In this study, an improved method based on evolutionary algorithms for denoising of satellite images is proposed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC), and particle swarm optimisation (PSO) technique and their different variants are exploited for learning the parameters of adaptive thresholding function...

As Unmanned Aerial Vehicles (UAVs) become more readily available and reduce in cost, using multiple UAVs simultaneously to accomplish a task becomes increasingly attractive. Once swarms of UAVs share the same workspace (operational environment) it is necessary to have a means to rapidly adopt an optimal collision-free formation in the workspace. A popular approach for formation of an optimal sw...

The paper presents an overview of recent research on the Particle Swarm Optimization (PSO) algorithm parallelization on the Graphics Processing Unit for general-purpose computations (GPGPU). This survey attempts to collect, organize, and present reports in the area published since 2007 in a unified way. In order to organize the literature a classification by objective functions and PSO variants...

Training neural networks is a complex task of great importance in problems of supervised learning. The Particle Swarm Optimization (PSO) consists of a stochastic global search originated from the attempt to graphically simulate the social behavior of a flock of birds looking for resources. In this work we analyze the use of the PSO algorithm and two variants with a local search operator for neu...

يا هچروم يدنب هشوخ متيروگلا ياراد پ ا ار يددعتم ياهرتم هلمج زا ،نتشادرب هب طوبرم ياهرتماراپ نتشاذگ اه هداد ديد عاعش ، يم هك دشاب ريثات و دنراد متيروگلا ييارگمه و دركلمع رد يدايز اطخ و شيامزآ تروص هب لاومعم نييعت يم رگ د دن . شور هلاقم نيا رد ي رب ينتبم CLA-PSO 1 ي هتسسگ لدم كي هك PSO دشاب يم يارب قيبطت كيتاموتا پ ياهرتمارا يا هچروم يدنب هشوخ يم داهنشيپ ددرگ . روظنم هب گلا ساسا رب هك د...

This paper presents a new learning algorithm, Multi-Population Cooperative Particle Swarm Optimizer (MCPSO), for neural network training. MCPSO is based on a master-slave model, in which a population consists of a master group and several slave groups. The slave groups execute a single PSO or its variants independently to maintain the diversity of particles, while the master group evolves based...

In the present study, an efficient optimization algorithm is proposed to optimal design of structures. The proposed algorithm is an improved particle swarm optimization (PSO) which its global search performance is enhanced by employing the concept of cellular automata (CA). In the so-called improved particle swarm optimization (IPSO) algorithm a new cellular automata based term is added to the ...

Particle swarm optimisation (PSO) is one of the widely adopted meta-heuristic methods for solving real-life problems. Its practical utility can be further enhanced by improving its performance. In order to acheive this, academics have presented several variants original PSO over past few years, including quantum (QPSO), bare-bones (BB-PSO), hybrid PSO, fuzzy etc. this paper, performance improve...

The random drift particle swarm optimization (RDPSO) algorithm is an effective search technique inspired by the trajectory analysis of canonical PSO and free electron model in metal conductors placed external electric field. However, like other variants, RDPSO also inevitably encounters premature convergence when solving multimodal problems. To address this issue, paper proposes a novel diversi...