Local Fitness Function Research Articles

Active contour model based on local absolute difference energy and fractional-order penalty term

The active contour model (ACM) is one of the popular image segmentation methods. In practice, however, images are often corrupted by severe noise and intensity inhomogeneity, making traditional ACMs unavailable. In this paper, a novel ACM is presented to effectively cope with noise and intensity inhomogeneity. To better discriminate the object and background regions in inhomogeneous images, a new saliency fitting image (SFI), defined as the difference between the original image and the local-weighted image (LWI), is first constructed. In addition, the local mean absolute deviation (LMAD) based weight factor can reflect the degree of the intensity variation in a local region, and the LWI represents the bias component accounting for intensity inhomogeneity. Then, a local fitting energy term is defined as the absolute difference between two local fitting functions, which represent the local averages of image intensity inside/outside the contour in two windows with different sizes. And a threshold strategy is utilized to improve the robustness of the proposed model to noise. Finally, a fractional-order penalty term is defined to penalize the deviation between the level set function (LSF) and the signed distance function (SDF), which makes the proposed model avoid the side effect of the penalty energy term in traditional ACMs. Experimental results show the efficiency and accuracy of the proposed ACM.

Leakage detection in water distribution networks using space search reduction and local fitness in steady-state and EPS (case study: water distribution network of Birjand)

AbstractUsing hydraulic model calibration of a water distribution network is one of the methods that can reduce leak detection costs by identifying areas with high leakage. In the present paper, a new factor called local fitness is used in combination with the search space reduction method to obtain an optimal answer. In this method, a fitness function is calculated for each zone. For each zone, proximity of the pressure and flow rates obtained from the model to the corresponding observational data indicates that the values selected for the nodes of that zone are close to the correct values. In the next steps, using a search space reduction method and performing the optimization process with the local fitness function, the chance of selecting those values is increased. In this study, leakage was assigned to nodes with emitter coefficient. This method was used on the water distribution network of Birjand. Three factors of background leakage, leakage hotspot and apparent losses were considered as unknown parameters. Results of the present method were compared with the results of a genetic algorithm (GA) and the corresponding exact values. Based the results, the present method showed better results in terms of convergence speed and accuracy.

Theory of preference modelling for communities in scale-free networks

Detecting a community structure on networks is a problem of interest in science and many other domains. Communities are special structures which may consist nodes with some common features. The identification of overlapping communities can clarify not so apparent features about relationships among the nodes of a network. A node in a community can have a membership in a community with a different degree. Here, we introduce a fuzzy based approach for overlapping community detection. A special type of fuzzy operator is used to define the membership strength for the nodes of community. Fuzzy systems and logic is a branch of mathematics which introduces many-valued logic to compute the truth value. The computed truth can have a value between 0 and 1. The preference modelling approach introduces some parameters for designing communities of particular strength. The strength of a community tells us to what degree each member of community is part of a community. As for relevance and applicability of the community detection method on different types of data and in various situations, this approach generates a possibility for the user to be able to control the overlap regions created while detecting the communities. We extend the existing methods which use local function optimization for community detection. The LFM method uses a local fitness function for a community to identify the community structures. We present a community fitness function in pliant logic form and provide mathematical proofs of its properties, then we apply the preference implication of continuous-valued logic. The preference implication is based on two important parameters nu and alpha. The parameter nu of the preference-implication allows us to control the design of the communities according to our requirement of the strength of the community. The parameter alpha defines the sharpness of preference implication. A smaller value of the threshold for community membership creates bigger communities and more overlapping regions. A higher value of community membership threshold creates stronger communities with nodes having more participation in the community. The threshold is controlled by delta which defines the degree of relationship of a node to a community. To balance the creation of overlap regions, stronger communities and reducing outliers we choose a third parameter delta in such a way that it controls the community strength by varying the membership threshold as community evolves over time. We test the theoretical model by conducting experiments on artificial and real scale-free networks. We test the behaviour of all the parameters on different data-sets and report the outliers found. In our experiments, we found a good relationship between nu and overlapping nodes in communities.

