1 / 23

Computational Intelligence Based on the Behavior of Cats

Computational Intelligence Based on the Behavior of Cats. Shu-Chuang Chu and Pei-Wei Tsai International Journal of Innovative Computing, Information and Control, Volume 3, Number 1, February 2007. Cat Swarm Optimization (CSO). Introduction of CSO Cats’ behavior Cat Swarm Optimization

marlis
Télécharger la présentation

Computational Intelligence Based on the Behavior of Cats

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Computational Intelligence Based on the Behavior of Cats Shu-Chuang Chu and Pei-Wei Tsai International Journal of Innovative Computing, Information and Control, Volume 3, Number 1, February 2007

  2. Cat Swarm Optimization (CSO) • Introduction of CSO • Cats’ behavior • Cat Swarm Optimization • Experimental Results • Conclusion

  3. Introduction of CSO • Ant Colony Optimization and Particle Swarm Optimization (PSO) are the most common optimization algorithms, which simulates the behaviors of creatures. • We propose a new optimization algorithm, Cat Swarm Optimization (CSO), by modeling the behaviors of cats. • After discussing the algorithm, which we proposed, we compare CSO with PSO-type algorithms and present the conclusion.

  4. Cats’ behaviors • Rest indolently most of time when they are awake. • Move speedily when they are tracing some targets. • Curious about all kinds of moving things.

  5. Cat Swarm Optimization • Solution Set -- Cat: - Cat • M-dimensional Position. • Velocities for each dimension. • A fitness value. • Seeking/Tracing flag.

  6. Cat Swarm Optimization (2) • Sub-models: - Seeking Mode: • To model the situation of the cat, which is resting, looking around and seeking the next position to move to. - Tracing Mode: • For modeling the case of the cat is tracing some targets.

  7. Cat Swarm Optimization (3) • Parameters: - Seeking Mode: • SMP • To be used to define the size of seeking memory for each cat, which indicates the points sought by the cat. • SPC - A Boolean variable, which decides whether the point, where the cat is already standing, will be one of the candidates to move to. • CDC • To disclose how many dimensions will be varied. • SRD - To declare the mutative ratio for the selected dimensions.

  8. Cat Swarm Optimization (4) • Parameters: - Tracing Mode: • r1 • A random variable belongs to [0,1]. • c1 - A constant, which is set to 2 in the experiments.

  9. Cat Swarm Optimization (5) Start Create N cats Initialize the position, velocities, and the flag of every cat. Evaluate the cats according to the fitness function and keep the position of the cat, which has the best fitness value. Moving Catk is in the seeking mode? Yes No Apply catk into tracing mode process Apply catk into seeking mode process Re-pick number of cats and set them into tracing mode according to MR, and set the others into seeking mode. No Terminate? Yes End

  10. Seeking Mode Operation Step1: Make j copies of the present position of catk, where j = SMP. If the value of SPC is true, let j = (SMP-1), then retain the present position as one of the candidates. Step2: For each copy, according to CDC, select the dimensions as the candidates for changing. Step3: For the dimensions, which are selected in step 2, in each copy, randomly plus or minus SRD percents of the present values and replace the old ones. Step4: Calculate the fitness values (FS) of all candidate points. Step5: If all FS are not exactly equal, calculate the selecting probability of each candidate point by equation (1), otherwise set all the selecting probability of each candidate point be 1. Step6: Randomly pick the point to move to from the candidate points, and replace the position of catk. , where 0 < i < j, for minimize: let FSb = FSmax (1)

  11. Tracing Mode Step1: Update the velocities for every dimension (vk,d) according to equation (2). Step2: Check if the velocities are in the range of maximum velocity. In case the new velocity is over-range, set it be equal to the limit. Step3: Update the position of catk according to equation (3). , where d = 1,2,…,M (2) (3)

  12. Parameter Settings The parameter settings for the experiments are listed as follows: Parameter settings for CSO. Parameter settings for PSO and PSO with WF. • Iteration: 2000 • Population Size: 160 • Dimension: 30 • Rounds for Average: 50

  13. Parameter Settings (2) Maximum velocities for PSO and PSO with WF.

  14. Experimental Result • Test Functions: (4) (5) (6) (7) (8) (9)

  15. Experimental Result (1)

  16. Experimental Result (2)

  17. Experimental Result (3)

  18. Experimental Result (4)

  19. Experimental Result (5)

  20. Experimental Result (6)

  21. Experimental Result (7)

  22. Conclusion for CSO • We present a new algorithm, Cat Swarm Optimization, through modeling the behaviors of cat to solve the optimization problems. • The experimental results indicate that CSO can better improve the performance on finding the global best solutions.

  23. Thank you for your attention 蔡沛緯pwtsai@bit.kuas.edu.tw

More Related