Genetic Clustering Algorithm Research Articles

A Novel Support Vector Machine Model of Traffic State Identification of Urban Expressway Integrating Parallel Genetic and C-Means Clustering Algorithm

A Novel Support Vector Machine Model of Traffic State Identification of Urban Expressway Integrating Parallel Genetic and C-Means Clustering Algorithm

Derived Multi-population Genetic Algorithm for Adaptive Fuzzy C-Means Clustering

Derived Multi-population Genetic Algorithm for Adaptive Fuzzy C-Means Clustering

The Implementation of Multiobjective Flexible Workshop Scheduling Based on Genetic Simulated Annealing-Inspired Clustering Algorithm

Multiobjective flexible workshop scheduling is an important subject to improve resource utilization and production efficiency and enhance the competitiveness of enterprises. As the situation of resource constraints becomes more and more severe, the problem of companies rationally allocating limited resources in production is becoming more and more serious. Today, the manufacturing industry widely adopts advanced manufacturing modes such as computer-integrated manufacturing and intelligent manufacturing, but in these semi-intelligent manufacturing modes with a high degree of uncertainty and a high degree of personnel dependence, it is difficult to adapt to the work of large-scale production. Therefore, suitable clustering algorithms are urgently needed to help solve these problems, and this paper selects a clustering algorithm based on the genetic simulation annealing algorithm. This article is aimed at studying the problem of efficiency improvement in the production process of large-scale manufacturing and at finding a stronger and more effective production mode for the manufacturing industry. Firstly, this paper introduces the basic principles of simulated annealing genetic algorithm and regularized clustering algorithm. These algorithms have excellent performance in searching for global optimal solutions. They can be constantly tested and computed to keep the calculation results close to the global optimal solution. In this paper, the K -means clustering algorithm is used to select the shortest completion time to represent the clustering target. According to the minimum distance principle, the machine, workpiece, and other objects are input into the clustering of the algorithm, and the K -means algorithm will send out the sorting plan. Therefore, a multiobjective flexible job shop scheduling model based on genetic simulated annealing algorithm and clustering algorithm is established. Then, by using hypothetical production data to simulate the operation of the workshop, the scheduling model was applied to conduct a deduction and empirical comparative study. The experimental results showed that the model shortened the completion time of the workpiece by 4.4% and increased the average load rate of the machine by 10%.

Rapid Site Selection of Shale Gas Multi-Well Pad Drilling Based on Digital Elevation Model

Drilling and completion platform construction is a fundamental part of oil and gas reservoir development, and the location of construction directly affects the whole process of shale gas drilling and development. Due to the complex surface conditions and fragile ecological environment in mountainous areas, having an appropriate platform location can significantly reduce shale gas development and environmental costs. The DEM (digital elevation model) includes geographic elevation, surface complexity, land use type, and other data, so it can be used for rapid site selection for shale gas multi-well pad drilling. In this study, first, research results related to drilling platform site selection were analyzed and summarized, and then a platform site selection method aiming to minimize the total well construction cost was developed. Second, the well construction costs were decomposed into the surface construction costs and the underground construction costs, and the site selection model with the lowest total multi-well pad construction costs was established. Third, ground feature data obtained from DEM (digital elevation model) processing were substituted into the site selection model and solved using the genetic clustering algorithm. Finally, two practical cases were used to verify the research method developed in this study. The results show that the platform site selection results can be used to not only guide the formulation of development plans, but also to reduce the scope of the field investigation in the process of site selection, reduce the intensity of field work, and improve the work efficiency.

A novel approach for clustering and routing in WSN using genetic algorithm and equilibrium optimizer

SummaryThe Internet of Things (IoT) is a new concept in the world of technology and information and has many applications in industry, communications, and various other fields. In the lowest layer of the IoT, wireless sensor networks (WSNs) play an important and pivotal role. WSN consists of a large number of sensors and is commonly used to monitor a target. It is important to reduce energy consumption in WSNs to extend network life, since it is usually impossible to replace sensor batteries. In this paper, a novel clustering and routing method is proposed. It is mainly based on genetic algorithms and equilibrium optimization. To improve scalability, the sensor nodes are clustered in the first phase using the genetic algorithm and then the best cluster heads are selected. It follows by the next step, where each node sends its collected data to the cluster head. The cluster heads first aggregate the received data and then use a multihop optimization routing algorithm by the new equilibrium optimization algorithm to send the data to the base station. Specifically, the main contribution of this work is to reduce energy consumption in WSN using a genetic algorithm for clustering and an equilibrium optimization algorithm for choosing the path between the cluster heads and the base station. The proposed method was simulated in MATLAB software, and it was compared with five other methods. It was observed that the proposed method outperforms other methods in terms of energy consumption, network lifespan, and the number of packages delivered.

