Generalized Multi-tasking for Evolutionary Optimization of Expensive Problems

Conventional evolutionary algorithms are not well suited for solving expensive optimization problems due to the fact that they often require a large number of fitness evaluations to obtain acceptable solutions. To alleviate the difficulty, this paper presents a multi-tasking evolutionary optimization framework for solving computationally expensive problems. In the framework, knowledge is transferred from a number of computationally cheap optimization problems to help the solution of the expensive problem on the basis of the recently proposed multifactorial evolutionary algorithm, leading to a faster convergence of the expensive problem. However, existing multifactorial evolutionary algorithms do not work well in solving multi-tasking problems whose optimums do not lie in the same location or when the dimensions of the decision space are not the same. To address the above issues, the existing multifactorial evolutionary algorithm is generalized by proposing two strategies, one for decision variable translation and the other for decision variable shuffling, to facilitate knowledge transfer between optimization problems having different locations of the optimums and different numbers of decision variables. To assess the effectiveness of the generalized multifactorial evolutionary algorithm, empirical studies have been conducted on eight multi-tasking instances and eight test problems for expensive optimization. The experimental results demonstrate that the proposed generalized multifactorial evolutionary algorithm works more efficiently for multi-tasking optimization and successfully accelerates the convergence of expensive optimization problems compared to single-task optimization