Universal Evolutionary Model for Periodical Species

Real-world examples of periodical species range from cicadas, whose life cycles are large prime numbers, like 13 or 17, to bamboos, whose periods are large multiples of small primes, like 40 or even 120. The periodicity is caused by interaction of species, be it a predator-prey relationship, symbiosis, commensalism, or competition exclusion principle. We propose a simple mathematical model, which explains and models all those principles, including listed extremal cases. This rather universal, qualitative model is based on the concept of a local fitness function, where a randomly chosen new period is selected if the value of the global fitness function of the species increases. Arithmetically speaking, the different interactions are related to only four principles: given a couple of integer periods either (1) their greatest common divisor is one, (2) one of the periods is prime, (3) both periods are equal, or (4) one period is an integer multiple of the other.

Enhancement in resource allocation system for cloud environment using modified grey wolf technique

<p class="normal">Cloud computing is an advanced technology which provides services with the help of internet. These services work under the rule of pay and gain. The services consist of hardware and software used in different fields of life. Due to growth of cloud computing the number of users are increased and their demand for better services also increased with the passage of time. Cloud computing faces different issues. One of them is resource scheduling. In this paper a new technique is used for improvement of scheduling in cloud computing. The improvement took place in GWO algorithm. Two main sections of this algorithm are modified, which are local search section and fitness function value. The above proposed technique is used to improve three main parameters of scheduling that are energy consumption, throughput and average network executation time in VM for cloud computing. The techniques results are compared with ABC algorithm and GWO algorithm. The results show that proposed algorithm improves in the three main parameters of scheduling for cloud computing.</p>

Evolutionary Divide-and-Conquer Algorithm for Virus Spreading Control Over Networks

The control of virus spreading over complex networks with a limited budget has attracted much attention but remains challenging. This article aims at addressing the combinatorial, discrete resource allocation problems (RAPs) in virus spreading control. To meet the challenges of increasing network scales and improve the solving efficiency, an evolutionary divide-and-conquer algorithm is proposed, namely, a coevolutionary algorithm with network-community-based decomposition (NCD-CEA). It is characterized by the community-based dividing technique and cooperative coevolution conquering thought. First, to reduce the time complexity, NCD-CEA divides a network into multiple communities by a modified community detection method such that the most relevant variables in the solution space are clustered together. The problem and the global swarm are subsequently decomposed into subproblems and subswarms with low-dimensional embeddings. Second, to obtain high-quality solutions, an alternative evolutionary approach is designed by promoting the evolution of subswarms and the global swarm, in turn, with subsolutions evaluated by local fitness functions and global solutions evaluated by a global fitness function. Extensive experiments on different networks show that NCD-CEA has a competitive performance in solving RAPs. This article advances toward controlling virus spreading over large-scale networks.

Image Segmentation Using an Active Contour Model Based on the Difference Between Local Intensity Averages and Actual Image Intensities

The local intensity fitting active contour models can handle inhomogeneous images, but they suffer from the shortcomings of poor performance in segmenting images with severe intensity inhomogeneity and being sensitive to initializations. To overcome these problems, we put forward a robust active contour model by introducing two adjustment coefficient functions. The energy functional of the proposed model is presented by integrating the local fitting term and two adjustment coefficient functions. The local fitting term is defined by introducing two local fitting functions that approximate the image intensities inside and outside of the contour. These two adjustment coefficient functions, which improve the segmentation performance and enhance the robustness to initialization, are constructed by utilizing the Sigmoid function as well as the difference between local intensity averages and image actual intensities. The results of the experiments on synthetic and real images demonstrate that the presented model not only is capable of handling intensity inhomogeneity better under more flexible initializations but also takes less time in comparison with other region-based models. Furthermore, these two adjustment coefficients can be employed to other local intensity fitting models to enhance the robustness to initialization and to decrease the segmentation time.

