jha ◽  
Dhirendra Prajapati ◽  
Nenavath Sreenu ◽  
Saurabh Pratap
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Network Design ◽  
Food Supply ◽  
Design Problem ◽  
Heuristic Algorithms ◽  
Network Design Problem ◽  
Content Type ◽  
Minlp Model ◽  
Food Grain

PurposeDue to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain.Design/methodology/approachA mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set.FindingsThe model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time.Research limitations/implicationsIn literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries.Originality/valueThe suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently.

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.

Multi-Start Jaya Algorithm for Software Module Clustering Problem

2018 ◽  
Vol 1 (1) ◽  
pp. 87-112 ◽  
Author(s):  
Kamal Z. Zamli ◽  
◽  
Abdulrahman Alsewari ◽  
Bestoun S. Ahmed ◽  
◽  
...  
Keyword(s):  
Jaya Algorithm ◽  
Software Module ◽  
Clustering Problem ◽  
Software Module Clustering

scholarly journals Discovering the maximum k-clique on social networks using bat optimization algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Akram Khodadadi ◽  
Shahram Saeidi
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Coding Theory ◽  
Evaluation Criteria ◽  
Convergence Speed ◽  
Optimization Approach ◽  
Computational Results ◽  
Complete Subgraph ◽  
Np Complete

AbstractThe k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of the problem, the meta-heuristic approaches have raised the interest of the researchers and some algorithms are developed. In this paper, a new algorithm based on the Bat optimization approach is developed for finding the maximum k-clique on a social network to increase the convergence speed and evaluation criteria such as Precision, Recall, and F1-score. The proposed algorithm is simulated in Matlab® software over Dolphin social network and DIMACS dataset for k = 3, 4, 5. The computational results show that the convergence speed on the former dataset is increased in comparison with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) approaches. Besides, the evaluation criteria are also modified on the latter dataset and the F1-score is obtained as 100% for k = 5.

scholarly journals The Structure of the Cysteine-Rich Domain of Plasmodium falciparum P113 Identifies the Location of the RH5 Binding Site

mBio ◽  
2020 ◽  
Vol 11 (5) ◽  
Author(s):  
Ivan Campeotto ◽  
Francis Galaway ◽  
Shahid Mehmood ◽  
Lea K. Barfod ◽  
Doris Quinkert ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Binding Site ◽  
Structural Information ◽  
Blood Stage ◽  
Site Directed Mutagenesis ◽  
Effective Vaccine ◽  
Binding Region ◽  
Fab Fragment ◽  
Content Type ◽  
Blood Stage Malaria

ABSTRACT Plasmodium falciparum RH5 is a secreted parasite ligand that is essential for erythrocyte invasion through direct interaction with the host erythrocyte receptor basigin. RH5 forms a tripartite complex with two other secreted parasite proteins, CyRPA and RIPR, and is tethered to the surface of the parasite through membrane-anchored P113. Antibodies against RH5, CyRPA, and RIPR can inhibit parasite invasion, suggesting that vaccines containing these three components have the potential to prevent blood-stage malaria. To further explore the role of the P113-RH5 interaction, we selected monoclonal antibodies against P113 that were either inhibitory or noninhibitory for RH5 binding. Using a Fab fragment as a crystallization chaperone, we determined the crystal structure of the RH5 binding region of P113 and showed that it is composed of two domains with structural similarities to rhamnose-binding lectins. We identified the RH5 binding site on P113 by using a combination of hydrogen-deuterium exchange mass spectrometry and site-directed mutagenesis. We found that a monoclonal antibody to P113 that bound to this interface and inhibited the RH5-P113 interaction did not inhibit parasite blood-stage growth. These findings provide further structural information on the protein interactions of RH5 and will be helpful in guiding the development of blood-stage malaria vaccines that target RH5. IMPORTANCE Malaria is a deadly infectious disease primarily caused by the parasite Plasmodium falciparum. It remains a major global health problem, and there is no highly effective vaccine. A parasite protein called RH5 is centrally involved in the invasion of host red blood cells, making it—and the other parasite proteins it interacts with—promising vaccine targets. We recently identified a protein called P113 that binds RH5, suggesting that it anchors RH5 to the parasite surface. In this paper, we use structural biology to locate and characterize the RH5 binding region on P113. These findings will be important to guide the development of new antimalarial vaccines to ultimately prevent this disease, which affects some of the poorest people on the planet.

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