ANALYSIS AND DESIGN OF CUCKOO ALGORITHM
Cuckoo search (CS) is an optimization algorithm developed by Xin-she Yang and Suash Deb in 2009. It was inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds (of other species). Some host birds can engage direct conflict with the intruding cuckoos. For example, if a host bird discovers the eggs are not their own, it will either throw these alien eggs away or simply abandon its nest and build a new nest elsewhere. Some cuckoo species such as the New World brood-parasitic Tapera have evolved in such a way that female parasitic cuckoos are often very specialized in the mimicry in colors and pattern of the eggs of a few chosen host species Cuckoo …show more content…
Each egg in a nest represents a solution, and a cuckoo egg represents a new solution. The aim is to use the new and potentially better solutions (cuckoos) to replace a not-so-good solution in the nests.
The three basic principles are:
1) Each cuckoo lays one egg at a time, and dumps its egg in a randomly chosen nest.
2) The best nests with high quality of eggs will carry over to the next generation.
3) The number of available host nests is fixed and the host bird finds the egg laid by the cuckoo having fixed probability.
The random walks and the Lvy flights are applied in the calculation of the new solutions of the generic equation. Here, the random walks are linked with the similarity between a cuckoo's egg and the host's egg. An important issue is the applications of Levy flights and random walks in the generic equation for generating new solutions. The step size S determines how far a random walker can go for a fixed number of iterations. If s is too large, then the new solution generated will be too far away from the old solution (or even jump out side of the bounds). Then, such a move is unlikely to be accepted. If s is too small, the change is too small to be significant, and consequently such search is not efficient. So a proper step size is important to maintain the search as efficient as possible. This constraint is met by applying the Lvy flight as the …show more content…
3] Feature selection: Assigning the parameter N: dimension of search space/no. of host nest.
G: maximum generation.
C: total number of cuckoos.
4] Generation step: t
5] while (t< G)
{
For (i=0, iFj)
{
Fj=Fi;
}
End for
• A fraction Pa of the worst solution is abandoned and new ones are built.
• Rank the solutions as per the fitness.
• Find the current best solution (nest).
• Pass the current best solutions to the next generation.
End while.
6] Pick up the solution (nest) with the maximum egg i.e. maximum fitness.
7] This solution is the output of the feature selection procedure.
8] Classifier:-
Euclidean distance is defined as the straight-line distance between two points. Pi, Qi: co-ordinates of points in dimension i. Euclidean distance is employed to measure the similarity between the feature subset of query image and the reference feature subsets in the image gallery. The image which has the smallest distance with the image under test is considered to be the required image.
CONCLUSTION:
In this chapter, a brief explanation about the design of CUCKOO ALGORITHM method, its theoretical analysis of cuckoo algorithm method modulation are
Cuckoo search (CS) is an optimization algorithm developed by Xin-she Yang and Suash Deb in 2009. It was inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds (of other species). Some host birds can engage direct conflict with the intruding cuckoos. For example, if a host bird discovers the eggs are not their own, it will either throw these alien eggs away or simply abandon its nest and build a new nest elsewhere. Some cuckoo species such as the New World brood-parasitic Tapera have evolved in such a way that female parasitic cuckoos are often very specialized in the mimicry in colors and pattern of the eggs of a few chosen host species Cuckoo …show more content…
Each egg in a nest represents a solution, and a cuckoo egg represents a new solution. The aim is to use the new and potentially better solutions (cuckoos) to replace a not-so-good solution in the nests.
The three basic principles are:
1) Each cuckoo lays one egg at a time, and dumps its egg in a randomly chosen nest.
2) The best nests with high quality of eggs will carry over to the next generation.
3) The number of available host nests is fixed and the host bird finds the egg laid by the cuckoo having fixed probability.
The random walks and the Lvy flights are applied in the calculation of the new solutions of the generic equation. Here, the random walks are linked with the similarity between a cuckoo's egg and the host's egg. An important issue is the applications of Levy flights and random walks in the generic equation for generating new solutions. The step size S determines how far a random walker can go for a fixed number of iterations. If s is too large, then the new solution generated will be too far away from the old solution (or even jump out side of the bounds). Then, such a move is unlikely to be accepted. If s is too small, the change is too small to be significant, and consequently such search is not efficient. So a proper step size is important to maintain the search as efficient as possible. This constraint is met by applying the Lvy flight as the …show more content…
3] Feature selection: Assigning the parameter N: dimension of search space/no. of host nest.
G: maximum generation.
C: total number of cuckoos.
4] Generation step: t
5] while (t< G)
{
For (i=0, iFj)
{
Fj=Fi;
}
End for
• A fraction Pa of the worst solution is abandoned and new ones are built.
• Rank the solutions as per the fitness.
• Find the current best solution (nest).
• Pass the current best solutions to the next generation.
End while.
6] Pick up the solution (nest) with the maximum egg i.e. maximum fitness.
7] This solution is the output of the feature selection procedure.
8] Classifier:-
Euclidean distance is defined as the straight-line distance between two points. Pi, Qi: co-ordinates of points in dimension i. Euclidean distance is employed to measure the similarity between the feature subset of query image and the reference feature subsets in the image gallery. The image which has the smallest distance with the image under test is considered to be the required image.
CONCLUSTION:
In this chapter, a brief explanation about the design of CUCKOO ALGORITHM method, its theoretical analysis of cuckoo algorithm method modulation are