This paper presents an optimal design of linear phase digital low pass finite impulse response (FIR) filter using Novel Particle Swarm Optimization (NPSO). NPSO is an improved particle swarm optimization (PSO) that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved. The inertia weight has been modified in the PSO to enhance its search...

BACKGROUND Rod failure has been reported clinically in pedicle subtraction osteotomy (PSO) to correct flat back deformity. OBJECTIVE To characterize the fatigue life of posterior screw-rod constructs in the setting of PSO as a function of the severity of rod contour angle. METHODS A modified ASTM F1717 to 04 was used. Rods were contoured to the appropriate angle for the equivalent 20-, 40-,...

Particle swarm optimization (PSO) is a new promising evolutionary algorithm for the optimization and search problem. One problem of PSO is its tendency to trap into local optima due to its mechanism in information sharing. This paper proposes a novel hybrid PSO, namely (HPSO) technique by merging both a mutation operator and natural selection to solve the problem of premature convergence. By in...

Finding an optimal schedule of tasks for an application in distributed environment is critical in general. Task assignment is an extremely NP complete problem. This type of problem can be resolved by heuristic algorithms efficiently because the traditional methods such as dynamic programming and the back tracking need more time for solving this NP complete problem. Particle Swarm Optimization (...

Warehousing management policy is a crucial issue in logistic management. It must be managed effectively and efficiently to reduce the production cost as well as the customer satisfaction. Synchronized zoning system is a warehousing management policy which aims to increase the warehouse utilization and customer satisfaction by reducing the customer waiting time. This policy divides a warehouse i...

This paper introduces a modified particle swarm algorithm to handle multiobjective optimization problems. In multiobjective PSO algorithms, the determination of Pareto optimal solutions depends directly on the strategy of assigning a best local guide to each particle. In this work, the PSO algorithm is modified to assign a best local guide to each particle by using minimum angular distance info...

Bare bones particle swarm optimization (PSO) greatly simplifies the particles swarm by stripping away the velocity rule, but performance seems not good as canonical one in some test problems. Some studies try to replace the sampling distribution to improve the performance, but there are some problems in the algorithm itself. This paper proposes a modified algorithm to solve these problems. In a...

Recent literatures show how modified particle swarm had achieved its name and fame over its parental algorithm called as PSO by optimizing. In this paper we exploit its advantage over image enhancement for improving image contents. This makes handy for visualizing the information from enhanced images. Work deal with use of parameterized transformation and objective function for local/global inf...

An attempt has been made in this paper to solve the non-linear and ill-posed Electrical Impedance Tomography (EIT) inverse problem using clustering-based particle swam optimization (PSO). To enhance optimal search capability in such an ultra high dimension space and improve the quality of the reconstructed image, an adaptive PSO algorithm combined with a modified Newton–Raphson algorithm and a ...

Reduction of sidelobe level in concentric ring arrays results in wide first null beamwidth (FNBW). Theauthors propose a pattern synthesis method based on modified Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) algorithm to reduce sidelobe level while keeping the first null beamwidth (FNBW) fixed or variable. This is achieved by optimizing both ring spacing and numbe...