The problem of optimization algorithm is tuning the parameters. We need to enhance the performance of metaheuristic algorithms. Thus, is there any the state-of-the art parameter tuning algorithm for solving this problem? Thanks
Hi,
I recommend you reading this paper from Eiben and Smit: http://web.info.uvt.ro/~dzaharie/cne2012/proiecte/tehnici/statistical_analysis/parameter_tuning.pdf
Regards
Manuel.
There is a set of works in which optimum values of parameters are investigated. It is possible to tell that in each work this task is set and solved. Optimum values of parameters depend on a type of criterion function and dimension of a task. Proceeding from personal experience, for genetic algorithms we use the following parameters: quantity of individuals in population = 200, probability of crossing = 0.1, probability of a mutation = 0.01.
You might as well look at this no-parameters-to-adjust metaheuristic (self-adjusting, to be more precise):
http://web.unbc.ca/~bluskovi/pdo.pdf
Journal of Combinatorial Optimization, 2015, DOI 10.1007/s10878-014-9826-x.
The final publication is available at link.springer.com:
http://link.springer.com/article/10.1007/s10878-014-9826-x
you can look at adaptative PSO algorithms:
see for instance:
MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm
Yann Cooren,Maurice Clerc, Patrick Siarry
M. Fakhfakh, P. Siarry
Chapter title: MO-TRIBES for the Optimal Design of Analog Filters
In: Swarm Intelligence for Electric and Electronic Engineering
IGI-Global 2013
Y. Cooren, P. Siarry, M. Fakhfakh
‘‘Application of MO-TRIBES to the Design of Analog Electronic Circuits’’
The IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2009,
December 13-16, 2009. Hammamet, Tunisia
you can go to the following link
https://sites.google.com/site/tlborao/
Now self adaptive methods are emerging for each metaheuristic algorithms. i have used parameter adaptive HS algorithm for solving design optimization problems.
By using those self adaptive methods the problem of parameter tuning issue may be addressed
I would like to recommend you the following article, which may be useful to you.
https://www.researchgate.net/publication/280244004_A_comparative_study_of_metaheuristic_optimization_approaches_for_directional_overcurrent_relays_coordination
Article A comparative study of metaheuristic optimization approaches...
You might also examine this paper from forestry...
http://www.sciencedirect.com.proxy-remote.galib.uga.edu/science/article/pii/S1468121805001665
"Optimizing heuristic search in forest planning"
Nonlinear Analysis: Real World Applications, 2006, 7: 1284-1297
You might also examine this paper from forestry...
http://www.sciencedirect.com.proxy-remote.galib.uga.edu/science/article/pii/S1468121805001665
"Optimizing heuristic search in forest planning"
Nonlinear Analysis: Real World Applications, 2006, 7: 1284-1297
PSO codes in MATLAB
https://www.researchgate.net/publication/296636431_Codes_in_MATLAB_for_Particle_Swarm_Optimization?ev=prf_pub
Code Codes in MATLAB for Particle Swarm Optimization
I recommend the following articles
Karafotias, Giorgos, Mark Hoogendoorn, and Agoston E. Eiben.(2015). Parameter Control in Evolutionary Algorithms: Trends and Challenges. IEEE Trans. Evolutionary Computation, 19(2), 167-187.
Yang, X. S., Deb, S., Loomes, M., & Karamanoglu, M. (2013). A framework for self-tuning optimization algorithm. Neural Computing and Applications, 23(7-8), 2051-2057.
Eiben, A. E., & Smit, S. K. (2011). Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm and Evolutionary Computation, 1(1), 19-31.
https://pdfs.semanticscholar.org/2358/139ba8bb50fe2e4f38c5d6200623d7de2843.pdf
https://pdfs.semanticscholar.org/ac1f/f0dc2f86ad8dd4780b4a765cca853ab07277.pdf
http://arxiv.org/pdf/1312.5667
Fuzzy logic control can also be used to dynamically adapt parameters of optimization algorithm.Yo can refer to following article on improving the performance of Differential evolution using fuzzy logic.
he Above Procedure:Fa, P., & Soria, J. (2016). Differential Evolution with Fuzzy Logic for Dynamic Adaptation of Parameters in Mathematical Function Optimization. In Imprecision and Uncertainty in Information Representation and Processing (pp. 361-374). Springer International Publishing.
Valdez, F., Melin, P., & Castillo, O. (2014). A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation. Expert Systems with Applications, 41(14), 6459-6466.
http://link.springer.com/chapter/10.1007%2F978-3-319-26302-1_21
Article A survey on nature-inspired optimization algorithms with fuz...
- Badges
- Science topic
- Similar topics
- Filing