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Intrusion Detection Systems (IDS) are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively in effective. Recently applying Artificial Intelligence, machine learning and data mining techniques to IDS are increasing. Artificial Intelligence plays a driving role in security services. An intrusion detection method based...
Clustering is an important research topic in data mining that appears in a wide range of unsupervised classification applications. Partitional clustering algorithms such as the k-means algorithm are the most popular for clustering large datasets. The major problem with the k-means algorithm is that it is sensitive to the selection of the initial partitions and it may converge to local optima. In this...
A distinct trend has emerged that the Internet is used to transport data on a more and more massive scale. Capacity shortage in the backbone networks has become a genuine possibility, which will be more serious with fiber-based access. The problem addressed in this paper is how to conduct massive content distribution efficiently in the future network environment, where the capacity limitation can...
This paper solves the Phasor Measurement Unit (PMU) placement problem by Particle swarm Optimization (PSO). The PSO algorithm is implemented on three bus systems namely the 7, 14, 57 IEEE standard Bus systems. In this paper it has been proved that the placing of Phasor measurement units only at buses with the highest number of incident branches surely doesn't yield the optimal placement of the PMUs...
The traditional searching scheme of independent component analysis (ICA) is based on gradient algorithm. And a learning step size is required beforehand. It couldn't resolve the problem of convergence. To overcome the drawback, an improved particle swarm optimization (PSO) is applied to ICA algorithm. Firstly, the dynamic inertia weight which is based on evolution speed and aggregation degree is introduced...
Particle swarm optimization (PSO) algorithm is widely used in function optimization, but the performance of PSO algorithm is restricted as frequently occurrences of the premature. Introduce immune operator to PSO algorithm can be to avoid premature and improve the performance of algorithm. For the four benchmark functions, the results show that the immune operator improve performance of algorithm...
The knapsack problem is formulated as a discrete optimization problem. In this paper, a solution strategy based on an improved binary PSO is presented. It applies new update functions and the strategy of disturbance to deals with the knapsack problem. Furthermore, a penalty function is suggested to change constrained problem into an unconstrained one. The example shows that this algorithm has a faster...
The service composition composing the existing Web services to form new, satisfying different user requirements and value-added composition service has become new application requirement and popular research. The optimization of services composition is a nonlinear multi-objective optimization problem which has been proven to be NP-complete. It is preponderant to settle the multi-objective optimization...
The coexistence of multimedia services in e-communication systems, with varying bandwidth utilization characteristics, impedes the efficiency of rate control and thereby impacts on the Quality of Service (QoS), in terms of low throughput. As such, the rate control for multimedia flows remains an open problem. This paper proposes a memetic optimization approach to rate allocation of multiclass services...
This paper presents a novel variant of particle swarm optimization (PSO) called adaptive accelerated exploration particle swarm optimizer (AAEPSO). AAEPSO algorithm identifies the particles which are far away from the goal and accelerate them towards goal with an exploration power. These strategies particularly avoid the premature convergence and improve the quality of solution. The performance comparisons...
This paper presents fusion of Bacterial Foraging with parameter free Particle Swarm Optimization (HBF-pfPSO). The proposed technique is used to enhance quality of global optima of multimodal functions. The authors propose two major modifications in Bacterial Foraging Optimization (BFO). Firstly, all bacteria position and direction are updated after all fitness evaluations instead of each fitness evaluation...
Evaluation of certain properties of calcined alumina or special grade alumina is necessary and important to its manufactures. Generally it is determined in the laboratories using different instrumental and manual methods, which is cost and time intensive. In the present work, evolving neural network has been used for the estimation of a property given few others. To evolve the neural network model...
In this paper, we applied an existing particle swarm optimization algorithm to biological pairwise sequence alignment problem. In addition, we improved the basic algorithm according to sequence characteristics. This improved algorithm provides constraint conditions of the initial position of particles and the mobile strategy in motion, taking account of the other particle swarm' s next generation...
A new optimization technique named as sliced particle swarm optimization (SPSO) is proposed. It introduces the slicing of search space into rectangular slices. It gives complete solution in terms of reduction in the computational cost and tracking minutely each sliced search space. It introduces the momentum factor which restricts the particle in a sliced search space. Linearly decreasing inertia...
Abstract-A new approach to ORPF (optimal reactive power flow) based on SFLA (shuffled frog leaping algorithm) is proposed. The algorithm approaches to solving ORPF problem are given. By applying the algorithm to dealing with IEEE 30-bus system, compared with the particle swarm optimization (PSO) algorithm and SGA(simple genetic algorithm),the experimental results show that the algorithm is indeed...
This paper presents a new diversity guided particle swarm optimization algorithm (PSO) named beta mutation PSO or BMPSO for solving global optimization problems. The BMPSO algorithm makes use of an evolutionary programming based mutation operator to maintain the level of diversity in the swarm population, thereby maintaining a good balance between the exploration and exploitation phenomena and preventing...
Aiming at the phenomenon of premature convergence and later period oscillatory occurrences, an adaptive particle swarm optimization algorithm with the changes of the population diversity was proposed. In the algorithm, the adaptive exponent decreasing inertia weight and a dynamic adaptive changing threshold were proposed, the satisfied particle of threshold will be mutation by the average distance...
In this paper, a chaotic cooperative particle swarm optimization based on tent map (TCCPSO) is proposed. The cooperative particle swarm optimization (CPSO), can significantly improve the performance of the original algorithm. However, CPSO has the defect of leading to pseudominimizer, which can not be easily escaped by interleaving the CPSO and PSO algorithm. Therefore, we take full advantages of...
Particle swarm optimization (PSO) is an effective robust and simple method to solve many problems proposed in science and engineering. How does the particle motion and how the particles in a swarm find the optimal solutions are an open problem. This paper investigates the particle trajectories for the standard PSO based on difference equations theories. Equilibrium point and asymptotically stable...
In order to eliminate the shortcomings of traditional neural networks in handwritten Chinese characters recognition, such as the premature convergence, a novel intelligent method is presented, which uses the particle swarm optimization (PSO) algorithm with adaptive inertia weight to train the neural networks. The main idea is that the optimum weights and thresholds of the neural networks is acquired...
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