Near Feasibility Driven Adaptive Penalty Functions Embedded MOEA/D-Reference-Cited by-同舟云学术

Near Feasibility Driven Adaptive Penalty Functions Embedded MOEA/D

Published:2023 Issue: Volume:11 Page:105182-105213
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Khan Akhtar Munir¹^ORCID,Jan Muhammad Asif¹^ORCID,Sagheer Muhammad¹^ORCID,Khanum Rashida Adeeb²^ORCID,Uddin Muhammad Irfan³^ORCID,Ahmad Shafiq⁴^ORCID,Huda Shamsul⁵^ORCID

Affiliation:

1. Institute of Numerical Sciences, Kohat University of Science and Technology, Kohat, Pakistan

2. Jinnah College for Women, University of Peshawar, Peshawar, Pakistan

3. Institute of Computing, Kohat University of Science and Technology, Kohat, Pakistan

4. Industrial Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia

5. School of Information Technology, Deakin University, Burwood, VIC, Australia

Funder

King Saud University, Riyadh, Saudi Arabia, through the King Saud University Researchers Supporting Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10256221.pdf?arnumber=10256221

Reference43 articles.

1. SPEA2: Improving the strength Pareto evolutionary algorithm;zitzler,2001

2. An efficient constraint handling method for genetic algorithms

3. An evolutionary algorithm for multiobjective optimization: The strength Pareto approach;zitzler,1998

4. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach

5. HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization