In this article we propose a simple but very efficient hybrid evolutionary algorithm that embeds the difference vector based mutation scheme of Differential Evolution (DE) into another recently developed global optimization algorithm known as Invasive Weed Optimization (IWO).
The main aspects of modifications include the twin analogy and a number of mutation schemes.
Alternatively, to achieve acceptable results even for the same problem, different parameter settings along with different mutation schemes at different stages of evolution are needed.
a) Adaptive selection learning strategy in mutation operator In our study of ISADE, we randomly chose three mutation schemes: DE/best/1/bin, DE/best/2/bin, and DE/rand to best/1/bin.
Namely, we extended the ES algorithm by including variable size mutations, random mutation schemes, and a heuristic algorithm that defines the "reasonable" places of mutation (for large-scale problems) (Mester et al. 2010).
The mutation schemes used are swap node and swap sequence.
We simulated the SCN model with different mutation schemes.
The values of the parameters of the evolutionary algorithm (population size, crossover rate, mutation scheme, and number of generations) have been found to obtain a quite good tradeoff between design time and performance for the experiments carried out in this paper.
To perform comprehensive evaluation and to assess the effectiveness of the proposed self-adaptive crossover rate scheme and new mutation scheme, another version of EADE, named EADE*, has been tested and compared against EADE and other DE-based algorithms.
In this paper, a kind of discrete artificial bee colony with composite mutation strategies is presented to compensate the defects of the single mutation scheme that is easy to get into the local best for PFSSP, named CDABC.
