Mixed constrained image filter design using particle swarm optimization

Liu

et al. 2022

Mathematics

An extended membrane system with a dynamic nested membrane structure, which is integrated with the evolution-communication mechanism of a cell-like P system with evolutional symport/antiport rules and active membranes (ECP), and the evolutionary mechanisms of particle swarm optimization (PSO) and improved PSO inspired by starling flock behavior (SPSO), named DSPSO-ECP, is designed and developed to try to break application restrictions of P systems in this paper. The purpose of DSPSO-ECP is to enhance the performance of extended membrane system in solving optimization problems. In the proposed DSPSO-ECP, the updated model of velocity and position of standard PSO, as basic evolution rules, are adopted to evolve objects in elementary membranes. The modified updated model of the velocity of improved SPSO is used as local evolution rules to evolve objects in sub-membranes. A group of sub-membranes for elementary membranes are specially designed to avoid prematurity through membrane creation and dissolution rules with promoter/inhibitor. The exchange and sharing of information between different membranes are achieved by communication rules for objects based on evolutional symport rules of ECP. At last, computational results, which are made on numerical benchmark functions and classic test images, are discussed and analyzed to validate the efficiency of the proposed DSPSO-ECP.

Section: Related Workmentioning

confidence: 99%

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

Liu

et al. 2022

Mathematics

“…The best position found by all particles in the swarm, called the global best, is represented by = { 1 , 2 , ⋅ ⋅ ⋅ }. At iteration , the new updated position and velocity of particle are determined by (1) and (2), respectively, in the following:…”

Section: The Standard Psomentioning

confidence: 99%

“…Compared with other evolutionary algorithms, PSO uses individual and global experiences of the particles and has well-balanced mechanism between its exploitation and exploration abilities [1]. Therefore, it has been successfully applied to many difficult optimization problems [2].…”

Section: Introductionmentioning

confidence: 99%

A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems

Mathematical Problems in Engineering

Liu

Sun

et al. 2018

A new method using collective responses of starling birds is developed to enhance the global search performance of standard particle swarm optimization (PSO). The method is named chaotic starling particle swarm optimization (CSPSO). In CSPSO, the inertia weight is adjusted using a nonlinear decreasing approach and the acceleration coefficients are adjusted using a chaotic logistic mapping strategy to avoid prematurity of the search process. A dynamic disturbance term (DDT) is used in velocity updating to enhance convergence of the algorithm. A local search method inspired by the behavior of starling birds utilizing the information of the nearest neighbors is used to determine a new collective position and a new collective velocity for selected particles. Two particle selection methods, Euclidean distance and fitness function, are adopted to ensure the overall convergence of the search process. Experimental results on benchmark function optimization and classic clustering problems verified the effectiveness of this proposed CSPSO algorithm.

“…Any input to any CLB may be connected either to a primary circuit input or to the output of a CLB in the preceding column. Any CLB can be programmed to implement one of the functions given in Table 1, 7,9 and all these functions operate with 8-bit operands and produce 8-bit results.…”

Section: Reconfi Gurable Processing Array For Image Fi Ltermentioning

confidence: 99%

Fault-tolerant image filter design using particle swarm optimization

Bao

Zhao

et al. 2011

Artif Life Robotics

Self Cite

Fault-tolerant image fi lter design using particle swarm optimizationRecently chip integration has become higher and higher, so that it increases the probability of faulty components, and the complexity of designs increases the probability of human error. The tolerance for faults diminishes as highly reliable systems are in demand. Therefore, the need for faulttolerant designs is stated as the long-term grand challenge. 6 To solve this problem, we have proposed a fault-tolerant image fi lter design using a GA (genetic algorithm), 7 and the experimental results show that the resultant image fi lter is indeed fault-tolerant. However, the problems of quality and processing time still remain, and which evolutionary method (e.g., GA, genetic programming (GP), particle swarm optimization (PSO), or ant colony optimization (ACO)) is most suitable has not been investigated.This article describes an effi cient image fi lter design for noise reduction using PSO on a reconfi gurable processing array, where some faulty confi gurable logic blocks (CLBs) may exist at random, as shown in Fig. 1. The mixed constraints on circuit complexity, power, and signal delay in both logic blocks and wires are optimized. In this design, fi rst evaluations of correctness, complexity, power, and signal delay are introduced to the fi tness function. Then PSO autonomously synthesizes an image fi lter which is simple, shows a better performance, and fi ts the reconfi gurable processing array with some faults. To verify the validity of our method, an image fi lter for noise reduction is experimentally synthesized.The organization of this article is as follows: a brief overview of PSO is given in the next section, Sect. 3 describes fault-tolerant design optimization for an image fi lter using PSO, and Sect. 4 shows the experimental results. Finally, Sect. 5 gives our conclusions. Particle swarm optimizationPSO is an algorithmic model of swarm intelligence that fi nds a solution to an optimization problem in a search space.Abstract This article describes a mixed constrained image fi lter design with fault tolerance using particle swarm optimization (PSO) on a reconfi gurable processing array. There may be some faulty confi gurable logic blocks (CLBs) in a reconfi gurable processing array. The proposed method with PSO autonomously synthesizes a fi lter fi tted to the reconfi gurable device with some faults in order to optimize the complexity and power of the circuit, and the signal delay in both the CLBs and the wires. An image fi lter for noise reduction is experimentally synthesized to verify the validity of our method. By evolution, the quality of the optimized image fi lter on a reconfi gurable device with a few faults is almost same as that on a device with no faults.