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QoS-driven web services selection plays an important role in web service composition. Since the selected services would fail down because of the dynamic network situation, this paper presents a service pool model to help users save more composite plans in one selection process. In addition, an efficient service pool construction algorithm, ISPCA, based on an Improved Discrete Particle Swarm Optimization...
This paper presents a heuristic optimization methodology Bacterial foraging and PSO-DE (BPSO-DE) by integrating Bacterial foraging optimization Algorithm (BFOA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) for solving Dynamic Economic Dispatch problem (DED) with valve-point effects. PSO and DE are excellent evolutionary algorithms but they face solution stagnation problem after...
Most of optimization problems have more than one objective function. As a heuristic search technique, particle swarm optimization (PSO) simulates the movements of a flock of birds which aim to find food. The success of PSO has motivated researchers to extend the use of population-based technique to multi-objective optimization. Rapid development of the DNA microarray technology make it very possible...
Linear text segmentation has been used in several natural language processing tasks, such as information retrieval and text summarization. It has been proven that linear text segmentation is beneficial to these tasks. To improve the performance of linear text segmentation, a novel domain independent linear text segmentation algorithm, called DPSO-SEG, is proposed in this paper. DPSO-SEG applies the...
In order to overcome the shortage of premature convergence caused by local optimization in the process of global optimization, an adaptive weight Particle Swarm Optimization algorithm with constriction factor is proposed combined with an analysis of convergence of Particle Swarm Optimization algorithm. The value of the inertia weight is set according to dynamic information about the changes in the...
The paper presents the dynamic modeling and coordinated control strategy for an integrated micro grid scheme using Photo Voltaic PV, Fuel Cell FC, and backup Diesel generation with additional battery backup system. The integrated scheme is fully stabilized using a novel FACTS based green filter compensators that ensures stabilized DC bus voltage, minimal inrush current conditions, and load excursions...
This paper presents a novel PID self regulating tri loop controller for a hybrid PV-FC-Diesel-Battery powered all-wheel drive electric vehicle using four Permanent Magnet DC (PMDC) motors, which are modeled to include existing nonlinearities in motor plus load inertia (J) and viscous friction (B). A Tri Loop dynamic error driven scheme is based on Multi Objective Particle Swarm Optimization MOPSO...
This work presents a new mechanism to reduce statistically the chances of the optimization process of nonlinear functions stagnating in local minima, using the meta-heuristic Particle Swarm Optimization. Such mechanism adopts a nonmonotonic way to control the particle inertia, which is one of the factors responsible for this movement during the optimization process. For this, the cosine function was...
In this paper, a Coevolutionary Comprehensive Learning Particle Optimizer (Co-CLPSO) is proposed for solving constrained real-parameter optimization problems. In this novel algorithm, a coevolutionary schedule and a novel constraint-handling mechanism are employed. Two swarms with different thresholds are constructed and they exchange information in the evolution process. Different with the existing...
A key feature in population based optimization algorithms is the ability to explore a search space and make a decision based on multiple solutions. In this paper, an incremental learning strategy based on a dynamic particle swarm optimization (DPSO) algorithm allows to produce heterogeneous ensembles of classifiers for video-based face recognition. This strategy is applied to an adaptive classification...
Particle swarm optimization (PSO) is a nature-inspired technique for solving continuous optimization problems. For a fixed optimization problem, the quality of the found solution depends significantly on the choice of the algorithmic PSO parameters such as the inertia weight and the acceleration coefficients. It is a challenging task to choose appropriate values for these parameters by hand or mathematically...
The Nelder-Mead Algorithm (NMA) is an almost half-century old method for numerical optimization, and it is a close relative of Particle Swarm Optimization (PSO) and Differential Evolution (DE). In recent work, PSO, DE and NMA have been generalized using a formal geometric framework that treats solution representations in a uniform way. These formal algorithms can be used as templates to derive rigorously...
This paper introduces a new description-centric algorithm for web document clustering based on the hybridization of the Global-Best Harmony Search with the K-means algorithm, Frequent Term Sets and Bayesian Information Criterion. The new algorithm defines the number of clusters automatically. The Global-Best Harmony Search provides a global strategy for a search in the solution space, based on the...
The Particle Swarm Optimization (PSO) algorithm has been successfully applied to dynamic optimization problems with very competitive results. One of its best performing variants, the mQSO is based on an atomic model, with quantum and trajectory particles. This work introduces a new version of this algorithm which uses heuristic rules for improving its performance. Two new rules are presented: one...
A clustering algorithm based on particle swarm optimization (PSO) and fuzzy theorem was introduced for data analysis. Clustering algorithms require users to set some parameters, such as the number of clusters k. However, it is unreasonable to expect users to specify a meaningful value of k if they lack prior knowledge of the data. This paper proposed an algorithm to determine the appropriate number...
This paper studies a new version of growing particle swarm optimizers. In the algorithm, a new particle is born if a particle exploring the optimum is stagnated and the swarm can grow in variable tree or multi-loop topology depending on problem complexity. The particle velocity is controlled by a dynamic acceleration parameter that can attenuate depending on the number of particles and can vibrate...
The optimisation in dynamic and noisy environments brings closer real-world optimisation. One interesting proposal to adapt the PSO for working in dynamic and noisy environments was the incorporation of an evaporation mechanism. The evaporation mechanism avoids the detection of environment changes, providing a continuous adaptation to the environment changes and reducing the effect when the fitness...
In this paper a novel discrete Particle Swarm Optimization (PSO) algorithm is proposed to solve the Orienteering Problem (OP). Discrete evolution is achieved by re-defining all operators and operands used in PSO. To obtain better results, Strengthened-PSO which improves both exploration and exploitation during the search process is employed for experimental evaluation. Our proposed algorithm either...
This paper presents a new hybrid heuristic combining particle swarm optimization with a Lagrangian heuristic along the lines first proposed by Wedelin. We will refer to this as a Combinatorial Lagrangian Particle Swarm Optimization Algorithm (CoLaPSO). It uses a problem representations that works simultaneously in the dual space (Lagrangian multipliers) and the primal space in the form of cost perturbations...
Given a set of images of a set of 3D points, with known correspondence among them, we would like to reconstruct the coordinates of the 3D points and discover the projection matrices associated with the cameras used to capture these images. Once this information is obtained, we would be able to compute any novel view of these 3D points from a virtual camera placed at any position and orientation. We...
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