Are All the Subproblems Equally Important? Resource Allocation in Decomposition Based Multiobjective Evolutionary Algorithms

Decomposition based multiobjective evolutionary algorithms decompose a multiobjective optimization problem into a set of scalar objective subproblems and solve them in a collaborative way. A naive way for distributing computational effort is to treat all the subproblems equally and assign the same computational resource to each subproblem. This paper proposes a generalized resource allocation strategy for decomposition based multiobjective evolutionary algorithms by using a probability of improvement vector. Each subproblem is chosen to invest according to this vector. An offline measurement and an online measurement of the subproblem hardness are used to maintain and update this vector. Utility functions are proposed and studied for implementing a reasonable and stable online resource allocation strategy. Extensive experimental studies on the proposed generalized resource allocation strategy have been conducted.