BSO Algorithm Research Articles

Optimal support vector machine and hybrid tracking model for behaviour recognition in highly dense crowd videos

PurposeObject detection models have gained considerable popularity as they aid in lot of applications, like monitoring, video surveillance, etc. Object detection through the video tracking faces lot of challenges, as most of the videos obtained as the real time stream are affected due to the environmental factors.Design/methodology/approachThis research develops a system for crowd tracking and crowd behaviour recognition using hybrid tracking model. The input for the proposed crowd tracking system is high density crowd videos containing hundreds of people. The first step is to detect human through visual recognition algorithms. Here, a priori knowledge of location point is given as input to visual recognition algorithm. The visual recognition algorithm identifies the human through the constraints defined within Minimum Bounding Rectangle (MBR). Then, the spatial tracking model based tracks the path of the human object movement in the video frame, and the tracking is carried out by extraction of color histogram and texture features. Also, the temporal tracking model is applied based on NARX neural network model, which is effectively utilized to detect the location of moving objects. Once the path of the person is tracked, the behaviour of every human object is identified using the Optimal Support Vector Machine which is newly developed by combing SVM and optimization algorithm, namely MBSO. The proposed MBSO algorithm is developed through the integration of the existing techniques, like BSA and MBO.FindingsThe dataset for the object tracking is utilized from Tracking in high crowd density dataset. The proposed OSVM classifier has attained improved performance with the values of 0.95 for accuracy.Originality/valueThis paper presents a hybrid high density video tracking model, and the behaviour recognition model. The proposed hybrid tracking model tracks the path of the object in the video through the temporal tracking and spatial tracking. The features train the proposed OSVM classifier based on the weights selected by the proposed MBSO algorithm. The proposed MBSO algorithm can be regarded as the modified version of the BSO algorithm.

Grid based clustering for satisfiability solving

The originality of this work resides into the exploitation of data mining techniques for problem solving. Two major phases define this work. The first one is to determine the clustering technique that best suits each SAT instance based on the distribution of the later. The clustering technique is then applied to reduce the complexity of each instance by creating sub-instances that can be solved independently in the second phase. The latter consists into a resolution step where the DPLL or BSO algorithms are executed depending on the number of variables to be assigned in each cluster. This two-phase resolution strategy provides more efficient problem solving. The Boolean Satisfiability problem (SAT) is considered in this study because of its importance for the Artificial Intelligence (AI) community and the impact of its solving on other complex problems. Three different distributions of the problem were observed. The first distribution defines a space where the variables are dispersed forming regions of considerable density interspersed with regions of lower density or empty regions. On the other hand, the other two distributions do not show any significant shape, as the variables are randomly scattered, with one of these two dispersions having the particularity that almost all its variables are of high occurrence. To each of the three distributions, a clustering technique is associated. Density-based clustering techniques are the most appropriate type of clustering for the first distribution. Meanwhile, grid-based clustering and frequent patterns mining seem to be the most suitable clustering techniques for the second and third distributions. Investigations are undertaken on these latter issues and contributions are presented in this paper. Experiments were conducted on public benchmarks and the results showed the importance of the pre-processing step of data mining to solve the SAT problem.

An active learning brain storm optimization algorithm with a dynamically changing cluster cycle for global optimization

An active learning brain storm optimization (ALBSO) algorithm with a dynamically changing cluster cycle is proposed to enhance the performance of BSO algorithm. In the original BSO, a new individual is generated only by simply choosing one individual or two individuals from one cluster or two clusters with a certain probability. Although the diversity of the population is well maintained, owing to this way of random selection, it seriously affects the performance of BSO. In machine learning, instead of learning from random samples, a learner with active learning has the ability to select its own training data, such as the data of best value or mean value. Referring to the idea of active learning, the active learning strategy with intra-cluster learning and inter-cluster learning is integrated into BSO to elevate the solution accuracy and the convergence speed. In addition, to reduce the time burden of clustering operation, a dynamically changing cluster cycle with one-step clustering is adopted in the proposed ALBSO. Through experimental analysis by utilizing the active learning strategy with the dynamically changing cluster cycle, on the basis of the original diversity maintenance framework, the proposed ALBSO can balance the relationship of diversification and intensification, meanwhile, the time complexity is remarkably reduced. The experimental study on a set of 38 test functions and two real-world problems shows that the performance of BSO is significantly improved, and ALBSO is an effective method to optimize complex unimodal and multimodal functions in comparison with several other well-known algorithms.

