Simple Particle Swarm Optimization Algorithm Research Articles

Particle Swarm Optimization Algorithm based on Differential Mutation and Thermodynamic Selection

In order to improve the convergence speed and the drawback of easily converging to the local optimum of the standard particle swarm optimization (PSO), two improved PSO algorithms are presented based on the simple particle swarm optimization algorithm without speed attribute. One is introducing differential mutation technology of differential evolution algorithm into the simple PSO algorithm for the disturbance of the particle position update, so the particles have the more opportunity to escape from local extreme points and reach the global optimum. The other is based on the law of free energy minimization in the statistical physics and thermodynamics. The particles with the smaller free energy component are chosen to retain in the new population. This selection strategy effectively maintains the diversity of the population and improves the search performance of the PSO algorithm. The experimental results on the twelve classical test functions show that the two improved simple PSO algorithms have the better convergence rate, convergence precision and stability.

Simplified particle swarm optimization algorithm

Real ants and bees are considered social insects, which present some remarkable characteristics that can be used, as inspiration, to solve complex optimization problems. This field of study is known as swarm intelligence. Therefore, this paper presents a new algorithm that can be understood as a simplified version of the well known Particle Swarm Optimization (PSO). The proposed algorithm allows saving some computational effort and obtains a considerable performance in the optimization of nonlinear functions. We employed four nonlinear benchmark functions, Sphere, Schwefel, Schaffer and Ackley functions, to test and validate the new proposal. Some simulated results were used in order to clarify the efficiency of the proposed algorithm.

Simple Particle Swarm Optimization for Solving Beam-Slab Layout Design Problems

This study presents a particle swarm optimization (PSO) algorithm for beam-slab layout design. Design of beam-slab layouts is considered a heuristic task rather than an algorithmic task since its process cannot be written as an algorithm. In this study, this design task is expressed as an optimization problem. This is done by establishing appropriate objective and constraint functions based on selected suitable engineering consideration. The obtained optimization problem is then solved by a simple PSO algorithm. PSO is a rather new optimization method that is inspired by the flocking behavior of fishes and birds. Recently, it has gained popularity because its algorithm is simple and there has been evidence of its excellent performance. To employ PSO in solving the optimization problem that represents the beam-slab layout design problem, a suitable coding scheme for beam-slab layouts is used. Since the beam-slab layout design problem deals with a discrete search space of beam-slab layouts, a discrete version of PSO is employed in this study. The proposed PSO algorithm is tested by solving the beam-slab layout design problem of a rectangular floor. The validity of the proposed algorithm is observed from the obtained results.

Application of improved particle swarm optimization in orthogonal codes

In order to avoid the interference between the same model radars,the radar signals are generally asked to be orthogonal,so to design orthogonal signals with low auto-correlation and cross-correlation is the key to anti-jamming.Concerning the frequency-coded radar signals,improved Particle Swarm Optimization (PSO) algorithm was used to optimize the signal-coding sequences selected from which to meet the objective function to find the orthogonal codes group.Crossover and mutation idea of Genetic Algorithm (GA) was introduced to overcome the slow convergence and local optimum of Simple PSO (SPSO) algorithm,and design results were analyzed at last.The results show that the method is effective and feasible,and the performance is superior to that of SPSO,Simulated Annealing (SA) and Hybrid Genetic Algorithm (HGA).