A Dynamic Multi-Objective Evolutionary Algorithm

Citation Author(s):
Yilin
Fang
School of Information Engineering, Wuhan University of Technology
Submitted by:
Yilin Fang
Last updated:
Tue, 01/03/2023 - 23:42
DOI:
10.21227/ssjk-pj85
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Abstract 

A Dynamic Multi-Objective Evolutionary Algorithm

Instructions: 

Explanation for data folder: 

1. The D-TAOG folder contains the data for the product disassembly model (D-TAOG) for each test instance. We constructed 12 test instances of different scales, including 6 small-scale instances and 6 large-scale instances.

2. The environment folder contains the data for environment similarity settings for each test instance. We stipulate that each test instance contains 10 time-varying disassembly environments. Each test instance is further expanded into high-, medium-, and low-similarity test instances by randomly adjusting the state of normal nodes in the product disassembly model for each environment.

3. The obj folder contains performance data for each algorithm. There are two subfolders under the folder for each competitor in the obj folder, namely the IGD&HV folder and the time folder. The IGD&HV folder contains MIGD results and MHV results for each compared algorithm on each test instance. The running time is set as the termination condition for these compared algorithms. The time folder contains the runtime required for different compared algorithms to achieve similar MIGD values for all test instances. Note that the runtime of an algorithm for one test instance is defined here as the sum of its running times in all environments of the test instance.

Funding Agency: 
National Natural Science Foundation of China
Grant Number: 
52075402

Comments

About the experimental data of a dynamic multi-objective evolutionary algorithm

Submitted by Yilin Fang on Sun, 02/20/2022 - 02:10