This paper discusses a novel technique to extract small signal equivalent circuit model parameters of GaAs MESFET device based on particle swarm optimization (PSO) technique. Three different variants of PSO namely basic PSO, Delta well quantum PSO (DQPSO) and Harmonic well quantum PSO (HQPSO) are implemented and compared. We find that these techniques extract the 16-element small signal model p...

Particle swam optimization (PSO) is a popular stochastic optimization method that has found wide applications in diverse fields. However, PSO suffers from high computational complexity and slow convergence speed. High computational complexity hinders its use in applications that have limited power resources while slow convergence speed makes it unsuitable for time critical applications. In this...

Trilateration-based localization (TBL) has become a corner stone of modern technology. This study formulates the concern on how wireless sensor networks can take advantage of the computational intelligent techniques using both singleand multi-objective particle swarm optimization (PSO) with an overall aim of concurrently minimizing the required time for localization, minimizing energy consumed ...

Back propagation algorithm (BPA) have the complexity, local minima problem so we are using Particle Swarm optimization (PSO) algorithms to reduce and optimize BPA. In this paper, two variants of Particle Swarm Optimization (PSO) PSO_Hill and PSO_A* is used as optimization algorithm. PSO_Hill and PSO_A* algorithms are analyzed and evaluated on the basis of their advantages, applied to feed forwa...

Among the variants of the basic Particle Swarm Optimization (PSO) algorithm as first proposed in 1995, EPSO (Evolutionary PSO), proposed by Miranda and Fonseca, seems to produce significant improvements. We analyze the effects of two modifications introduced in that work (adaptive parameter setting and selection based on an evolution strategies-like approach) separately, reporting results obtai...

Cell assignment is an important issue in the area of resource management in cellular networks. The problem is an NP-hard one and requires efficient search techniques for its solution in real-time. In this letter, in order to minimize the cabling and handoff costs, we use two novel discrete particle swarm optimization (PSO) algorithms, the barebones (BB) and the exploiting barebones (BBExp) PSO ...

The partitioned step particle swarm optimization (PSPSO) introduces a two-fold searching mechanism that increases the search capability of Particle Swarm Optimization. first layer involves γ and λ, values which are introduced to describe current condition characteristics searched solution diversifies particles when it is converging too much on some optima. second partitioning tries prevent prem...

Standard particle swarm optimization is easy to fall into local optimum and has the problem of low precision. To solve these problems, the paper proposes an effective approach, called particle swarm optimization based on multiple swarms and opposition-based learning, which divides swarm into two subswarms. The 1st sub-swarm employs PSO evolution model in order to hold the self-learning ability;...

This paper aims to improve the performance of original particle swarm optimization (PSO) so that the consequent method can be more robust and statistically sound for global optimization. A variation of PSO called the orthogonal permutation particle swarm optimization (OPPSO) is presented. An orthogonal permutation strategy, based on the orthogonal experimental design, is developed as a metaboli...

Despite considerable popularity, the mechanisms that govern the behavior of Particle Swarm Optimization (PSO) are still a subject of research. Regarding communication between particles, for example, many authors have discussed the effects of swarm topology, but few have studied the dynamics of the information exchange among particles. In this paper we show that a synchronous update of the socia...