Particle swarm optimization (PSO) is becoming one of the most important swarm intelligent paradigms for solving global optimization problems. Although some progress has been made to improve PSO algorithms over the last two decades, additional work is still needed to balance parameters to achieve better numerical properties of accuracy, efficiency, and stability. In the optimal PSO algorithm, th...

This paper presents an optimal design of linear phase digital low pass finite impulse response (FIR) filter using Novel Particle Swarm Optimization (NPSO). NPSO is an improved particle swarm optimization (PSO) that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved. The inertia weight has been modified in the PSO to enhance its search...

the main purpose in this paper, to express strong algorithmic optimization in their ability to detect expression target of sar that use in aircraft, plane and satellites to observe the target on the ground. for this purpose,the sar technology and its applications are introduced. previous algorithms used in this field with poor diagnosis and appropriate speed is low, like means and fuzzy and pso...

This paper mainly discusses the application of the particle swarm optimization in logistics distribution routing problems. Combining with the characteristics of logistics and distribution, it established a mathematical model of the distribution routing problem. Introducing three kinds of optimization strategies in the particle swarm optimization to optimize the particle swarm algorithm, constru...

This chapter presents particle swarm optimization (PSO) based algorithms. After an overview of PSO’s development and application history also two application examples are given in the following. PSO’s robustness and its simple applicability without the need for cumbersome derivative calculations make it an attractive optimization method. Such features also allow this algorithm to be adjusted fo...

This work presents the definition and solution of an optimization model using techniques based on the Particle Swarm Optimization (PSO) algorithm or the Particle Swarm Optimization and Genetic Algorithms. The model corresponds to the Joint Replenishment Problem in a system operating with quantity discounts. Results are shown in problems, where their size makes it difficult or impossible to esta...

This paper describes an evolutionary strategy called PSOGA-NN, which uses Neural Network (NN) for selfadaptive control of hybrid Particle Swarm Optimization and Adaptive Plan system with Genetic Algorithm (PSO-APGA) to solve large scale problems and constrained real-parameter optimization. This approach combines the search ability of all optimization techniques (PSO, GA) for stability of conver...

Keywords: PSO QPSO Mean best position Weight parameter WQPSO a b s t r a c t Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. In this paper, we propose an improved quantum-behaved particle swarm optimization with weighted mean best position according t...

In this paper, five previous Particle Swarm Optimization (PSO) algorithms for multimodal function optimization are reviewed. A new and a successful PSO based algorithm, named as CPSO is proposed. CPSO enhances the exploration and exploitation capabilities of PSO by performing search using a random walk and a hill climbing components. Furthermore, one of the previous PSO approaches is improved i...

The rest of this paper is organized as follows. Section 2 describes traditional System Identification and introduces the use of Particle Swarm Optimization (PSO) for determining the coefficients of a simple autoregressive moving average model (SwARMA). Section 3 explains Particle Swarm Optimization. Section 4 describes the results of using PSO for determining the ARMA model parameter (SwARMA) f...