Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results

TL;DR

This paper introduces nine benchmark problems for multi-task multi-objective optimization, providing a comprehensive evaluation framework to advance research in this emerging field.

Contribution

It proposes new benchmark problems with varied task relationships for evaluating multi-task multi-objective optimization algorithms.

Findings

Nine test problems with diverse task relationships

Baseline results for these benchmark problems

Facilitates systematic evaluation of MO-MFO algorithms

Abstract

In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously. The relationship between tasks varies between different test problems, which would be helpful to have a comprehensive evaluation of the MO-MFO algorithms. It is expected that the proposed test problems will germinate progress the field of the MTMOO research.