At the late evolution stage of the basic particle swarm optimization (BPSO), convergence process starts to slow down and the best fitness particle fluctuates around the globally-optimal solution, which may give rise to decrease on convergence precision of the BPSO. Therefore, an improved algorithm for particle swarm optimization was proposed. The modified version of PSO uses a controllable velo...

Particle swarm optimization (PSO) algorithms are now being practiced for more than a decade and have been extended to solve various types of optimization problems. Despite their wide-spread use, in this paper, we investigate standard PSO algorithms for their ability to (i) approach the optimal region and (ii) simultaneously focus in order to find the optimum precisely, in simplistic yet scalabl...

The concept of particle swarms, although initially introduced for simulating human social behaviors, has become very popular these days as an efficient means for intelligent search and optimization. The particle swarm optimization (PSO), as it is called now, does not require any gradient information of the function to be optimized, uses only primitive mathematical operators and is conceptually ...

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in finding the optimum solution or close to the optimum for a wide variety of real and complicated optimization problems in engineering and computer science. This paper adapts PSO methodology by first solving an optimization problem on the hypercube and then projecting the solution onto the q-si...

This paper describes the application of Particle Swarm Optimization (PSO) for gait optimization on a humanoid robot. The biped gait is modeled by a number of parameterizable trajectories. To achieve omni-directional walking, different sets of gait parameters are optimized for specific walk directions and interpolated later. By using a fitness test based on an acceleration walk, the optimized se...

This paper constructs fully parallel NN hardware realization of Artificial Neural Network (ANN) depends on the efficient execution of single neuron. Field Programmable Gate Array (FPGA) reconfigurable computing architecture is appropriated for hardware achievement of ANN. Numerous implementation of ANNs have been reported in scientific documents, trying to reduce Neural Networks NNs hardware ci...

A modified Particle Swarm Optimization (PSO) using Great Deluge Algorithm (GDA) called MPSO for optimization is presented in this paper. In the proposed algorithm, the range for achieved answers is defined that is the same parameter used in the GDA called “water level”. Amount of this range reduces or increases regarding to algorithm’s property being used in terms of minimum or maximum during t...

This paper introduces a modified PSO, Non-dominated Sorting Particle Swarm Optimizer (NSPSO), for better multiobjective optimization. NSPSO extends the basic form of PSO by making a better use of particles’ personal bests and offspring for more effective nondomination comparisons. Instead of a single comparison between a particle’s personal best and its offspring, NSPSO compares all particles’ ...

It is generally believed genetic algorithm (GA) is superior to particle swarm optimization (PSO) while dealing with the discrete optimization problems. In this paper, a suitable mapping method is adopted and the modified PSO can effectively deal with the discrete optimization problems of linear array pattern synthesis. This strategy has been applied in thinned linear array pattern synthesis wit...

Image enhancement plays an important role in improving the quality of the poor image. Most of existing image enhancement approaches suffer from the problems of color distortion, edge preservation, and halo artifacts. In this paper, an effective local image enhancement technique for remotely sensed images is proposed to enhance the spectral details. The proposed technique utilizes Particle Swarm...