A robust active contour model driven by fuzzy c-means energy for fast image segmentation

In this paper, we propose a robust region-based active contour model driven by fuzzy c-means energy that draws upon the clustering intensity information for fast image segmentation. The main idea of fuzzy c-means energy is to quickly compute the two types of cluster center functions for all points in image domain by fuzzy c-means algorithm locally with a proper preprocessing procedure before the curve starts to evolve. The time-consuming local fitting functions in traditional models are substituted with these two functions. Furthermore, a sign function and a Gaussian filtering function are utilized to replace the penalty term and the length term in most models, respectively. Experiments on several synthetic and real images have proved that the proposed model can segment images with intensity inhomogeneity efficiently and precisely. Moreover, the proposed model has a good robustness on initial contour, parameters and different kinds of noise.

Active contour model based on fuzzy c‐means for image segmentation

In this Letter, the authors propose an active contour model based on the fuzzy c-means method. Using fuzzy c-means to calculate the values of two types of cluster centres for all points in the image domain, by which local fitting functions in traditional models are substituted. Since the values are calculated before curve evolution, the proposed model can segment images with intensity inhomogeneity efficiently. Furthermore, it has the good robustness to initial contour. The effectiveness of the proposed model is tested by experiments on some synthetic and real images.

Community Detection Based on Differential Evolution Using Social Spider Optimization

Community detection (CD) has become an important research direction for data mining in complex networks. Evolutionary algorithm-based (EA-based) approaches, among many other existing community detection methods, are widely used. However, EA-based approaches are prone to population degradation and local convergence. Developing more efficient evolutionary algorithms thus becomes necessary. In 2013, Cuevas et al. proposed a new differential evolution (DE) hybrid meta-heuristic algorithm based on the simulated cooperative behavior of spiders, known as social spider optimization (SSO). On the basis of improving the SSO algorithm, this paper proposes a community detection algorithm based on differential evolution using social spider optimization (DESSO/CD). In this algorithm, the CD detection process is done by simulating the spider cooperative operators, marriage, and operator selection. The similarity of nodes is defined as local fitness function; the community quality increment is used as a screening criterion for evolutionary operators. Populations are sorted according to their contribution and diversity, making evolution even more different. In the entire process, a random cloud crossover model strategy is used to maintain population diversity. Each generation of the mating radius of the SSO algorithm will be adjusted appropriately according to the iterative times and fitness values. This strategy not only ensures the search space of operators, but also reduces the blindness of exploration. On the other hand, the multi-level, multi-granularity strategy of DESSO/CD can be used to further compensate for resolution limitations and extreme degradation defects based on modular optimization methods. The experimental results demonstrate that the DESSO/CD method could detect the community structure with higher partition accuracy and lower computational cost when compared with existing methods. Since the application of the SSO algorithm in CD research is just beginning, the study is competitive and promising.

Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

Drought Monitoring by remotely sensed moisture vegetation indexes is being an active research subject as the vegetation spectral responses are showed to be highly correlated to water content. The MODIS (MODerate resolution Imaging Spectro-radiometer) sensor of the Terra satellite provides MOD09A1 product of BRDF (Bidirectional Reflectance Distribution Function) used in computing moisture vegetation indexes (MVI). The exploration of an MVI time-series in the Kroumirie forest in Northern Tunisia showed important noise due to both clouds contamination and sensor defaults that had to be removed. Amongst methods for removing these imperfections, TIMESAT tool was designed for correcting time-series of satellite data and also to retrieve seasonal parameters from smoothed vegetation indexes. The methodology of smoothing functions to fit the time series data is based on two stages. First, a least square fit to the upper envelope of the vegetation indices series is applied. The second stage is achieved by local and adaptive fitting functions. The corrections have been made by spikes removal due to abrupt change of MVI variations and by fitting the MVI time-series to the upper envelop to correct the negative biases of remote sensing vegetation indexes. The adaptive Savitsky-Golay function filter compared to local filtering process produces variations that conserve local variations for all the tested MVI. Seasonal vegetation parameters were extracted for each year of the time-series analysis and compared to the Standardized Precipitation Index (SPI) calculated at meteorological station level and for different time scales. Positive relations were found between SPI and the seasonal parameters expressed by the length and the amplitude of the season, indicating MODIS derived MVI sensitivity to water deficit or surplus conditions. The 6-month SPI showed the best performance when related to water sensitive indexes suggesting that MODIS derived indexes are more correlated to the precipitation variations over seasons.

