Solving interval many-objective optimization problems by combination of NSGA-III and a local fruit fly optimization algorithm

Ge, Fawei; Li, Kun; Han, Ying

doi:10.1016/j.asoc.2021.108096

Cited by 12 publications

(2 citation statements)

References 41 publications

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“…In order to overcome these shortcomings of traditional PSO, researchers put forward many improved and variant PSO algorithms, such as adaptive PSO, multi-strategy PSO and hybrid PSO, to adapt to different types of problems and needs. These improved versions of PSO usually improve the performance and robustness of the algorithm by introducing new strategies, parameter control methods and heuristic techniques [9][10].…”

Section: Overview Of Pso Algorithm Principlementioning

confidence: 99%

Simulation of intelligent allocation model of local cultural and educational resources based on improved particle swarm optimization algorithm

Zhang

2024

Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024)

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With the continuous development of society, the distribution of cultural and educational resources has become a concern, which is related to the quality of local cultural heritage and education popularization. Traditional resource allocation methods usually have some problems, such as low efficiency, obvious inequality and difficulty in dealing with uncertainty. In order to meet these challenges, this study introduces an improved Particle Swarm Optimization(PSO) algorithm, and constructs an intelligent allocation model of local cultural and educational resources based on adaptive PSO to optimize the allocation of cultural and educational resources. Through the simulation experiment based on real data, this study verifies the effectiveness of the improved PSO algorithm in the allocation of local cultural and educational resources. The results show that the improved PSO algorithm not only improves the efficiency of resource utilization, but also optimizes the resource allocation strategy and reduces the occurrence of inequality. The algorithm also responds to the uncertainty well, which is expected to provide strong support for the intelligent allocation of local cultural and educational resources.

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Section: Overview Of Pso Algorithm Principlementioning

confidence: 99%

Simulation of intelligent allocation model of local cultural and educational resources based on improved particle swarm optimization algorithm

Zhang

2024

Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024)

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“…Lastly, ref. [30] presents an enhanced version of the NSGA-III algorithm. This algorithm combines the genetic algorithm, the local fruit fly optimization algorithm, and measures of hyper-volume and imprecision.…”

Section: Introductionmentioning

confidence: 99%

Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow

Zheng,

Tao,

et al. 2023

Applied Sciences

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The conventional optimal power flow (OPF) is confronted with challenges in tackling more than three objectives and the stochastic characteristics due to the uncertainty and intermittence of the RESs. However, there are few methods available that simultaneously address high-dimensional objective optimization and uncertainty handling. This paper proposes a set-based group search optimizer (SetGSO) to tackle the stochastic many-objective optimal power flow (MaOPF) of power systems penetrated with renewable energy sources. The proposed SetGSO depicts the original stochastic variables by set-based individuals under the evolutionary strategy of the basic GSO, without using repeated sampling or probabilistic information. Consequently, two metrics, hyper-volume and average imprecision, are introduced to transform the stochastic MaOPF into a deterministic bi-objective OPF, guaranteeing a much superior Pareto-optimal front. Finally, our method was evaluated on three modified bus systems containing renewable energy sources, and compared with the basic GSO using Monte Carlo sampling (GSO-MC) and a set-based genetic algorithm (SetGA) in solving the stochastic MaOPF. The numerical results demonstrate a saving of 90% of the computation time in the proposed SetGSO method compared to sampling-based approaches and it achieves improvements in both the hyper-volume and average imprecision indicators, with a maximum enhancement of approximately 30% and 7% compared to SetGA.

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Multi-mechanism swarm optimization for multi-UAV task assignment and path planning in transmission line inspection under multi-wind field

Li,

Yan,

Han

2024

Applied Soft Computing

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Solving interval many-objective optimization problems by combination of NSGA-III and a local fruit fly optimization algorithm

Cited by 12 publications

References 41 publications

Simulation of intelligent allocation model of local cultural and educational resources based on improved particle swarm optimization algorithm

Simulation of intelligent allocation model of local cultural and educational resources based on improved particle swarm optimization algorithm

Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow

Multi-mechanism swarm optimization for multi-UAV task assignment and path planning in transmission line inspection under multi-wind field

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