Neural Net-Enhanced Competitive Swarm Optimizer for Large-Scale Multiobjective Optimization

Li, Lingjie; Li, Yongfeng; Lin, Qiuzhen; Liu, Songbai; Zhou, Junwei; Ming, Zhong; Coello, Carlos A. Coello

doi:10.1109/tcyb.2023.3287596

Cited by 12 publications

(2 citation statements)

References 52 publications

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“…However, traditional evolutionary algorithms face large-scale optimization problems with rapidly decreasing efficiency due to dimensionality catastrophe. Inspired by [43,44], two different evolutionary strategies are employed in LDOMO to explore different solution spaces (X p and X e ), i.e., the CSO-based evolutionary strategy and the MLP-based evolutionary strategy. Specifically, the former strategy makes the poor solutions in X p quickly approach the elite solutions in X e as a way to accelerate population convergence.…”

Section: Evolution Phasementioning

confidence: 99%

Joint UAV Deployment and Task Offloading in Large-Scale UAV-Assisted MEC: A Multiobjective Evolutionary Algorithm

Qiu,

Li,

Xiao

et al. 2024

Mathematics

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With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms.

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Section: Evolution Phasementioning

confidence: 99%

Joint UAV Deployment and Task Offloading in Large-Scale UAV-Assisted MEC: A Multiobjective Evolutionary Algorithm

Qiu,

Li,

Xiao

et al. 2024

Mathematics

Self Cite

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“…Recent MOEAs address LSMOPs directly through innovative search strategies that enhance traditional MOEA operators, including crossover and mutation [35]. These approaches focus on generating superior offspring without complex manipulation of the vast decision space [36][37][38]. Typical strategies include new crossover operators and innovative probabilistic models.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

Chen,

Zhang,

et al. 2024

CMES

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The large-scale multi-objective optimization algorithm (LSMOA), based on the grouping of decision variables, is an advanced method for handling high-dimensional decision variables. However, in practical problems, the interaction among decision variables is intricate, leading to large group sizes and suboptimal optimization effects; hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables (MOEAWOD) is proposed in this paper. Initially, the decision variables are perturbed and categorized into convergence and diversity variables; subsequently, the convergence variables are subdivided into groups based on the interactions among different decision variables. If the size of a group surpasses the set threshold, that group undergoes a process of weighting and overlapping grouping. Specifically, the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables. The decision variable with the highest interaction in the group is identified and disregarded, and the remaining variables are then reclassified into subgroups. Finally, the decision variable with the strongest interaction is added to each subgroup. MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups, which contributed to the optimized direction of convergence and diversity exploration with different groups. MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems, and the experimental results demonstrate the effectiveness of our methods. Compared with the other algorithms, our method is still at an advantage.

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Gene-targeting multiplayer battle game optimizer for large-scale global optimization via cooperative coevolution

Zhong,

2024

Cluster Comput

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Neural Net-Enhanced Competitive Swarm Optimizer for Large-Scale Multiobjective Optimization

Cited by 12 publications

References 52 publications

Joint UAV Deployment and Task Offloading in Large-Scale UAV-Assisted MEC: A Multiobjective Evolutionary Algorithm

Joint UAV Deployment and Task Offloading in Large-Scale UAV-Assisted MEC: A Multiobjective Evolutionary Algorithm

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

Gene-targeting multiplayer battle game optimizer for large-scale global optimization via cooperative coevolution

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