Based on introducing two optimization algorithms, group search optimization (GSO) algorithm and particle swarm optimization (PSO) algorithm, a new hybrid optimization algorithm which named particle swarm-group search optimization (PS-GSO) algorithm is presented and its application to optimal structural design is analyzed. The PS-GSO is used to investigate the spatial truss structures with discr...

Magnetic resonance imaging (MRI) is a powerful tool for clinical diagnosis because it allows to distinguish different tissues and allows multiple modalities (T1, T2, ...) each having particular properties. In this work, the segmentation of MR Brain images is considered as an optimization problem and solved using evolutionary algorithms: particle swarm optimization (PSO) and differential evoluti...

Particle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm. Most of the PSO studies are empirical, with only a few theoretical analyses that concentrate on understanding particle trajectories. This paper overviews current theoretical studies, and extend these studies to applications in mechatronic systems, such as identification, control gains and o...

Particle swarm optimization (PSO) is an artificial intelligence (AI) technique that can be used to find approximate solutions to extremely difficult or impossible numeric maximization and minimization problems. Particle swarm optimization is an optimization method. It is an optimization algorithm, which is based on swarm intelligence. Optimization problems are widely used in different fields of...

This paper proposes a novel optimization algorithm inspired by water flowing and shaking behaviors in a vessel. Water drops in our algorithm flow to the gradient descent direction and are sometimes shaken for getting out of local optimum areas when most water drops fall in local optimum areas. These flowing and shaking operations allow our algorithm to quickly approach to the global optimum wit...

In recent days, Swarm Intelligence plays an important role in solving many real life optimization problems. Particle Swarm Optimization (PSO) is swarm intelligence based search and optimization algorithm which is used to solve global optimization problems. But due to lack of population diversity and premature convergence it is often trapped into local optima. We can increase diversity and preve...

In order to have clarity in the satellite images we have used Particle Swarm Optimization technique. When incorporated with traditional clustering algorithms, problems such as local optima and sensitivity to initialization, are reduced, thus exploring a greater area using global search. This segmented image is further classified using Kappa coefficient. Keywords— Particle Swarm Optimization(PSO...

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic ...

Multi-objective open-shop scheduling is definitely significant in practical. However, the research focused on multi-objective open-shop scheduling was relatively scarce. This article proposed a particle swarm optimization to address open-shop scheduling problems with multiple objectives. Originally, particle swarm optimization was invented to treat continuous optimization problems. In this pape...

Recently, the scheduling problem in distributed data-intensive computing environments has been an active research topic. This Chapter models the scheduling problem for work-flow applications in distributed dataintensive computing environments (FDSP) and makes an attempt to formulate the problem. Several meta-heuristics inspired from particle swarm optimization algorithm are proposed to formulat...