Cuttlefish Optimization Algorithm

In computer science, the Cuttlefish Optimization Algorithm (CFA) is a population-based search algorithm inspired by skin color changing behaviour of Cuttlefish which was developed in 2013 It has two global search and two local search.

Cuttlefish Optimization Algorithm (CFA)

The six cases of reflection used by the Cuttlefish

The algorithm considers two main processes: Reflection and Visibility. Reflection process simulates the light reflection mechanism, while visibility simulates the visibility of matching patterns. These two processes are used as a search strategy to find the global optimal solution. The formulation of finding the new solution (newP) by using reflection and visibility is as follows:

$newP=Reflection+Visibility$

CFA divide the pubulaion to 4 Groups (G1, G2, G3 and G4). For G1 the algorithm appling case 1 and 2 (the interaction between chromatophores and iridophores) to produce a new solutions. These two cases are used as a golobal search. For G2, the algorithm uses case 3 (Iridophores reflection opaerator) and case 4 (the interaction between Iridophores and chromatophores) to produces a new solutions) as a local search. While for G3 the interaction between the leucophores and chromatophores (case 5) is used to produse solutions around the best solution (local search). Finaly for G4, case 6 (reflection operator of leucophores) is used as a global search by reflecting any incoming light as it with out any modification. The main step of CFA is discribed as follows:

   1 Initialize population (P[N]) with random solutions, Assign the values of r1, r2, v1, v2.
   2 Evaluate the population and Keep the best solution.
   3 Divide population into four groups (G1, G2, G3 and G4).
   4 Repeat 
        4.1 Calculate the average value of the best solution.
        4.2 for (each element in G1)
                     generate new solution using Case(1 amd 2)
        4.3 for (each element in G2)
                     generate new solution using Case(3 amd 4)
        4.4 for (each element in G3)
                     generate new solution using Case(5)
        4.5 for (each element in G4)
                     generate new solution using Case(6)
        4.6 Evluate the new solutions 
   5. Untile (stopping criterion is met)
   6. Return the best solution

Equations that are used to calculate reflection and visibility for the four Groups are described bellow:

Case 1 and 2 for G1:

$Reflection[j]=R*G_{1}[j].Points[j]$

$Visibility[j]=V*(Best.Points[j]-G_{1}[i].Points[j])$

Case 3 and 4 for G2:

$Reflection[j]=R*Best.Points[j]$

$Visibility[j]=V*(Best.Points[j]-G_{2}[i].Points[j])$

Case 5 for G3:

$Reflection[j]=R*Best.Points[j]$

$Visibility[j]=V*(Best.Points[j]-AV_{Best}$

Case 6 for G4:

$P[i].Points[j]=random*(upperLimit-lowerLinit)+LowerLimit,i=1,2,...,N;j=1,2,...,d$

Where $G_{1}$, $G_{2}$ are Group1 and Group2, i presents the $i^{th}$ element in G, j is the $j^{th}$ point of $i^{th}$ element in group G, Best is the best solution and $AV_{Best}$ presents the average value of the Best points. While R and V are two random numbers produced around zero such as between (-1, 1), R represents the degree of reflection, V represents the visibility degree of the final view of the pattern, upperLimit and lowerLimit are the upper limit and the lower limit of the problem domain.

Applications

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The Cuttlefish Optimization Algorithm has found many applications in literature, such as:

Feature Selection

Travelling salesman problem

Image processing (Komal Sinsinwar, 2016)

Control

See also

• Mathematical optimization
• Metaheuristic
• Evolutionary computation
• Swarm intelligence
• Particle swarm optimization
• Ant colony optimization algorithms
• Artificial bee colony algorithm
• Intelligent Water Drops

External links

• The Cuttlefish Optimization Algorithm website