Hybridization of Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Function Optimization

2019 ◽  
Author(s):  
Sajad Ahmad Rather ◽  
P Shanthi Bala
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Gravitational Search ◽  
Constriction Coefficient

A hybrid constriction coefficient-based particle swarm optimization and gravitational search algorithm for training multi-layer perceptron

2020 ◽  
Vol 13 (2) ◽  
pp. 129-165 ◽  
Author(s):  
Sajad Ahmad Rather ◽  
P. Shanthi Bala
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Multi Layer Perceptron ◽  
Swarm Optimization ◽  
Local Optima ◽  
Content Type ◽  
Gravitational Search ◽  
Constriction Coefficient

PurposeIn this paper, a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA) has been employed for training MLP to overcome sensitivity to initialization, premature convergence, and stagnation in local optima problems of MLP.Design/methodology/approachIn this study, the exploration of the search space is carried out by gravitational search algorithm (GSA) and optimization of candidate solutions, i.e. exploitation is performed by particle swarm optimization (PSO). For training the multi-layer perceptron (MLP), CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error. Secondly, a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.FindingsThe experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems. Besides, it gives the best results for breast cancer, heart, sine function and sigmoid function datasets as compared to other participating algorithms. Moreover, CPSOGSA also provides very competitive results for other datasets.Originality/valueThe CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP. Basically, CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power. In the research literature, a little work is available where CPSO and GSA have been utilized for training MLP. The only related research paper was given by Mirjalili et al., in 2012. They have used standard PSO and GSA for training simple FNNs. However, the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms. In this paper, eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs. In addition, a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5% significance level to statistically validate the simulation results. Besides, eight state-of-the-art meta-heuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

scholarly journals Performance Comparison of Particle Swarm Optimization and Gravitational Search Algorithm to the Designed of Controller for Nonlinear System

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sahazati Md Rozali ◽  
Mohd Fua’ad Rahmat ◽  
Abdul Rashid Husain
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear System ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Tracking Error ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Backstepping Controller ◽  
Gravitational Search

This paper presents backstepping controller design for tracking purpose of nonlinear system. Since the performance of the designed controller depends on the value of control parameters, gravitational search algorithm (GSA) and particle swarm optimization (PSO) techniques are used to optimise these parameters in order to achieve a predefined system performance. The performance is evaluated based on the tracking error between reference input given to the system and the system output. Then, the efficacy of the backstepping controller is verified in simulation environment under various system setup including both the system subjected to external disturbance and without disturbance. The simulation results show that backstepping with particle swarm optimization technique performs better than the similar controller with gravitational search algorithm technique in terms of output response and tracking error.

Sign in / Sign up

Export Citation Format

Share Document