Novel Particle Swarm Optimization and Its Application in Calibrating the Underwater Transponder Coordinates

Zheping Yan,Jiajia Zhou,Chao Deng,Benyin Li

doi:10.1155/2014/672412

Abstract

A novel improved particle swarm algorithm named competition particle swarm optimization (CPSO) is proposed to calibrate the Underwater Transponder coordinates. To improve the performance of the algorithm, TVAC algorithm is introduced into CPSO to present anextension competition particle swarm optimization(ECPSO). The proposed method is tested with a set of 10 standard optimization benchmark problems and the results are compared with those obtained through existing PSO algorithms,basic particle swarm optimization(BPSO),linear decreasing inertia weight particle swarm optimization(LWPSO),exponential inertia weight particle swarm optimization(EPSO), andtime-varying acceleration coefficient(TVAC). The results demonstrate that CPSO and ECPSO manifest faster searching speed, accuracy, and stability. The searching performance for multimodulus function of ECPSO is superior to CPSO. At last, calibration of the underwater transponder coordinates is present using particle swarm algorithm, and novel improved particle swarm algorithm shows better performance than other algorithms.

Highlights

Particle swarm optimization (PSO) technique is considered as one of the modern heuristic algorithms for optimization first proposed by Kennedy and Eberhart in 1995 [1]
The comparison results elucidate that the searching accuracy and stability ranging from low to high are listed as basic particle swarm optimization (BPSO), linear decreasing inertia weight particle swarm optimization (LWPSO), exponential inertia weight particle swarm optimization (EPSO), time-varying acceleration coefficient (TVAC), extension competition particle swarm optimization (ECPSO), and competition particle swarm optimization (CPSO) for unimodal function
It is obvious that the performances of ECPSO and CPSO are superior due to their advantage of obtaining the optimal speed direction and the searching efficiency, while, in the multimodal function, the CPSO algorithm is easy to trap into local minimum, and TVAC shows better performance than CPSO

Summary

Introduction

Particle swarm optimization (PSO) technique is considered as one of the modern heuristic algorithms for optimization first proposed by Kennedy and Eberhart in 1995 [1]. The motivation for the development of this method was based on the simulation of simplified animal social behaviors [2]. Several researches were carried out so far to analyze the performance of the PSO with different settings; for example, Shi and Eberhart [5] indicated that the optimal solution can be improved by varying the value of ω from 0.9 at the beginning of the search to 0.4 at the end of the search for most problems, and they introduced a method named TVIW with a linearly varying inertia weight over the generations. Epitropakis et al [9], motivated by the behavior and spatial characteristics of the social and cognitive experience of each particle in the swarm, develop a hybrid framework that combines the particle swarm optimization and the differential

Methods

Results

Conclusion