A Hybrid Differential Evolution Algorithm and Particle Swarm Optimization with Alternative Replication Strategy

2018 ◽  
pp. 487-497
Author(s):  
Lulu Zuo ◽  
Lei Liu ◽  
Hong Wang ◽  
Lijing Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Replication Strategy ◽  
Evolution Algorithm

scholarly journals Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm

2021 ◽  
Vol 11 (3) ◽  
pp. 1107
Author(s):  
Miloš Sedak ◽  
Božidar Rosić
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Planetary Gearbox ◽  
Multi Objective ◽  
Mutation Operators ◽  
Evolution Algorithm

This paper considers the problem of constrained multi-objective non-linear optimization of planetary gearbox based on hybrid metaheuristic algorithm. Optimal design of planetary gear trains requires simultaneous minimization of multiple conflicting objectives, such as gearbox volume, center distance, contact ratio, power loss, etc. In this regard, the theoretical formulation and numerical procedure for the calculation of the planetary gearbox power efficiency has been developed. To successfully solve the stated constrained multi-objective optimization problem, in this paper a hybrid algorithm between particle swarm optimization and differential evolution algorithms has been proposed and applied to considered problem. Here, the mutation operators from the differential evolution algorithm have been incorporated into the velocity update equation of the particle swarm optimization algorithm, with the adaptive population spacing parameter employed to select the appropriate mutation operator for the current optimization condition. It has been shown that the proposed algorithm successfully obtains the solutions of the non-convex Pareto set, and reveals key insights in reducing the weight, improving efficiency and preventing premature failure of gears. Compared to other well-known algorithms, the numerical simulation results indicate that the proposed algorithm shows improved optimization performance in terms of the quality of the obtained Pareto solutions.

Applying Bionic Intelligent Algorithm Combined with Finite Element Method to Optimize Cable

2013 ◽  
Vol 380-384 ◽  
pp. 1629-1632
Author(s):  
Zhi Peng Jiang ◽  
Xi Shan Wen ◽  
Xiao Qing Yuan
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Particle Swarm ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Evolution Algorithm ◽  
Element Method

The paper adopts bionic intelligent algorithm including particle swarm optimization and differential evolution algorithm combined with finite element method to optimize cable on the platform of ANSYS finite element soft. Parametric programming of a single-phase cable and a three-phase cable is accomplished to optimize the maximum electric field strength of cable insulation layer by using particle swarm optimization and differential evolution algorithm combined with finite element method, that provides enlightenment for optimizing high-voltage equipment in other aspects of electromagnetic field.

scholarly journals Hypercube optimization based global solution in numerical benchmark and sensor localization

2018 ◽  
Vol 7 (2.6) ◽  
pp. 88
Author(s):  
S R.Sujatha ◽  
M Siddappa
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Dynamic Weight ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm ◽  
Self Adaptive

An original learning algorithm for solving global numerical optimization problems is proposed. The proposed algorithm is strong stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The hypercube optimization algorithm includes the initialization and evaluation process, and searching space process. The designed HO algorithm is tested on specific benchmark functions. The comparative performance analysis have made against with other approaches of dynamic weight particle swarm optimization and self-adaptive differential evolution algorithm. Convergence characteristics of self-adaptive differential evolution algorithm has deliver the much better functional   value in compare to dynamic weight based particle swarm optimization.

Sign in / Sign up

Export Citation Format

Share Document