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SwarmFish1002CMOS
  • Classification:Numerical Algorithm-Artificial Intelligence - matlab
  • Development Tool:matlab
  • Sise:2.52 MB
  • Upload time:2014/7/17 0:57:00
  • Uploader:egl0
  • Download Statistics:
Description
another good matlab code for fish swarm optimization


SGA__fitness_obj.jpg

File list:
SwarmFish1002CMOS
................\data
................\....\CMOS4007
................\....\........\CMOS4007_bathtub.eps
................\....\........\CMOS4007_bathtub.fig
................\....\........\CMOS4007_bathtub.jpg
CMOS4007_bathtub.jpg
................\....\........\CMOS4007_bathtub.pdf
................\....\........\CMOS4007_bathtub_compare_g10.eps
................\....\........\CMOS4007_bathtub_compare_g10.fig
................\....\........\CMOS4007_bathtub_compare_g10.jpg
CMOS4007_bathtub_compare_g10.jpg
................\....\........\CMOS4007_bathtub_compare_g10.pdf
................\....\........\CMOS4007_bathtub_compare_g20.eps
................\....\........\CMOS4007_bathtub_compare_g20.fig
................\....\........\CMOS4007_bathtub_compare_g20.jpg
CMOS4007_bathtub_compare_g20.jpg
................\....\........\CMOS4007_bathtub_compare_g20.pdf
................\....\........\CMOS4007_bathtub_compare_g30.eps
................\....\........\CMOS4007_bathtub_compare_g30.fig
................\....\........\CMOS4007_bathtub_compare_g30.jpg
CMOS4007_bathtub_compare_g30.jpg
................\....\........\CMOS4007_bathtub_compare_g30.pdf
................\....\........\CMOS4007_bathtub_compare_g40.eps
................\....\........\CMOS4007_bathtub_compare_g40.fig
................\....\........\CMOS4007_bathtub_compare_g40.jpg
CMOS4007_bathtub_compare_g40.jpg
................\....\........\CMOS4007_bathtub_compare_g40.pdf
................\....\........\CMOS4007_bathtub_compare_g50.eps
................\....\........\CMOS4007_bathtub_compare_g50.fig
................\....\........\CMOS4007_bathtub_compare_g50.jpg
CMOS4007_bathtub_compare_g50.jpg
................\....\........\CMOS4007_bathtub_compare_g50.pdf
................\....\........\fitnessg50.eps
................\....\........\fitnessg50.fig
................\....\........\fitnessg50.jpg
fitnessg50.jpg
................\....\........\fitnessg50.pdf
................\....\........\fitnessg50_maxminmean.eps
................\....\........\fitnessg50_maxminmean.fig
................\....\........\fitnessg50_maxminmean.jpg
fitnessg50_maxminmean.jpg
................\....\........\fitnessg50_maxminmean.pdf
................\....\........\fitnessg50_maxminmean_10times.eps
................\....\........\fitnessg50_maxminmean_10times.fig
................\....\........\fitnessg50_maxminmean_10times.jpg
fitnessg50_maxminmean_10times.jpg
................\doc
................\...\160.pdf
................\...\ICQR2MSE_BATHTUB_CR.PDF
................\...\links.txt
................\...\parameters.txt
................\...\SwarmsLAB_01_fish.pdf
................\figs
................\....\SGA__fitness_2obj.eps
................\....\SGA__fitness_2obj.jpg
SGA__fitness_2obj.jpg
................\....\SGA__fitness_obj.eps
................\....\SGA__fitness_obj.jpg
SGA__fitness_obj.jpg
................\assign_visual.p
................\list_current_dir_files.p
................\readme.txt
................\SECF__assess_mAP.p
................\SECF__assess_mAP_plot.p
................\SECF__assess_R2.p
................\SECF__data_generation_CMOS.m
................\SECF__index_gini.p
................\SGA_FITNESS_function.m
................\SGA__any_negative.p
................\SGA__cell2str.p
................\SGA__delete_whitespace.p
................\SGA__entry_SO_std.p
................\SGA__fitness_evaluating.p
................\SGA__fitness_plot.p
................\SGA__FLC_MF_bathtub.p
................\SGA__FLC_MF_rbf.p
................\SGA__FLC_MF_rbf_replot.p
................\SGA__FLC_normalisation.p
................\SGA__FLC_normalisation_reverse.p
................\SGA__get_max_number_of_row.p
................\SGA__howmany.p
................\SGA__is_same_array.p
................\SGA__str2cell.p
................\SGA__where_is.p
................\SGA__where_is_NaN.p
................\SGA__where_is_NON_NaN.p
................\SwarmFish_demo_SO_std.m
................\SwarmFish_I_crowd.txt
................\SwarmFish_I_max_confines.txt
................\SwarmFish_I_max_generation.txt
................\SwarmFish_I_min_confines.txt
................\SwarmFish_I_population.txt
................\SwarmFish_I_steps.txt
................\SwarmFish_I_testnumber.txt
................\SwarmFish_I_try_number.txt
................\SwarmFish_I_visual.txt
................\SwarmFish_O_bestfitness.txt
................\SwarmFish_O_best_result_space.txt
................\SwarmFish_O_fitness_MAX.mat
................\SwarmFish_O_fitness_MEAN.mat
................\SwarmFish_O_fitness_MIN.mat
................\SwarmFish_O_mAP_fitness.mat
................\SwarmFish_O_maxfitness.txt
................\SwarmFish_O_meanfitness.txt
................\SwarmFish_O_minfitness.txt
................\SwarmFish_O_result_space.mat
................\SwarmFish__behaviour.p
................\SwarmFish__behaviour_flag.p
................\SwarmFish__behaviour_follow.p
................\SwarmFish__behaviour_search.p
................\SwarmFish__behaviour_swarm.p
................\SwarmFish__bulletin.p
................\SwarmFish__entry_SO_std.p
................\SwarmFish__get_distance.p
................\SwarmFish__initialisation.p
................\SwarmFish__min_besides.p
................\SwarmFish__RAND.p
................\SwarmFish__updating_ii.p
................\SwarmFish__updating_ij.p
................\SwarmFish__updating_random.p
................\SwarmFish__visual_generation.p
................\timebar.p
Related source code
[Multi-Objective Particle Swarm Optim...] - This package contains the functions for a MOPSO. MOPSO is among the most powerful algorithms in multi-objective optimizations.
[particle swarm optimisation] - particle swarm optimisation code
[Particle swarm optimization] - PSO in matlab
[Training Neural Network by Particle ...] - optimizing simple neural networks weights by particle swarm optimization
[particle swarm optimization] - The program is a binary version of the PSO procedure can be used as a binary version of PSO in a variety of applications, the template program.
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