Opposition-Based Animal Migration Optimization

Algorithm 2

Opposition-based AMO algorithm.

(1)  Begin

(2)  Set the generation counter 𝐺=0, and randomly initialize with a population of NP animal 𝑋_𝑖.

(3)  Evaluate the fitness for each individual 𝑋_𝑖.

(4)  For 𝑖=1 to NP do

(5)   For 𝑗=1 to D do

      𝑂𝑃𝑋_𝑖,𝑗=𝑘𝑎_𝑗+𝑏_𝑗−𝑋_𝑖,𝑗

(6)   End for

(7)   Calculate the fitness value of 𝑂𝑃𝑋_𝑖

(8)  End for

(9)  Select NP fittest individual from {𝑋_𝑖}∪{𝑂𝑃𝑋_𝑖} as an initial population;

(10) While stopping criteria is not satisfied do

(11)  For 𝑖=1 to NP do

(12)     For 𝑗=1 to D do

(13)        𝑋_𝑖,𝐺+1=𝑋_𝑖,𝐺+𝛿⋅(𝑋_{neighborhood,𝐺}−𝑋_𝑖,𝐺)

(14)   End for

(15)   If rand < Pa then

(16)    For 𝑗=1 to D do

(17)     𝑂𝑃𝑋_𝑖,𝐺+1=𝑘𝑎_𝑗+𝑏_𝑗−𝑋_𝑖,𝐺+1

(18)    End for

(19)  End for

(20) Select NP fittest particles from {𝑋_𝐺}∪𝑋_𝐺+1∪{𝑂𝑃𝑋_𝐺+1} as current population;

(21) For 𝑖=1 to NP

(22)  For 𝑗=1 to D

(23)       Select randomly 𝑟1≠𝑟2≠𝑖

(24)   If rand > Pa then

(25)     𝑋_𝑖,𝐺+1=𝑋_𝑟1,𝐺+rand⋅(𝑋_best,𝐺−𝑋_𝑖,𝐺)+rand⋅(𝑋_𝑟2,𝐺−𝑋_𝑖,𝐺)

(26)   End if

(27)  End for

(28) For 𝑖=1 to NP do

(29)  Evaluate the offspring 𝑥_𝑖,𝐺+1

(30)   If 𝑥_𝑖,𝐺+1 is better than 𝑥_𝑖 then

(31)       𝑥_𝑖=𝑥_𝑖,𝐺+1

(32)   End If

(33) End for

(34) Memorize the best solution achieved so far

(35) End while

(36) End