Therein, IDE employs several mutation strategies and the binomial crossover of differential evolution (DE) to generate the offspring population.
We here elaborate the binomial crossover.
In the binomial crossover, the target vector is mixed with the mutated vector, using the following scheme, to yield the trial vector (u_i^G ).
The binomial crossover operator inherits the j-th variable of mutant vector v i to its corresponding element in the trial vector u i if it meets the condition.
The two triangles (varvec{u}_{i}^{1}) and (varvec{u}_{i}^{2}) represent the two possible locations for the trial vector after performing binomial crossover operation.
Each individual in the parent population is employed to generate three different offspring with three different mutant strategies and binomial crossover, and then the better individuals retaining into the next generation are chosen from the new offspring and parent population by ATM strategy.
To further enhance the search performance of ARA, self-organizing map (SOM) and binomial crossover operator (BCO) are integrated into ARA.
where r 1,r 2,r 3,r 4, and r 5 are randomly selected integers in the range [1,N P]. c) Crossover operation After mutation process, DE performs a binomial crossover operator on (X_{i}^{G}) and (V_{i}^{G}) to generate a trial vector (U_{i}^{G} = left (U_{i,1}^{G},ldots, U_{i,D}^{G}right)) for each individual population i as shown in Eq. 10.
Binomial crossover and exponential crossover are two commonly used crossover operators in current popular DE.
Commonly used operators are uniform crossover, binomial crossover, exponential crossover, arithmetic crossover.
