Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results
This work provides foundational test problems for researchers in evolutionary computation, but it is incremental as it builds on existing multi-task optimization concepts.
The authors proposed nine benchmark problems for multi-task single-objective optimization, each with two tasks having varying relationships, to enable comprehensive evaluation of algorithms in this field.
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.