Evidence of fine-scale genetic structure for reef manta rays Mobula alfredi in New Caledonia

Our understanding of the genetic connectivity of manta ray populations and the drivers that shape genetic population structure is still limited. This information is crucial to identify the spatial boundaries of discrete populations and guide decisions on units to conserve. In this study, we used genome-wide single nucleotide polymorphisms (SNPs) to assess the genetic structure and diversity of reef manta raysMobula alfrediat a local scale within New Caledonia and regionally in the western Pacific Ocean. We provide the first evidence of fine-scale genetic differentiation inM.alfredi, found between the 3 cleaning station aggregation sites in New Caledonia (n = 65 samples, N = 2676 SNPs,FST= 0.01, p < 0.0001). Furthermore, population structure was evident at the regional scale between individuals from New Caledonia (n = 73) and East Australia (n = 19) on the basis of genetic differentiation statistics (3619 SNPs,FST= 0.096, p < 0.0001) and clustering algorithms, with unidirectional gene flow detected from east (New Caledonia) to west (Australia). These results reveal that reef manta rays can form genetically distinct groups within a relatively small geographic range and highlights the need to consider genetic structure when designating management units for conservation action and planning.

Fairness-aware genetic-algorithm-based few-shot classification.

Artificial-intelligence-assisted decision-making is appearing increasingly more frequently in our daily lives; however, it has been shown that biased data can cause unfairness in decision-making. In light of this, computational techniques are needed to limit the inequities in algorithmic decision-making. In this letter, we present a framework to join fair feature selection and fair meta-learning to do few-shot classification, which contains three parts: (1) a pre-processing component acts as an intermediate bridge between fair genetic algorithm (FairGA) and fair few-shot (FairFS) to generate the feature pool; (2) the FairGA module considers the presence or absence of words as gene expression, and filters out key features by a fairness clustering genetic algorithm; (3) the FairFS part carries out the task of representation and fairness constraint classification. Meanwhile, we propose a combinatorial loss function to cope with fairness constraints and hard samples. Experiments show that the proposed method achieves strong competitive performance on three public benchmarks.

Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive Engine

Multiparameter optimization of complex electromechanical systems in a physical space is a challenging task. CPS (Cyberphysical system) technology can speed up the solution of the problem based on data interaction and collaborative optimization of physical space and cyberspace. This paper proposed a general multiparameter optimization framework by combining physical process simulation and clustering genetic algorithm for the CPS application. The utility of this approach is demonstrated in the instance of automobile engine energy-saving in this paper. A 1.8-L turbocharged GDI (gasoline direct injection) engine model was established and calibrated according to the test data and physical entity. A joint simulation program combining CGA (Clustering Genetic Algorithm) with the GDI engine simulation model was set up for the engine multiparameter optimization and performance prediction in cyberspace; then, the influential mechanism of multiple factors on engine energy-saving optimization was analyzed at 2000 RPM (Revolutions Per Minute) working condition. A multiparameter optimization with clustering genetic algorithm was introduced for multiparameter optimization among physical and digital data. The trade-off between fuel efficiency, dynamic performance, and knock risk was discussed. The results demonstrated the effectiveness of the proposed method and that it can contribute to develop a novel automotive engine control strategy in the future.

Diversification, spread, and admixture of octoploid strawberry in the Western Hemisphere.

PremisePolyploid species often have complex evolutionary histories that have, until recently, been intractable due to limitations of genomic resources. While recent work has further uncovered the evolutionary history of the octoploid strawberry (Fragaria L.), there are still open questions. Much is unknown about the evolutionary relationship of the wild octoploid species, Fragaria virginiana and Fragaria chiloensis, and gene flow within and among species after the formation of the octoploid genome.MethodsWe leveraged a collection of wild octoploid ecotypes of strawberry representing the recognized subspecies and ranging from Alaska to southern Chile, and a high‐density SNP array to investigate wild octoploid strawberry evolution. Evolutionary relationships were interrogated with phylogenetic analysis and genetic clustering algorithms. Additionally, admixture among and within species is assessed with model‐based and tree‐based approaches.ResultsPhylogenetic analysis revealed that the two octoploid strawberry species are monophyletic sister lineages. The genetic clustering results show substructure between North and South American F. chiloensis populations. Additionally, model‐based and tree‐based methods support gene flow within and among the two octoploid species, including newly identified admixture in the Hawaiian F. chiloensis subsp. sandwicensis population.Conclusions F. virginiana and F. chiloensis are supported as monophyletic and sister lineages. All but one of the subspecies show extensive paraphyly. Furthermore, phylogenetic relationships among F. chiloensis populations supports a single population range expansion southward from North America. The inter‐ and intraspecific relationships of octoploid strawberry are complex and suggest substantial gene flow between sympatric populations among and within species.