Robust active contours for fast image segmentation

A robust active contour model is proposed for fast image segmentation. By introducing the intensity fitting energy in a local region, the proposed model can segment the images with intensity inhomogeneity efficiently. Since the local fitting functions are computed before curve evolution, the proposed model is insensitive to initialisation and has a high segmentation efficiency. Experiments on several synthetic and real images have proved the effectiveness of the proposed model.

Adaptive Differential Evolution by Adjusting Subcomponent Crossover Rate for High-Dimensional Waveform Inversion

In this letter, a new adaptive differential evolution (DE) for high-dimensional waveform inversion is proposed. In conventional DE algorithms, individuals are treated as a whole and share the same fitness function and parameters. However, conventional DE algorithms have ignored the huge difference among the subcomponents in an individual and are not effective for high-dimensional problems. Therefore, for high-dimensional problems, we expand the unit of crossover rate from the whole individual to its subcomponents and propose a new adaption algorithm by adjusting the crossover rate of each subcomponent. In our algorithm, both kinds of crossover rate, including individual crossover rate and subcomponent crossover rate, play important roles in crossover operation. Based on local fitness function, the subcomponent crossover rate is adaptively obtained to improve the efficiency of crossover operation. On the other hand, the individual crossover rate is used to prevent the population diversity from decreasing in crossover operation. We embed the adaption algorithm into cooperative coevolutionary DE (CCDE) and propose a new adaptive DE by adjusting the subcomponent crossover rate named CRsADE. We have conducted experiments on waveform inversion to test the performance of the proposed algorithm. The results show that CRsADE performs better than CCDE significantly both on convergence speed and accuracy. In order to estimate the validity of CRsADE, we have also applied it to real seismic data.

A Fast Method of Detecting Overlapping Community in Network Based on LFM

Detect overlapping communities efficiently and effectively in various social networks has been more and more important. Aiming at the high complexity of expanding strategy and the defect of generating many homeless nodes in LFM, we propose a quick algorithm based on local optimization of a fitness function(QLFM). The proposed algorithm firstly select a node as seed randomly .With a local fitness function ,the algorithm then will expand from inside to outside of the seed according to the Breadth-First-Search in graph. As different seeds will expand to different communities independently ,and these communities have same nodes ,thus our method can detect overlapping nodes quickly and efficiently. An empirical evaluation of the method using real and synthetic datasets shows that the method give better result not only in time efficiency, but also in quality aspect than other methods at the overlapping community detection.

Evolutionary dynamics of infectious diseases in finite populations

In infectious disease epidemiology the basic reproductive ratio, R0, is defined as the average number of new infections caused by a single infected individual in a fully susceptible population. Many models describing competition for hosts between non-interacting pathogen strains in an infinite population lead to the conclusion that selection favors invasion of new strains if and only if they have higher R0 values than the resident. Here we demonstrate that this picture fails in finite populations. Using a simple stochastic SIS model, we show that in general there is no analogous optimization principle. We find that successive invasions may in some cases lead to strains that infect a smaller fraction of the host population, and that mutually invasible pathogen strains exist. In the limit of weak selection we demonstrate that an optimization principle does exist, although it differs from R0 maximization. For strains with very large R0, we derive an expression for this local fitness function and use it to establish a lower bound for the error caused by neglecting stochastic effects. Furthermore, we apply this weak selection limit to investigate the selection dynamics in the presence of a trade-off between the virulence and the transmission rate of a pathogen.

