A random benchmark suite and a new reaction strategy in dynamic multiobjective optimization

Ruan, Gan; Zheng, Jinhua; Zou, Juan; Ma, Zhongwei; Yang, Shengxiang

doi:10.1016/j.swevo.2021.100867

Cited by 11 publications

(1 citation statement)

References 56 publications

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“…• How to make real-time decisions in each short time slot? Remark 2: Although the studied optimization problem in this paper and the most common dynamic optimization problems [29]- [33] have the time-slotted structure, the latter focus on the short-term performance in each time slot. Queue length:…”

Section: B Problem Formulationmentioning

confidence: 99%

Combining Lyapunov Optimization With Evolutionary Transfer Optimization for Long-Term Energy Minimization in IRS-Aided Communications

Huang

Wang

et al. 2023

IEEE Trans. Cybern.

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Section: B Problem Formulationmentioning

confidence: 99%

Combining Lyapunov Optimization With Evolutionary Transfer Optimization for Long-Term Energy Minimization in IRS-Aided Communications

Huang

Wang

et al. 2023

IEEE Trans. Cybern.

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An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems

Chen

Guo

Jin

et al. 2022

Complex Intell. Syst.

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In dynamic multi-objective optimization problems, the environmental parameters may change over time, which makes the Pareto fronts shifting. To address the issue, a common idea is to track the moving Pareto front once an environmental change occurs. However, it might be hard to obtain the Pareto optimal solutions if the environment changes rapidly. Moreover, it may be costly to implement a new solution. By contrast, robust Pareto optimization over time provides a novel framework to find the robust solutions whose performance is acceptable for more than one environment, which not only saves the computational costs for tracking solutions, but also minimizes the cost for switching solutions. However, neither of the above two approaches can balance between the quality of the obtained non-dominated solutions and the computation cost. To address this issue, environment-driven hybrid dynamic multi-objective evolutionary optimization method is proposed, aiming to fully use strengths of TMO and RPOOT under various characteristics of environmental changes. Two indexes, i.e., the frequency and intensity of environmental changes, are first defined. Then, a criterion is presented based on the characteristics of dynamic environments and the switching cost of solutions, to select an appropriate optimization method in a given environment. The experimental results on a set of dynamic benchmark functions indicate that the proposed hybrid dynamic multi-objective evolutionary optimization method can choose the most rational method that meets the requirements of decision makers, and balance the convergence and robustness of the obtained non-dominated solutions.

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A decision variable classification strategy based on the degree of environmental change for dynamic multiobjective optimization

Sun

Wang

et al. 2024

European Journal of Operational Research

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A random benchmark suite and a new reaction strategy in dynamic multiobjective optimization

Cited by 11 publications

References 56 publications

Combining Lyapunov Optimization With Evolutionary Transfer Optimization for Long-Term Energy Minimization in IRS-Aided Communications

Combining Lyapunov Optimization With Evolutionary Transfer Optimization for Long-Term Energy Minimization in IRS-Aided Communications

An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems

A decision variable classification strategy based on the degree of environmental change for dynamic multiobjective optimization

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