A Memetic Algorithm with Particle Swarm Optimization and Differential Evolution Algorithm to Rescheduling Problem in Multi-Agent System

2012 ◽  
Vol 6 (1) ◽  
pp. 530-533
Author(s):  
Fuqing Zhao ◽  
Yahong Yang ◽  
Junbiao Wang ◽  
. Jonrinaldi
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Memetic Algorithm ◽  
Differential Evolution Algorithm ◽  
Particle Swarm ◽  
Multi Agent System ◽  
Agent System ◽  
Swarm Optimization ◽  
Multi Agent ◽  
Evolution Algorithm

scholarly journals IMPLEMENTASI ALGORITMA PARTICLE SWARM OPTIMIZATION UNTUK PENENTUAN POSISI STRATEGIS AGENT PADA SIMULASI ROBOT SEPAK BOLA DUA DIMENSI

2012 ◽  
Vol 1 (2) ◽  
Author(s):  
Galih Hermawan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Multi Agent System ◽  
Agent System ◽  
Swarm Optimization ◽  
Multi Agent

Robot sepak bola merupakan perpaduan antara olah raga, teknologi robotika, dan multi agent system. Untuk mencapai tujuan, selain membutuhkan kecerdasan individu, juga menuntut kemampuan kerja sama antar individu. Posisi robot ketika bermain mempengaruhi kemampuan robot dalam bekerja sama dan memilih aksi yang sesuai. Dalam tulisan ini akan disajikan hasil penelitian kami dalam penerapan algoritma particle swarm optimization (PSO) untuk menentukan posisi robot ketika bermain sepak bola. Hasil pengujian pada simulasi RoboCup Soccer dua dimensi menunjukkan bahwa tim robot sepak bola yang menggunakan algoritma PSO memiliki performa bermain lebih baik ketimbang tim sebelum menggunakan algoritma PSO.

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.

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