Study of Intelligent Optimization Methods Applied in the Fractional Fourier Transform

2012 ◽  
Vol 461 ◽  
pp. 323-328
Author(s):  
Han Qing Wang ◽  
Ping Bo Wang ◽  
Shu Zong Wang ◽  
Mao Lin Li
Keyword(s):  
Fourier Transform ◽  
Ant Colony Algorithm ◽  
Fractional Fourier Transform ◽  
Optimization Methods ◽  
Optimization Method ◽  
Extremum Seeking ◽  
Intelligent Optimization ◽  
Chaos Optimization ◽  
Computation Efficiency ◽  
Searching Method

In order to overcome the inefficiency shortcoming of traditional step-based searching method for extremum seeking in two-dimensional fractional Fourier domain, some typical intelligent optimization methods such as genetic algorithms, continuous ant colony algorithm, particle swarm optimization and chaos optimization method are introduced and applied successfully in fractional Fourier transform. The performances of the global optimization methods are compared with step-based method based on simulation. Results show that the COA optimization algorithm is much more preferable considering computation efficiency, precision and resolution in all the above mentioned optimization methods

Improved Harmony Search Algorithm with Crossover Operation

2013 ◽  
Vol 415 ◽  
pp. 353-356 ◽  
Author(s):  
Hong Gang Xia ◽  
Qing Liang Wang
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
Intelligent Optimization ◽  
New Variant ◽  
Improved Harmony Search ◽  
Improved Harmony Search Algorithm

Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method and it does depend on imitating the music improvisation process to generate a perfect state of harmony. However, intelligent optimization methods is easily trapped into local optimal, HS is no exception. In order to improve the performance of HS, a new variant of harmony search algorithm is proposed in this paper. The variant introduce a new crossover operation into HS, and design a strategy to adjust parameter pitch adjusting rate (PAR) and bandwidth (BW). Several standard benchmarks carried out to be tested. The numerical results demonstrated that the superiority of the proposed method to the HS and recently developed variants (IHS, and GHS).

A New Variant Harmony Search Algorithm with Random Mutation Strategy

2014 ◽  
Vol 1065-1069 ◽  
pp. 3425-3428
Author(s):  
Xiu Hong Zhao
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Random Mutation ◽  
Local Optima ◽  
Intelligent Optimization ◽  
New Variant ◽  
Mutation Strategy

Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method, which has been paid much attention recently. However, intelligent optimization methods are easily trapped into local optima, HS is no exception. In order to improve the performance of HS, a new variant of harmony search algorithm with random mutation strategy (HSRM) is proposed in this paper. The HSRM uses a random mutation strategy to replace the pitch adjusting operation, and dynamically adjust the key parameter pitch adjusting rate (PAR). Experiment results demonstrated that the proposed method is superior to the HS and recently developed variants (IHS, and GHS) and other meta-heuristic algorithm.

A New Variant Harmony Search Algorithm for Unconstrained Numerical Optimization Problems

2013 ◽  
Vol 365-366 ◽  
pp. 174-177
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
Intelligent Optimization ◽  
New Variant ◽  
Key Parameter

Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method and it does depend on imitating the music improvisation process to generate a perfect state of harmony. However, intelligent optimization methods is easily trapped into local optimal, HS is no exception. In order to modify the optimization performance of HS, a new variant of harmony search algorithm is proposed in this paper. The variant integrate the position updating of the particle swarm optimization algorithm with pitch adjustment operation, and dynamically adjust the key parameter pitch adjusting rate (PAR) and bandwidth (BW). Several standard benchmarks are to be tested. The numerical results demonstrated the superiority of the proposed method to the HS and recently developed variants (IHS, and GHS).

A Study on Multi-Colony Diploid Immune Algorithm and Its Application in Manufacture

2006 ◽  
Vol 532-533 ◽  
pp. 1056-1059
Author(s):  
Shu Bin Si ◽  
Shu Dong Sun ◽  
Pei Yong Hou ◽  
Jian Jun Yu
Keyword(s):  
Optimization Methods ◽  
Optimization Method ◽  
Immune Algorithm ◽  
Hard Problem ◽  
Benchmark Problem ◽  
Intelligent Optimization ◽  
Cell Scheduling ◽  
Np Hard Problem ◽  
Simulation Results ◽  
Set Up

Manufacture cell scheduling (MCS) is a NP-hard problem, and the traditional optimization methods have their limitations when dealing with MCS. Immune algorithm that simulates the biologic immunity system is an intelligent optimization method and has potential to solve MCS. Multi-colony diploid immune algorithm (MDIA) is put forward to solve MCS. Firstly, the model of MCS is set up. Then, a typical benchmark problem is simulated with MDIA and other algorithms. Finally, simulation results show that MDIA is both effective and superior to other algorithms.

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