Evolutionary Multitasking Across Multi and Single-Objective Formulations for Improved Problem Solving

Traditionally, single-objective and multi-objective optimization have only considered a single problem in one run. However, the notion of evolutionary multitasking, which aims at solving multiple optimization problems simultaneously, has recently emerged in Evolutionary Computation (EC). It is inspired by the implicit parallelism of population-based search, which attempts to take advantage of implicit genetic transfer in a multitasking environment. According to optimization literature, transforming a single-objective optimization (SOO) problem into a multi-objective optimization (MOO) problem has often been found to remove local optima. Motivated by the aforementioned idea and the concept of multitasking, in this paper, we introduce a new strategy for tackling complex multi-modal problems. In particular, we solve the original (or target) SOO task together with an artificially formulated MOO task in a multitask setting. Therein, the MOO task is expected to provide a useful inductive bias to the search progress of the target SOO task by leveraging on the transferable knowledge shared between them, thereby helping overcome local optima and effectively guiding the population towards more promising regions of the search space.