The prediction of stock price return volatilities is important for financial companies and investors to help measure managing market risk support decision-making. literature points out alternative models - such as the widely used heterogeneous autoregressive (HAR) specification which attempt forecast realized accurately. However, recent variants artificial neural networks, echo state network (E...

OBJECTIVE To compare the prevalence of comorbidities, health care utilization, and costs between moderate-to-severe psoriasis (PsO) patients with comorbid psoriatic arthritis (PsA) and matched controls. METHODS Adults ages 18-64 years with concomitant diagnoses of PsO and PsA (PsO+PsA) were identified in the OptumHealth Reporting and Insights claims database between January 2007 and March 201...

Particle Swarm Optimization (PSO) is a popular population-based optimization algorithm. While PSO has been shown to perform well in a large variety of problems, PSO is typically implemented in software. Population-based optimization algorithms such as PSO are well suited for execution in parallel stages. This allows PSO to be implemented directly in hardware and achieve much faster execution ti...

Particle Swarm Optimization (PSO) is a population based optimal method and very simple in both theory and numerical implementation. Nowadays, PSO has been recognized as a paradigm for numerical optimizations; however, PSO is easily trapped into a local optimum when solving multidimensional and complex problems. In order to overcome this difficulty, this paper presents a modified PSO with a dyna...

In order to effectively solve combinatorial optimization problems, the Estimation of Distribution Algorithm (EDA) and Particle Swarm Optimization (PSO) combine to form a new ED-PSO hybrid algorithm, the algorithm can effectively apply global statistical information and global optimal solution to the solution space search. This algorithm is used to solve the Multidimensional Knapsack Problem (MK...

This paper proposes an improved particle swarm optimization named PSO with Controllable Random Exploration Velocity (PSO-CREV) behaving an additional exploration behavior. Different from other improvements on PSO, the updating principle of PSO-CREV is constructed in terms of stochastic approximation diagram. Hence a stochastic velocity independent on cognitive and social components of PSO can b...

so far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is particle swarm optimization (pso). prior some efforts by applying fuzzy logic for improving defects of pso such as trapping in local optimums and early convergence has been done. moreover to overcome the problem of i...

The paper presents a novel particle swarm optimizer (PSO), called gender-hierarchy particle swarm optimizer based on punishment (GH-PSO). In the proposed algorithm, the social part and recognition part of PSO both are modified in order to accelerate the convergence and improve the accuracy of the optimal solution. Especially, a novel recognition approach, called general recognition, is presente...

Particle Swarm Optimization is currently employed in several optimization and search problems due its ease and ability to find solutions successfully. A variant of PSO, called as Improved PSO has been developed in this paper and is hybridized with the simulated annealing approach to achieve better solutions. The hybrid technique has been employed, inorder to improve the performance of improved ...

Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. Being a stochastic algorithm, PSO and its randomness present formidable challenge for the theoretical analysis of it, and few of the existing PSO improvements have make an effort to eliminate the random coefficients in the PSO updating formula. This paper analyzes the importance of the randomness in the...