Hybrid GWOCS Optimization

Hybrid Grey Wolf and Cuckoo Search Optimization

(71 customer reviews)

$0.00

Total downloads: 372

This free code is for hybrid GWOCS optimization algorithm which combines the global converging power of GWO with CS. We tested it on benchmark optimization functions and found GWOCS performing better than GWO alone. This repository includes:

  • Complete code for hybrid GWO CS optimization
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Description

GWO operates on the basis of hierarchy in the group. Once all wolves are initialized with some random feed then fitness function is calculated for each wolf. In a group, 10-20 wolves are considered. Out of them the one with minimum fitness function (as GWO works to reduce the distance between prey and wolf and optimal position is the prey position whereas CS works for maximization of profit ) is considered as leader of the group and \(\alpha_{wolf}\) , followed by two more wolf with corresponding decreasing fitness function as $$\beta_{wolf}$$  and $$\gamma_{wolf}$$ . The mean of these positions is considered as an optimal position of the wolf in that iteration.

$$GWO optimal position=\frac{\alpha_{wolf}+\beta_{wolf}+\gamma_{wolf}}{3}$$

Top three wolf positions are updated by equation 3.1 and 3.2 and the new position is the mean of these three. In GWO, to move towards the prey, the distance between prey and golf is minimized and changed over time. The step size by which wolf moves are randomly weighted by a constant which leads to falling it into local optima. This problem is solved by cuckoo search algorithm which updates the current position based on the best position so far. CS optimality more relies on other habitat groups rather than only time. To make it hybrid we updated the best three locations of wolves in the group by CS method which update it by a step of with angle. The step size is updated as:

$$stepsize=wXstepX(s-best)$$

where ‘s’ is the position of $$\alpha_{wolf}$$ $$\beta_{wolf}$$ $$\gamma_{wolf}$$

‘step’ is the previous step size of the cuckoo movement

‘step size’ is the updated step size

‘w’ is the weighting factor = 0.001

The position of the cuckoo is now updated as:

where is the deviation of the cuckoo and a random quantity

Using the above equation they are updated to new positions and handle will get back to GWO form CS. Now GWO takes mean of all three best positions again and tradeoff the local optima error in this hybrid.

The hybrid of Grey Wolf with Particle Swarm Optimization can also be checked here.

71 reviews for Hybrid Grey Wolf and Cuckoo Search Optimization

  1. Anonymous –

    Excellent

  2. Ram Kumar R P (verified owner) –

    Excellent Codes

  3. neeraj.arora (verified owner) –

    zxx

  4. k.sudheer (verified owner) –

    Greatly useful thesis available

  5. k.sudheer (verified owner) –

    very useful

  6. Fawad (verified owner) –

    Excellent

  7. Fawad (verified owner) –

    Good codes

  8. venkat.reddy (verified owner) –

    great

  9. venkat.reddy (verified owner) –

    great

  10. praveen.hipparge (verified owner) –

    good

  11. shiffali.goyal (verified owner) –

    Gr8

  12. ramahk92 (verified owner) –

    Thanks for Providing Code.

  13. admin (verified owner) –

    Nicely written code

  14. alok.kumar (verified owner) –

    Thanks for your support.

  15. alok.kumar (verified owner) –

    Thanks.

  16. sameer.kumthekar (verified owner) –

    best

  17. vankani.arjun (verified owner) –

    Nice project !!

  18. garba.abdulrauf (verified owner) –

    thank you

  19. preethi.g (verified owner) –

    good

  20. mohammed.dhriyyef (verified owner) –

    merci

  21. xuexi (verified owner) –

    good

  22. hasan (verified owner) –

    Thanks

  23. richa.singh (verified owner) –

    nice

  24. umit.cetinkaya (verified owner) –

    It is so good

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    very useful website

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  28. daniela.irimia (verified owner) –

    Usefull, I hope!

  29. zhang.xin (verified owner) –

    nice code

  30. chou_aib (verified owner) –

    great

  31. thiyagarajan.n (verified owner) –

    good

  32. thiyagarajan.n (verified owner) –

    good

  33. thiyagarajan.n (verified owner) –

    good

  34. thiyagarajan.n (verified owner) –

    good

  35. chou_aib (verified owner) –

    Great

  36. wisamjr (verified owner) –

    very good and quick

  37. Md. Mohin Islam (verified owner) –

    thank you.

  38. vishnupriya.vijayan (verified owner) –

    nice job!!!

  39. akash.raghuvanshi (verified owner) –

    good

  40. saranya.gunasekar (verified owner) –

    thank u very much

  41. saranya.gunasekar (verified owner) –

    good

  42. ibrahim.alnaib (verified owner) –

    good

  43. ibrahim.alnaib (verified owner) –

    good

  44. durgendra kumar.kanigiri (verified owner) –

    k

  45. JacobsYoung (verified owner) –

    nice

  46. JacobsYoung (verified owner) –

    good

  47. saranya.gunasekar (verified owner) –

    good

  48. anuj.goel (verified owner) –

    Excellent and appreciative initiative.

  49. john.seed (verified owner) –

    good

  50. john.seed (verified owner) –

    good

  51. thiyagarajan.n-1218 (verified owner) –

    thankyou

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  54. nenisi.j (verified owner) –

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  55. mostafaham (verified owner) –

    great

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    good

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    excellent

  59. prashant.kulkarni-0716 (verified owner) –

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  60. prashant.kulkarni-0716 (verified owner) –

    EXCELLENT

  61. prashant.kulkarni-0716 (verified owner) –

    EXCELLENT WORK

  62. manishshukla8840 (verified owner) –

    na

  63. manishshukla8840 (verified owner) –

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  64. manishshukla8840 (verified owner) –

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  65. manishshukla8840 (verified owner) –

    ZGoog theme but all topics was not covered in this portal

  66. jayati.vaish (verified owner) –

    good

  67. jayati.vaish (verified owner) –

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  68. jayati.vaish (verified owner) –

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  70. abc (verified owner) –

    good

  71. ibrahim.alnaib (verified owner) –

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  72. prabhat.kumar (verified owner) –

    thanks a lot

  73. zheng.wang (verified owner) –

    good

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