Green Vehicle Routing Under Customer Demand Uncertainty

customer satisfaction is the main focus area in supply chain management and the distribution of goods plays a vital role in customer satisfaction. cost optimization and in time delivery leads to customer satisfaction. Optimization of Vehicle route plan is the method generally applied to deal it. such plans shall consider the minimization of pollution emissions. This paper proposes a optimization method to handle the vehicle routing problem(vrp). Genetic algorithm and fuzzy clustering algorithm are applied in the method.

Market Segmentation of Innovative Products Using Genetic Algoritms

One way to increase the company's competitiveness is to find new market niches. The market niche is the result of innovations that stimulate hidden, potential demand, as a result of which the company, developing a new market, avoids intense competition and receives a higher rate of return. It is proved that the growing number and complexity of tasks in the field of marketing research, working with a large amount of information, leads to the need to group data. The aim of the study is to develop a universal approach to solving the problem of market segmentation of innovative products based on a combination of genetic algorithm with traditional clustering methods. An ideal market niche can be defined as a compact and isolated series of points, which in some space of characteristics are objects or data elements. The selection of a market niche in the medical equipment market is carried out using a top-down approach. This approach implies the traditional segmentation of customers, which is carried out in the following order: segmentation, segment selection, positioning. It is believed that segmentation is the starting point for the formation of a market niche. To segment the medical equipment market, it is proposed to use cluster analysis methods. According to the results of the analysis, it can be seen that the market segments of potential consumers of medical equipment and consumables of Siemens in Ukraine are characterized by a fairly dense grouping of images of consumers around the center of its cluster in the space of features. The presented genetic clustering algorithm is flexible in relation to the decision-making process, as it allows to perform clustering based on various criteria, such as maximum mutual removal of clusters, proximity of geometric images of objects to the center of the cluster, other criteria. This is achieved by changing the calculation formula of the fitness function, which takes into account the necessary combination of clustering criteria without changing the structure of the algorithm. The algorithm is insensitive to initialization, as in the process of evolution of chromosomes through the use of genetic operators, the algorithm completely covers the whole set of acceptable solutions, which, in turn, provides high quality market segmentation.

Construction of Student Information Management System Based on Data Mining and Clustering Algorithm

Data mining is a new technology developed in recent years. Through data mining, people can discover the valuable and potential knowledge hidden behind the data and provide strong support for scientifically making various business decisions. This paper applies data mining technology to the college student information management system, mines student evaluation information data, uses data mining technology to design student evaluation information modules, and digs out the factors that affect student development and the various relationships between these factors. Predictive assessment of knowledge and personalized teaching decision-making provide the basis. First, the general situation of genetic algorithm and fuzzy genetic algorithm is introduced, and then, an improved genetic fuzzy clustering algorithm is proposed. Compared with traditional clustering algorithm and improved genetic fuzzy clustering algorithm, the effectiveness of the algorithm proposed in this paper is proved. Based on the analysis system development related tools and methods, in response to the needs of the student information management system, a simple student information management system is designed and implemented, which provides a platform and data source for the next application of clustering algorithm for performance analysis. Finally, clustering the students’ scores with a clustering algorithm based on fuzzy genetic algorithm, the experimental results show that this method can better analyze the students’ scores and help relevant teachers and departments make decisions.

