In this paper a CAD (Computer Aided Diagnosis) system is proposed to optimize the feature set using hybrid of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) technique called Genetical Swarm Optimization (GSO) in Digital Mammogram. Even though PSO is a good optimization technique, it may be trapped in local minima and may prematurely converge. So, the genetic operators are used in ...

Particle Swarm Optimization algorithm (PSO) is a popular stochastic searching optimization algorithm to solve complicated optimization problems. The approach of retrieving duct parameters from the sea-surface reflected radar clutter is also known as Refractivity From Clutter (RFC) technique. RFC technique provides the near-real-time duct parameters to evaluate the radio system performance, with...

This paper introduces the particle swarm optimization (PSO)-based approach to resolve the resource-constrained project scheduling problem (RCPSB) with the objective of minimizing project duration. Activities priorities for scheduling are represented by particles and a parallel scheme is utilized to transform the particle-represented priorities to a feasible schedule according to the precedence ...

7 Addressing the issue of SVMs parameters optimization, this study proposes an efficient 8 memetic algorithm based on Particle Swarm Optimization algorithm (PSO) and Pattern Search 9 (PS). In the proposed memetic algorithm, PSO is responsible for exploration of the search space 10 and the detection of the potential regions with optimum solutions, while pattern search (PS) is 11 used to produce ...

This paper presents a particle swarm optimization (PSO) based approach for multiple object tracking based on histogram matching. To start with, gray-level histograms are calculated to establish a feature model for each of the target object. The difference between the gray-level histogram corresponding to each particle in the search space and the target object is used as the fitness value. Multi...

Considering the standard particle swarm optimization (PSO) has the shortcomings of low convergence precision in job shop scheduling problems, the job shop scheduling solution is presented based on improved particle swarm optimization (A-PSO). In this paper, the basic theory of A-PSO is described. Also, the coding and the selection of parameters as well as the decoding of A-PSO are studied. It u...

Particle swarm optimization (PSO) is an efficient tool for optimization and search problems. However, it is easy to be trapped into local optima due to its information sharing mechanism. Many research works have shown that mutation operators can help PSO prevent premature convergence. In this paper, several mutation operators that are based on the global best particle are investigated and compa...

The authors applied the particle swarm optimization (PSO) algorithm to solve the conditional nonlinear optimal perturbation (CNOP) and the lower bound of maximum predictable time (LBMPT). The results obtained by the PSO algorithm were compared to those by the traditional optimization algorithm (such as, a gradient descent algorithm based on the adjoint model, ADJ). The authors found that the PS...

The aim of this research is to design a PID Controller using particle swarm optimization (PSO) algorithm for multiple-input multiple output (MIMO) Takagi-Sugeno fuzzy model. The conventional gain tuning of PID controller (such as Ziegler-Nichols (ZN) method) usually produces a big overshoot, and therefore modern heuristics approach such as PSO are employed to enhance the capability of tradition...

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique developed by Eberhart (1995) and Kennedy, inspired by the social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as GA. The system is initialized with a population of random solutions and searches for optima by updating generations. Ho...