Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results

TL;DR

This paper introduces nine benchmark problems for multi-task single-objective optimization, providing a comprehensive evaluation framework to advance research in evolutionary multitasking algorithms.

Contribution

It proposes a set of nine diverse test problems for MTSOO, facilitating standardized benchmarking and progress in evolutionary multitasking research.

Findings

Nine benchmark problems introduced for MTSOO evaluation

Relationship variability between tasks in test problems

Expected to stimulate progress in MTSOO algorithm development

Abstract

In this report, we suggest nine test problems for multi-task single-objective optimization (MTSOO), each of which consists of two single-objective 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 MFO algorithms. It is expected that the proposed test problems will germinate progress the field of the MTSOO research.