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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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
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Abstract	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.
AbstractList	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.
Author	Liu, Peng Yue, Weichao Wang, Qian Hou, Wenjie Li, Sanyi
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SubjectTerms	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
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Title	A Special Points and Neural Network-Based Dynamic Multi-Objective Optimization Algorithm
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