Work

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

Year: 2017

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1706.03470

Authors: Da, Bingshui, Ong, Yew-Soon, Feng, Liang, Qin, A. K., Gupta, Abhishek, Zhu, Zexuan, Ting, Chuan-Kang, Tang, Ke, Yao, Xin

Institutions: Nanyang Technological University, Chongqing University, RMIT University, Shenzhen University, National Chung Cheng University, University of Science and Technology of China, University of Birmingham

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4293862731

Cites:

Cited by: 137

Related to: 10