Polynomial mutation is widely used in evolutionary optimization algorithms as a variation operator. In previous work on the use of evolutionary algorithms for solving multi-objective problems, two versions of polynomial mutations were introduced. The first is non-highly disruptive that is not prone to local optima and the second is highly disruptive polynomial mutation. This paper looks at the two variants and proposes a dynamic version of polynomial mutation. The experimental results show that the proposed adaptive algorithm is doing well for three evolutionary multiobjective algorithms on well known multiobjective optimization problems in terms of convergence speed, generational distance and hypervolume performance metrics.
|Number of pages||11|
|Journal||International Journal on Artificial Intelligence Tools|
|Publication status||Published - 1 Feb 2011|
- evolutionary algorithms
- Multi-objective optimization
ASJC Scopus subject areas
- Artificial Intelligence