# Hybrid Grey Wolf and Cuckoo Search Optimization

(71 customer reviews)

\$0.00

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

## 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 $$\beta_{wolf}$$ Ã‚Â 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

Excellent

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.

Nicely written code

14. alok.kumar (verified owner)

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

25. m.m (verified owner)

dgds

26. otuo.acheampong (verified owner)

very useful website

27. otuo.acheampong (verified owner)

very useful website

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

52. thiyagarajan.n-1218 (verified owner)

nice

53. thiyagarajan.n-1218 (verified owner)

goood

54. nenisi.j (verified owner)

heh

55. mostafaham (verified owner)

great

56. mostafaham (verified owner)

good

57. syber.shuai (verified owner)

nice

58. prashant.kulkarni-0716 (verified owner)

excellent

59. prashant.kulkarni-0716 (verified owner)

excellent

60. prashant.kulkarni-0716 (verified owner)

EXCELLENT

61. prashant.kulkarni-0716 (verified owner)

EXCELLENT WORK

62. manishshukla8840 (verified owner)

na

63. manishshukla8840 (verified owner)

na n

64. manishshukla8840 (verified owner)

na n

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)

good

68. jayati.vaish (verified owner)

good

69. anthony.dibia (verified owner)

Nice site

70. abc (verified owner)

good

71. ibrahim.alnaib (verified owner)

good

72. prabhat.kumar (verified owner)

thanks a lot

73. zheng.wang (verified owner)

good

Only logged in customers who have purchased this product may leave a review.