Work

Dynamic Multiobjective Optimization Algorithm Based on Average Distance Linear Prediction Model

Year: 2014

Type: article

Abstract: Many real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. Optimization in a changing environment is a challenging task, especially when multiple objectives are required to be optimized simultaneously. Nowadays the common way to solve dynamic multiobjective optimization problems (DMOPs) is to utilize history information to guide future search, but there is no common successful method to solve different DMOPs. In this paper, we define a kind of dynamic multiobjectives problem with translational Paretooptimal set (DMOP-TPS) and propose a new prediction model named ADLM for solving DMOP-TPS. We have tested and compared the proposed prediction model (ADLM) with three traditional prediction models on several classic DMOP-TPS test problems. The simulation results show that our proposed prediction model outperforms other prediction models for DMOP-TPS.

Source: The Scientific World JOURNAL

DOI: 10.1155/2014/389742

Authors: Zhiyong Li, Hengyong Chen, Zhaoxin Xie, Chao Chen, Ahmed Sallam

Institutions: Hunan University, Zhejiang University, Suez Canal University

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $860

Funder: National Natural Science Foundation of China