BSFS: Design and Development of Exponential Brain Storm Fuzzy System for Data Classification

The inductive learning of fuzzy rule classifier suffers in the rule generation and rule optimization when the search space or variables becomes high. This creates the new idea of making the fuzzy system with precise rules leading to less scalability and improved accuracy. Accordingly, different approaches have been presented in the literature for optimal finding of fuzzy rules using optimization algorithms. Here, we make use of the brain storm optimization algorithm for rule optimization. In this paper, a new fuzzy system called, exponential brain storm fuzzy system is developed by modifying the traditional fuzzy system in rule definition process. In rule derivation, we have presented an algorithm called, EBSO by modifying the BSO algorithm with exponential model. Also, the membership function is designed using simple uniform distribution-based approach. Finally, data classification is performed with a new BSFS system using three medical databases such as, PID, Cleveland and DRD. The experimentation proved that the proposed BSFS clearly outperformed in all the three datasets by reaching the maximum accuracy.

A Hybrid Strategy for Resource Allocation and Load Balancing in Virtualized Data Centers Using BSO Algorithms

In data centers are provided solution to the consumer and to the organization by means of store and process their data. When scheduling operation carrying more requirements for resources than it can hold, in this situation load balancing strategy distributes workloads across multiple servers to optimize the performances. However, resource allocation and load balancing is an inspiring problem for the cloud service providers to consumers in terms of Quality of Services. The proposed hybrid bacterial swarm optimization algorithm, achieve global seek over the entire search space through PSO while local search is achieved by BFO algorithm. This paper proposed a novel idea, how to tackle the scheduling problem by using hybrid load balancing techniques. The experimental results demonstrate that the projected algorithms overtake the existing SA, PSO, Dynamic ADS algorithms considerably by minimizing the operational cost, make-span and maximize the utilization of the resource.

Jordan triple disystems

We take an algorithmic and computational approach to a basic problem in abstract algebra: determining the correct generalization to dialgebras of a given variety of nonassociative algebras. We give a simplified statement of the KP algorithm introduced by Kolesnikov and Pozhidaev for extending polynomial identities for algebras to corresponding identities for dialgebras. We apply the KP algorithm to the defining identities for Jordan triple systems to obtain a new variety of nonassociative triple systems, called Jordan triple disystems. We give a generalized statement of the BSO algorithm introduced by Bremner and Sánchez-Ortega for extending multilinear operations in an associative algebra to corresponding operations in an associative dialgebra. We apply the BSO algorithm to the Jordan triple product and use computer algebra to verify that the polynomial identities satisfied by the resulting operations coincide with the results of the KP algorithm; this provides a large class of examples of Jordan triple disystems. We formulate a general conjecture expressed by a commutative diagram relating the output of the KP and BSO algorithms. We conclude by generalizing the Jordan triple product in a Jordan algebra to operations in a Jordan dialgebra; we use computer algebra to verify that resulting structures provide further examples of Jordan triple disystems. For this last result, we also provide an independent theoretical proof using Jordan structure theory.

A novel bee swarm optimization algorithm for numerical function optimization

The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel algorithm called bee swarm optimization, or BSO, and its two extensions for improving its performance are presented. The BSO is a population based optimization technique which is inspired from foraging behavior of honey bees. The proposed approach provides different patterns which are used by the bees to adjust their flying trajectories. As the first extension, the BSO algorithm introduces different approaches such as repulsion factor and penalizing fitness (RP) to mitigate the stagnation problem. Second, to maintain efficiently the balance between exploration and exploitation, time-varying weights (TVW) are introduced into the BSO algorithm. The proposed algorithm (BSO) and its two extensions (BSO–RP and BSO–RPTVW) are compared with existing algorithms which are based on intelligent behavior of honey bees, on a set of well known numerical test functions. The experimental results show that the BSO algorithms are effective and robust; produce excellent results, and outperform other algorithms investigated in this consideration.