A Special Points and Neural Network-Based Dynamic Multi-Objective Optimization Algorithm

This paper introduces a special points and neural network- based dynamic multi-objective optimization algorithm (SPNN-DMOA) for solving dynamic multi-objective optimization problems (DMOPs) with an irregularly changing pareto set. In the stage of population initialization, the algorithm employs a fe...

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Bibliographic Details
Published in:IEEE access Vol. 13; pp. 24765 - 24792
Main Authors: Li, Sanyi, Hou, Wenjie, Liu, Peng, Yue, Weichao, Wang, Qian
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Classification algorithms
Convergence
decision variable classification
Diversity reception
Dynamic multi-objective optimization
Evolutionary algorithms
Heuristic algorithms
irregular environment
Machine learning algorithms
Multiple objective analysis
neural network
Neural networks
Optical fibers
Optimization
Optimization algorithms
Pareto optimization
prediction
Prediction algorithms
Random variables
special point
Vectors
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:This paper introduces a special points and neural network- based dynamic multi-objective optimization algorithm (SPNN-DMOA) for solving dynamic multi-objective optimization problems (DMOPs) with an irregularly changing pareto set. In the stage of population initialization, the algorithm employs a feedforward neural network (FNN) along with special points to generate an initial population. The FNN is trained with historical special points (knee point, boundary point, center point), and the current special points are generated by the FNN when an environmental change is detected. Then the decision variables are classified into convergence variables and diversity variables. The convergence variables of special points are locally searched to form a new population and the best individuals of this population are selected. Finally, a portion of the initial population is generated by conducting a local search on the diversity variables of best individuals, while the remaining portion is produced using random strategies. SPNN-DMOA only needs to maintain non-dominated solutions in proximity to special points, which reduces the computational complexity in the dynamic evolution process. The proposed algorithm has been compared with other state-of-the-art algorithms on a series of benchmark problems, demonstrating its superior performance in optimizing DMOPs.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3538537