COMPARATIVE STUDY OF MUTATION OPERATORS IN THE GENETIC ALGORITHMS FOR THE K-MEANS PROBLEM

The k-means problem and the algorithm of the same name are the most commonly used clustering model and algorithm. Being a local search optimization method, the k-means algorithm falls to a local minimum of the objective function (sum of squared errors) and depends on the initial solution which is given or selected randomly. This disadvantage of the algorithm can be avoided by combining this algorithm with more sophisticated methods such as the Variable Neighborhood Search, agglomerative or dissociative heuristic approaches, the genetic algorithms, etc. Aiming at the shortcomings of the k-means algorithm and combining the advantages of the k-means algorithm and rvolutionary approack, a genetic clustering algorithm with the cross-mutation operator was designed. The efficiency of the genetic algorithms with the tournament selection, one-point crossover and various mutation operators (without any mutation operator, with the uniform mutation, DBM mutation and new cross-mutation) are compared on the data sets up to 2 millions of data vectors. We used data from the UCI repository and special data set collected during the testing of the highly reliable semiconductor components. In this paper, we do not discuss the comparative efficiency of the genetic algorithms for the k-means problem in comparison with the other (non-genetic) algorithms as well as the comparative adequacy of the k-means clustering model. Here, we focus on the influence of various mutation operators on the efficiency of the genetic algorithms only.

MWCSGA-Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network.

Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput.

Enhancement of Mutation Testing via Fuzzy Clustering and Multi-Population Genetic Algorithm

Mutation testing, a fundamental software testing technique, which is a typical way to evaluate the adequacy of a test suite. In mutation testing, a set of mutants are generated by seeding the different classes of faults into a program under test. Test data shall be generated in the way that as many mutants can be killed as possible. Thanks to numerous tools to implement mutation testing for different languages, a huge amount of mutants are normally generated even for small-sized programs. However, a large number of mutants not only leads to a high cost of mutation testing, but also make the corresponding test data generation a non-trivial task. In this paper, we make use of intelligent technologies to improve the effectiveness and efficiency of mutation testing from two perspectives. A machine learning technique, namely fuzzy clustering, is applied to categorize mutants into different clusters. Then, a multi-population genetic algorithm via individual sharing is employed to generate test data for killing the mutants in different clusters in parallel when the problem of test data generation as an optimization one. A comprehensive framework, termed as <inline-formula><tex-math notation="LaTeX">$\mathbf {FUZGENMUT}$</tex-math></inline-formula> , is thus developed to implement the proposed techniques. The experiments based on nine programs of various sizes show that fuzzy clustering can help to reduce the cost of mutation testing effectively, and that the multi-population genetic algorithm improves the efficiency of test data generation while delivering the high mutant-killing capability. The results clearly indicate that the huge potential of using intelligent technologies to enhance the efficacy and thus the practicality of mutation testing.

A Novel Active Equalization Method for Series-Connected Battery Packs Based on Clustering Analysis With Genetic Algorithm

Battery pack performance is the main concern for electric vehicles and energy storage systems. However, charge imbalance is inevitable due to inconsistent manufacturing techniques and environmental conditions. Charge imbalance reduces the power performance and available energy of battery packs. Hence, it is necessary to perform battery equalization. This article proposes an active equalization circuit and a novel equalization strategy to achieve energy redistribution. A bidirectional equalization topology consisting of a forward transformer and switch matrix is proposed first. Then, an innovative equalization strategy based on clustering analysis and genetic algorithm (GA) is developed. Clustering analysis is introduced to identify the target cells to be balanced. To further increase the speed of the equalization process, GA is employed to optimize the classification results. Finally, a series of experiments were conducted to confirm the effectiveness of the proposed topology and the strategy. Both the simulation and experimental results validate that the proposed equalization strategy not only improves the inconsistency but also increases the equalization speed. In practical battery pack experiments, the pack capacity is improved by 16.84% after equalization, and the equalization time is decreased by 23.8% using the proposed method.

A two stage converging genetic algorithm for graph clustering

ABSTRACT Graph clustering is a classical problem, and is proven to be NP-complete. It is at the core of many useful algorithms, like Network and VLSI design, computer graphics, data mining etc. In recent years, with exponential increase in the use of social network and strong incentive for creating applications exploiting the information hidden in these networks, clustering of large graphs (social networks) has become an important research topic. As the problem is NP-complete, various heuristic algorithms are proposed to find near optimal solutions efficiently. Optimization criteria are defined depending on the applications. Two important criteria for all heuristic algorithms are quality of the result and its stability over different runs on the same problem. In this work, we proposed a two stage genetic algorithm for network clustering. Modularity index for the partitioned graph is the criterion to optimize. By experimenting with several real-life networks, we have shown that our algorithm is stable and delivers a high modularity partitioning compared to other competitive heuristic algorithms. The stability of the algorithm is analyzed through simulations.

