Discrete artificial bee colony algorithm with fixed neighborhood search for traveling salesman problem

Li, Xing; Zhang, Shaoping; Shao, Peng

doi:10.1016/j.engappai.2023.107816

Cited by 9 publications

(2 citation statements)

References 26 publications

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“…Otherwise, crayfish will perform foraging behavior. Crayfish show strong foraging behavior between 20 • C and 30 • C. The optimal feeding temperature is 25 • C. Therefore, COA defines the temperature range as 20 • C to 35 • C. The mathematical model of crayfish feeding is shown in Equation (3).…”

Section: Initialization Phasementioning

confidence: 99%

“…These algorithms have proven their reliability and effectiveness in discovering optimal solutions to complex real-world problems. In addition to classical numerical optimization, metaheuristic algorithms have demonstrated their efficacy in a wide range of optimization tasks, including, but not limited to, feature selection [ 1 , 2 ], the traveling salesman problem [ 3 , 4 ], image segmentation problems [ 5 , 6 , 7 ], wireless sensor coverage problems [ 8 , 9 ], parameter estimation for solar photovoltaic models [ 10 , 11 ], and path planning [ 12 , 13 ]. In the past decades, new metaheuristic algorithms have been continuously proposed that are based on evolutionary theory, physical laws, biological population behavior, and human behavior.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems

Wang,

Zhang,

Zou

2024

Biomimetics

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The crayfish optimization algorithm (COA), proposed in 2023, is a metaheuristic optimization algorithm that is based on crayfish’s summer escape behavior, competitive behavior, and foraging behavior. COA has a good optimization performance, but it still suffers from the problems of slow convergence speed and sensitivity to the local optimum. To solve these problems, an improved multi-strategy crayfish optimization algorithm for solving numerical optimization problems, called IMCOA, is proposed to address the shortcomings of the original crayfish optimization algorithm for each behavioral strategy. Aiming at the imbalance between local exploitation and global exploration in the summer heat avoidance and competition phases, this paper proposes a cave candidacy strategy and a fitness–distance balanced competition strategy, respectively, so that these two behaviors can better coordinate the global and local optimization capabilities and escape from falling into the local optimum prematurely. The directly foraging formula is modified during the foraging phase. The food covariance learning strategy is utilized to enhance the population diversity and improve the convergence accuracy and convergence speed. Finally, the introduction of an optimal non-monopoly search strategy to perturb the optimal solution for updates improves the algorithm’s ability to obtain a global best solution. We evaluated the effectiveness of IMCOA using the CEC2017 and CEC2022 test suites and compared it with eight algorithms. Experiments were conducted using different dimensions of CEC2017 and CEC2022 by performing numerical analyses, convergence analyses, stability analyses, Wilcoxon rank–sum tests and Friedman tests. Experiments on the CEC2017 and CEC2022 test suites show that IMCOA can strike a good balance between exploration and exploitation and outperforms the traditional COA and other optimization algorithms in terms of its convergence speed, optimization accuracy, and ability to avoid premature convergence. Statistical analysis shows that there is a significant difference between the performance of the IMCOA algorithm and other algorithms. Additionally, three engineering design optimization problems confirm the practicality of IMCOA and its potential to solve real-world problems.

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Section: Initialization Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems

Wang,

Zhang,

Zou

2024

Biomimetics

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Feature Variable Selection for Near‐Infrared Spectroscopy Based on Simulated Annealing Bee Colony Algorithm

Shi,

Tong,

Liu

et al. 2024

Journal of Chemometrics

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Variable selection is an effective method to enhance the modeling performance of near‐infrared spectroscopy. Given the promising application prospects of intelligent optimization algorithms in spectral feature variable selection, this article combines the artificial bee colony algorithm with the simulated annealing algorithm to construct a simulated annealing bee colony algorithm (SABC). To explore the feasibility of SABC for spectral variable selection, SABC was applied to construct a partial least squares spectral quantitative detection model for corn stover cellulose and soil organic matter contents. The modeling performance was compared with that of the full spectrum, genetic algorithm, simulated annealing algorithm, and artificial bee colony algorithm; it was found that the model regression precision established by SABC was the best. For the cellulose and organic matter content detection models, the coefficients of determination of the validation set were 0.9433 and 0.9853, with the relative root mean squared error of 1.7901% and 0.8011%, and the residual prediction deviation of 4.1741 and 8.3931, respectively, which could meet the corresponding actual detection needs. SABC adopted the strategy of multiple runs to select the repeated wavelength variables, effectively reduced variable dimensions and model complexity, improved the prediction performance of the regression model, and provided a new approach for building a high‐performance near‐infrared spectroscopy (NIRS) quantitative calibration model.

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Constructing uniform design tables based on restart discrete dynamical evolutionary algorithm

Zhao,

Wu,

Yang

et al. 2024

Soft Comput

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Discrete artificial bee colony algorithm with fixed neighborhood search for traveling salesman problem

Cited by 9 publications

References 26 publications

An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems

An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems

Feature Variable Selection for Near‐Infrared Spectroscopy Based on Simulated Annealing Bee Colony Algorithm

Constructing uniform design tables based on restart discrete dynamical evolutionary algorithm

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