Dynamic Synthesis of a Multibody System: A Comparative Study Between Genetic Algorithm and Particle Swarm Optimization Techniques

Author(s):  
Mohamed Amine Ben Abdallah ◽  
Imed Khemili ◽  
Med Amine Laribi ◽  
Nizar Aifaoui
2012 ◽  
Vol 498 ◽  
pp. 115-125 ◽  
Author(s):  
H. Hachimi ◽  
Rachid Ellaia ◽  
A. El Hami

In this paper, we present a new hybrid algorithm which is a combination of a hybrid genetic algorithm and particle swarm optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO) for the global optimization. Denoted asGA-PSO, this hybrid technique incorporates concepts fromGAandPSOand creates individuals in a new generation not only by crossover and mutation operations as found inGAbut also by mechanisms ofPSO. The performance of the two algorithms has been evaluated using several experiments.


2019 ◽  
Vol 23 (4) ◽  
pp. 2343-2350
Author(s):  
Xiao-Hua Yang ◽  
Tong Liu ◽  
Yu-Qi Li

A bio-retention system is an important measure for non-point source pollution control. In order to improve the calculation precision for parameter optimization of the moisture movement in a bio-retention system, a real-encoded genetic algorithm based on the fractional-order operation is proposed, in which initial populations are generated by random mapping, and the searching range is automatically renewed with the excellent individuals by fractional-order particle swarm optimization operation. Its efficiency is verified experimentally. The results indicate that the absolute error by the fractional-order operation decreases by 67.73%, 62.23%, and 4.16%, and the relative error decreases by 42.88%, 35.76%, and 6.77%, respectively, compared to those by the standard binary-encoded genetic algorithm, random algorithm, and the particle swarm optimization algorithm. The fractional-order operation has higher precision and it is good for the practical parameter optimization in ecological environment systems.


2019 ◽  
Vol 8 (2) ◽  
pp. 40
Author(s):  
Saman M. Almufti ◽  
Amar Yahya Zebari ◽  
Herman Khalid Omer

This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation.  


2013 ◽  
Vol 2 (3) ◽  
pp. 86-101 ◽  
Author(s):  
Provas Kumar Roy ◽  
Dharmadas Mandal

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.


Author(s):  
Jagat Kishore Pattanaik ◽  
Mousumi Basu ◽  
Deba Prasad Dash

AbstractThis paper presents a comparative study for five artificial intelligent (AI) techniques to the dynamic economic dispatch problem: differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing. Here, the optimal hourly generation schedule is determined. Dynamic economic dispatch determines the optimal scheduling of online generator outputs with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators. The AI techniques for dynamic economic dispatch are evaluated against a ten-unit system with nonsmooth fuel cost function as a common testbed and the results are compared against each other.


Inverted Pendulum is a popular non-linear, unstable control problem where implementation of stabilizing the pole angle deviation, along with cart positioning is done by using novel control strategies. Soft computing techniques are applied for getting optimal results. The evolutionary computation forms the key research area for adaptation and optimization. The approach of finding optimal or near optimal solutions to the problem is based on natural evolution in evolutionary computation. The genetic algorithm is a method based on biological evolution and natural selection for solving both constrained and unconstrained problems. Particle swarm optimization is a stochastic search method inspired by collective behavior of animals like flocking of birds, schooling of fishes, swarming of bees etc. that is suited to continuous variable problems. These methods are applied to the inverted pendulum problem and their performance studied.


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