Research on MAS-Based Supply Chain Resilience and Its Self-Organized Criticality

Building resilient supply chain is an effective way to deal with uncertain risks. First, by analyzing the self-organization of supply chain, the supply chain resilience is described as a macroscopic property that generates from self-organizing behavior of each enterprise on the microlevel. Second, a MAS-based supply chain resilience model is established and its local fitness function, neighborhood structure, and interaction rules that are applicable to supply chain system are designed through viewing the enterprise as an agent. Finally, with the help of a case, we find that there is an agglomeration effect and a SOC characteristic in supply chain and the evolution of supply chain is controlled by parameters of MAS. Managers can control the supply chain within the resilient range and choose a good balance between interest and risk by controlling enterprises’ behavior.

Ant colony optimization for job shop scheduling using multi-attribute dispatching rules

This paper proposes a heuristic method based on ant colony optimization to determine the suboptimal allocation of dynamic multi-attribute dispatching rules to maximize job shop system performance (four measures were analyzed: mean flow time, max flow time, mean tardiness, and max tardiness). In order to assure high adequacy of the job shop system representation, modeling is carried out using discrete-event simulation. The proposed methodology constitutes a framework of integration of simulation and heuristic optimization. Simulation is used for evaluation of the local fitness function for ants. A case study is used in this paper to illustrate how performance of a job shop production system could be affected by dynamic multi-attribute dispatching rule assignment.

A differential evolution algorithm with cooperative coevolutionary selection operation for high-dimensional optimization

Although different kinds of evolutionary algorithms (EAs) have been designed and achieved great success on many optimization problems, they are usually limited to some small-scale problems, e.g. with less than 100 decision variables, which may be quite small comparing to the requirements of real-world applications. Therefore, scaling EAs to large size problems have attracted more and more interest. Conventional EAs mimic the seemingly random natural processes by which species evolve. These evolution processes are slow or inefficient. Now, genetic engineering has enabled man to increase both the yields and quality of some crops fast by modifying some part of their genome precisely. In this paper, inspired by the ideas of the genetic engineering, we designed a local selection operator by decomposing the high-dimensional problem into some subcomponents and assigning a local fitness function to evaluate each subcomponent. Then a new differential evolution (DE) is proposed by inserting the local selection operator into the framework of DE. Numerical experiments were carried out to evaluate the performance of the new algorithm on a large number of benchmark functions. The results show that the new algorithm is effective and efficient for high-dimensional optimization.

High-Dimensional Waveform Inversion With Cooperative Coevolutionary Differential Evolution Algorithm

In this letter, an improved differential evolution (DE) for high-dimensional waveform inversion is proposed. In conventional evolutionary algorithms, an individual is treated as a whole, and all its variables (genes) are evaluated with a uniform fitness function. This evaluation criterion is not effective for a high-dimensional individual. Therefore, for high-dimensional waveform inversion, we incorporate the decomposition strategy of cooperative coevolution into DE to decompose the individual into some subcomponents. Another novel feature that we introduce is a local fitness function for each subcomponent, and a new mutation operator is designed to guide the mutation direction of each subcomponent with the corresponding local fitness value. Coevolution among different subcomponents is realized in the selection operation with the global fitness function. Many experiments have been carried out to evaluate the performance of this new algorithm. The results clearly show that, for high-dimensional waveform inversion, this algorithm is effective and performs better than some other methods. Finally, the new method has been applied to real seismic data.

Self-organized combinatorial optimization

In this paper, we present a self-organized computing approach to solving hard combinatorial optimization problems, e.g., the traveling salesman problem (TSP). First of all, we provide an analytical characterization of such an approach, by means of formulating combinatorial optimization problems into autonomous multi-entity systems and thereafter examining the microscopic characteristics of optimal solutions with respect to discrete state variables and local fitness functions. Next, we analyze the complexity of searching in the solution space based on the representation of fitness network and the observation of phase transition. In the second part of the paper, following the analytical characterization, we describe a decentralized, self-organized algorithm for solving combinatorial optimization problems. The validation results obtained by testing on a set of benchmark TSP instances have demonstrated the effectiveness and efficiency of the proposed algorithm. The link established between the microscopic characterization of hard computational systems and the design of self-organized computing methods provides a new way of studying and tackling hard combinatorial optimization problems.