A Genetic Algorithm and Particle Swarm Optimization Approach for Lower Order Modelling of Linear Time Invariant Discrete Systems

2007 ◽  
Author(s):  
S.N. Sivanandam ◽  
S.N. Deepa
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Lower Order ◽  
Linear Time ◽  
Particle Swarm ◽  
Discrete Systems ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Linear Time Invariant ◽  
Time Invariant

Hybridization of Biogeography-Based

2013 ◽  
Vol 2 (3) ◽  
pp. 86-101 ◽  
Author(s):  
Provas Kumar Roy ◽  
Dharmadas Mandal
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimal Power Flow ◽  
Particle Swarm ◽  
Population Based ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Swarm Optimization

The aim of this paper is to evaluate a hybrid biogeography-based optimization approach based on the hybridization of biogeography-based optimization with differential evolution to solve the optimal power flow problem. The proposed method combines the exploration of differential evolution with the exploitation of biogeography-based optimization effectively to generate the promising candidate solutions. Simulation experiments are carried on standard 26-bus and IEEE 30-bus systems to illustrate the efficacy of the proposed approach. Results demonstrated that the proposed approach converged to promising solutions in terms of quality and convergence rate when compared with the original biogeography-based optimization and other population based optimization techniques like simple genetic algorithm, mixed integer genetic algorithm, particle swarm optimization and craziness based particle swarm optimization.

Sign in / Sign up

Export Citation Format

Share Document