A Fractal Evolutionary Particle Swarm Optimization (FEPSO) is proposed based on the classical particle swarm optimization (PSO) algorithm. FEPSO applies the fractal Brownian motion model used to describe the irregular movement characteristics to simulate the optimization process varying in unknown mode, and include the implied trends to go to the global optimum. This will help the individual to...

Image segmentation is an important research issue in image processing. In this paper, hybridizing of the PSO and BBO algorithm for 2-D image segmentation is implemented. The common features from PSO and BBO algorithm are used and then hybridized for the segmentation. The results are evaluated on the basis of parameters; PSNR and SSIM. The results depicts that the proposed hybrid algorithm perfo...

Cellular manufacturing system, an application of group technology, has been considered as an effective method to obtain productivity in a factory. For design of manufacturing cells, several mathematical models and various algorithms have been proposed in literature. In the present research, we propose an improved version of discrete particle swarm optimization (PSO) to solve manufacturing cell ...

Particle swarmoptimization (PSO) is a heuristic global optimizationmethod, proposed originally byKennedy and Eberhart in 1995. It is now one of themost commonly used optimization techniques.This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO)...

In this paper, optimization procedures based on particle swarm optimization (PSO) are investigated, aiming to efficiently solve the optimal resource allocation for signal-to-noise plus interference ratio (SNIR) optimization of optical code paths (OCPs) from wavelength division multiplexing/optical code division multiplexing (WDM/OCDM) considering imperfections on physical layer. The characteris...

The multidimensional knapsack problem (MKP) is a combinatorial optimization problem belonging to the class of NP-hard problems. This study proposes a novel self-adaptive check and repair operator (SACRO) combined with particle swarm optimization (PSO) to solve the MKP. The traditional check and repair operator (CRO) uses a unique pseudo-utility ratio, whereas SACRO dynamically and automatically...

Original scientific paper Particle swarm optimization (PSO) based optimization algorithms are simple and easily implementable techniques with low computational complexity, which makes them good tools for solving large-scale nonlinear optimization problems. This paper presents a modified version of the original method by combining PSO with a local search technique at the end of each iteration cy...

Design of an optimal controller requires optimization performance measures that are often noncommensurable and competing with each other. Design of such a controller is indeed a particle swarm optimization (PSO) problem. This paper investigates the comparison of application of PSO based optimization technique and Differential Evolution (DE) for the design of a Thyristor Controlled Series Compen...

The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applyin...

The paper extends our pervious work on solving multi-contingency transient stability constrained optimal power flow problems (MC-TSCOPF) with the approach of particles swarm optimization (PSO). A hybrid PSO method that incorporates with a new wavelet theory based mutation operation, intends to improve the searching strategies on previously used PSO methods, is proposed to solve MC-TSCOPF proble...