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Memory gradient method for multiobjective optimization
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DOI：10.1016/j.amc.2022.127791.png)
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摘要
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In this paper, we propose a new descent method, called multiobjective memory gradi-ent method, for finding Pareto critical points of a multiobjective optimization problem. The main thought in this method is to select a combination of the current descent direc-tion and past multi-step iterative information as a new search direction and to obtain a stepsize by two types of strategies. It is proved that the developed direction with suit-able parameters always satisfies the sufficient descent condition at each iteration. Based on mild assumptions, we obtain the global convergence and the rates of convergence for our method. Computational experiments are given to demonstrate the effectiveness of the proposed method. (c) 2022 Elsevier Inc. All rights reserved.
Keyword:
Multiobjective optimization
Memory gradient method
Descent direction
Pareto critical
Convergence analysis
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3.4
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2.3W
被引数:
3.3W
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