A DYNAMIC POLYNOMIAL MUTATION FOR EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION ALGORITHMS

摘要

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 multi-objective algorithms on well-known multi-objective optimization problems in terms of convergence speed, generational distance and hyper-volume performance metrics.