Elite-guided multi-objective cuckoo search algorithm based on crossover operation and information enhancement

Yang, Xunian; Hao, Xiaochen; Yang, Tianqi; Li, Yonghang; Zhang, Yuxuan; Wang, Junwei

doi:10.1007/s00500-022-07605-8

Cited by 7 publications

(3 citation statements)

References 47 publications

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“…Formula ( 14) describes that crayfish can directly ingest food when the size of the food is appropriate, and there is a certain blindness in this process of crayfish. Therefore, this paper introduces the elite factor [28] to guide the position update, as shown in Equation (19). With its replacement Formula ( 14), the algorithm can be guided to converge faster and improve the optimization accuracy to a certain extent.…”

Section: Elite Steering Factormentioning

confidence: 99%

Implementation of an Enhanced Crayfish Optimization Algorithm

Zhang,

Liu,

2024

Biomimetics

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This paper presents an enhanced crayfish optimization algorithm (ECOA). The ECOA includes four improvement strategies. Firstly, the Halton sequence was used to improve the population initialization of the crayfish optimization algorithm. Furthermore, the quasi opposition-based learning strategy is introduced to generate the opposite solution of the population, increasing the algorithm’s searching ability. Thirdly, the elite factor guides the predation stage to avoid blindness in this stage. Finally, the fish aggregation device effect is introduced to increase the ability of the algorithm to jump out of the local optimal. This paper performed tests on the widely used IEEE CEC2019 test function set to verify the validity of the proposed ECOA method. The experimental results show that the proposed ECOA has a faster convergence speed, greater performance stability, and a stronger ability to jump out of local optimal compared with other popular algorithms. Finally, the ECOA was applied to two real-world engineering optimization problems, verifying its ability to solve practical optimization problems and its superiority compared to other algorithms.

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Section: Elite Steering Factormentioning

confidence: 99%

Implementation of an Enhanced Crayfish Optimization Algorithm

Zhang,

Liu,

2024

Biomimetics

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“…(2) The TLBO is the global optimization algorithm, which searches for the global optimal solution in the entire solution space, but it has tendency to search towards the origin; When solving optimization problems far from the origin, this algorithm may lose its advantages [9][10]. Therefore, it is considered to combine the TLBO with Levy flight.…”

Section: Optimize Classic Tlbomentioning

confidence: 99%

Production Scheduling Optimization of An Aviation Bearing Manufacturing Enterprise Based on Teaching-Learning-based Optimization

Liang

2023

AJST

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This article is based on an aviation bearing manufacturing and wind power bearing manufacturing enterprise production scheduling for its key process optimization, in Qingdao, Shandong. The production scheduling is aimed at the shortest delivery time, and the production scheduling assessment indicator is the on-time delivery rate of orders. This article, based on the production process and production mode characteristics of the enterprise, improves the convergence ability of the Teaching-learning-based optimization (TLBO) in the early stage and the detail search ability in the later stage by optimizing the static teaching factor function and algorithm retrieval method of the classic algorithm. By combining Levy flight to further enhance the algorithm's global search ability and reduce the probability of falling into local optima. The theoretical reliability of the algorithm was verified by comparing the calculation result of the MK calculation example with other algorithms. The reliability of the algorithm in actual production was verified through the long-term order execution data and order delivery rate of the enterprise.

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“…As a result, solving the three-dimensional path planning problem for UAVs has become a classic NP-hard problem, leading to a focus on swarm intelligence optimization algorithms as a primary research approach to address these intricate issues [5]. VOLUME 11, 2023 Metaheuristic (MH) algorithms, inspired by natural biological behaviors, harness patterns found in nature to iteratively refine solutions, aiming for efficient outcomes within constrained timeframes [6] [7]. Their simplicity, versatility, and ease of use have made them applicable across a variety of domains, including but not limited to image segmentation [8], path planning [9] [10], agricultural monitoring [11], forest fire detection [12], rescue operations [13], rotor system [14], and cubic transmission [15].…”

mentioning

confidence: 99%

Improved Dung Beetle Optimizer Algorithm With Multi-Strategy for Global Optimization and UAV 3D Path Planning

Lyu,

Jiang,

Yang

2024

IEEE Access

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In high-dimensional scenarios, path planning is a challenging and computationally complex optimization task that requires finding optimal paths within complex domains. Metaheuristic (MH) algorithms offer a practical approach to addressing this issue. The Dung Beetle Optimizer (DBO), categorized as a MH algorithm, takes inspiration from the biological behaviors exhibited by dung beetles. However, DBO exhibits limitations, including inadequate global search capabilities and a tendency to converge on local optima. To address these challenges, this paper proposes a multi-strategy Improved Dung Beetle Optimization algorithm (IDBO) for UAV 3D path planning. Initially, cubic chaos mapping is applied for population initialization, enhancing diversity. Subsequently, a novel global exploration strategy replaces the DBO's original rolling phase, improving information exchange and minimizing parameter dependence. Third, an adaptive t-distribution is introduced to adjust dung beetle positions, balancing exploration and exploitation. Finally, an enhanced population update strategy is proposed, utilizing varied behavioral logic at different algorithm stages to improve solution quality and search efficiency. Additionally, performance comparisons with six advanced algorithms on the CEC2017 test suite, and the validation of IDBO's effectiveness via the Wilcoxon rank-sum and Friedman mean rank test. Meanwhile, in UAV 3D path planning experiment, IDBO achieves the best cost index, which is 1.34% higher than the best cost of original DBO, and is also significantly better than the most advanced algorithms such as WOA, GSA, HHO, COA, and the standard deviation is reduced by 99.93% compared with DBO algorithm, which proves the effectiveness and robustness of IDBO in UAV 3D path planning.

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Elite-guided multi-objective cuckoo search algorithm based on crossover operation and information enhancement

Cited by 7 publications

References 47 publications

Implementation of an Enhanced Crayfish Optimization Algorithm

Implementation of an Enhanced Crayfish Optimization Algorithm

Production Scheduling Optimization of An Aviation Bearing Manufacturing Enterprise Based on Teaching-Learning-based Optimization

Improved Dung Beetle Optimizer Algorithm With Multi-Strategy for Global Optimization and UAV 3D Path Planning

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