A framework for analyzing city-wide impact of building-integrated renewable energy

As the building-integrated renewables increase in the urban energy mix, it is important to assess the collective effects of building-integrated renewables at city scale. Estimation of the total energy supply and capacity of renewables, variations in the energy from the grid, and CO2 emission would aid renewable energy policy evaluation. However, analyzing the collective effects considering the optimal energy system for each building in a city is difficult due to a large number of buildings (on the order of 105). Therefore, this study proposes a new framework for analyzing the city-wide impact of increased building-integrated renewables. To reduce the number of optimization, clusters of buildings with similar characteristics and one virtual representative building for each cluster are generated. For apartment buildings, the characteristics are the floor area and roof area per household. For non-residential buildings, the characteristics are the shapes of the monthly electricity and gas usages, the ratio between the annual gas and electricity usages, and normalized roof area. To generate clusters of similar non-residential buildings, k-Means Clustering Algorithm and Genetic Algorithm have been applied. The proposed framework has been validated by comparing the collective results from i) optimization of 4,425 actual apartment buildings and 2,779 actual non-residential buildings in an urban district; and ii) optimization of corresponding 957 representative apartment buildings and 176 representative non-residential buildings. Total energy supply and capacity of each renewable energy source, total monthly electricity and gas from the grid, and total hourly electricity from the grid show good agreement. As a demonstration, the proposed framework has been applied to the city of Seoul, Korea for a future scenario of building energy obligation – i) to estimate the total capacities and energy supply of the building-integrated renewables and the change in the energy from the grid; and ii) to evaluate the cost-effectiveness of the obligation based on the unit cost of CO2 reduction for varying renewable energy requirements for the buildings.

Genetic Diversity and Connectivity of Southern Right Whales (Eubalaena australis) Found in the Brazil and Chile–Peru Wintering Grounds and the South Georgia (Islas Georgias del Sur) Feeding Ground

As species recover from exploitation, continued assessments of connectivity and population structure are warranted to provide information for conservation and management. This is particularly true in species with high dispersal capacity, such as migratory whales, where patterns of connectivity could change rapidly. Here we build on a previous long-term, large-scale collaboration on southern right whales (Eubalaena australis) to combine new (nnew) and published (npub) mitochondrial (mtDNA) and microsatellite genetic data from all major wintering grounds and, uniquely, the South Georgia (Islas Georgias del Sur: SG) feeding grounds. Specifically, we include data from Argentina (npub mtDNA/microsatellite = 208/46), Brazil (nnew mtDNA/microsatellite = 50/50), South Africa (nnew mtDNA/microsatellite = 66/77, npub mtDNA/microsatellite = 350/47), Chile–Peru (nnew mtDNA/microsatellite = 1/1), the Indo-Pacific (npub mtDNA/microsatellite = 769/126), and SG (npub mtDNA/microsatellite = 8/0, nnew mtDNA/microsatellite = 3/11) to investigate the position of previously unstudied habitats in the migratory network: Brazil, SG, and Chile–Peru. These new genetic data show connectivity between Brazil and Argentina, exemplified by weak genetic differentiation and the movement of 1 genetically identified individual between the South American grounds. The single sample from Chile–Peru had an mtDNA haplotype previously only observed in the Indo-Pacific and had a nuclear genotype that appeared admixed between the Indo-Pacific and South Atlantic, based on genetic clustering and assignment algorithms. The SG samples were clearly South Atlantic and were more similar to the South American than the South African wintering grounds. This study highlights how international collaborations are critical to provide context for emerging or recovering regions, like the SG feeding ground, as well as those that remain critically endangered, such as Chile–Peru.

Multi-order scheduling optimization considering product operation and worker allocation in divisional seru

This paper focuses on the allocation and scheduling problem in divisional seru production system, which is a new production mode originating in Japan and has been proved to be both efficient and flexible in practice. According to the production process, the allocation and scheduling problem considered in this paper includes worker allocation, operation allocation and order scheduling. A bi-objective nonlinear model is established to minimise the total completion time and optimise the production balance. Subsequently, since seru scheduling is a natural NP-hard problem, therefore, the solution method which combines the clustering analysis and genetic algorithm is designed. Finally, the numerical example is presented to verify the effectiveness of proposed model and solution method. The experimental results show that the satisfactory production scheduling scheme in divisional seru can be obtained effectively, and the performance of the divisional seru production system is better than that of the assembly line.