Research on Dual-Ant Colony Cooperative Algorithm with Dynamic Reference Points for Multi-Objective Optimization

DOI

10.2991/978-94-6239-758-3_19How to use a DOI?

Keywords

Multi-objective optimization; Dual-ant colony cooperation; NSGA-III; Dynamic reference point; Benchmark functions

Abstract

Multi-objective optimization problems are prevalent in engineering management, where decision-makers must balance multiple conflicting objectives. Traditional evolutionary algorithms such as NSGA-III employ fixed reference points and treat all decision variables uniformly, making it difficult to adapt to complex scenarios with coupled variables. This paper proposes a dual-ant colony cooperative optimization algorithm with dynamic reference points (DACO-NSGAIII). A dual-ant colony cooperative mechanism is constructed to achieve joint optimization among variable groups, and a dynamic reference point adaptation strategy is introduced to enhance the algorithm’s responsiveness to the optimization process. The dual-ant colony system decomposes decision variables into primary and coupled variable groups: Colony A optimizes the primary variables, Colony B optimizes the coupled variables, and global coordination is achieved through a pheromone sharing mechanism. Dynamic reference points are adaptively adjusted based on the evolutionary stage and population distribution characteristics. The proposed algorithm is validated using the ZDT and DTLZ benchmark functions. Experimental results demonstrate that, compared with standard NSGA-III and single-ant colony algorithms, the proposed method achieves significant improvements in both convergence and diversity, confirming the effectiveness of the hierarchical architecture in decoupling complex decisions and coordinating multiple objectives.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Hao Wu
PY  - 2026
DA  - 2026/09/08
TI  - Research on Dual-Ant Colony Cooperative Algorithm with Dynamic Reference Points for Multi-Objective Optimization
BT  - Proceedings of the 2026 7th International Conference on Management Science and Engineering Management (ICMSEM 2026)
PB  - Atlantis Press
SP  - 186
EP  - 194
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-758-3_19
DO  - 10.2991/978-94-6239-758-3_19
ID  - Wu2026
ER  -