Swarm intelligence-based optimisation algorithms: an overview and future research issues

Hu, Jinqiang; Wu, Husheng; Zhong, B.; Xiao, Renbin

doi:10.1504/ijaac.2020.110077

Cited by 10 publications

(3 citation statements)

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“…Metaheuristic algorithms have shown promising performance on such complicated tasks [3]. Both single and population-based metaheuristics stand out as effective alternatives to traditional optimization methods [4,5]. They tackle various optimization problems Classical CSA using the VSA evolution mechanism to revise and exploit the solution space [37], 2021 2 0.1, 0.5…”

Section: Introductionmentioning

confidence: 99%

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Roeva,

Zoteva

2023

Fermentation

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Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance.

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Section: Introductionmentioning

confidence: 99%

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Roeva,

Zoteva

2023

Fermentation

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“…SAs have become an important method for solving optimization problems because of their excellent self-organization, self-adaptation, and self-learning characteristics. It has been adopted in various domains [ 11 , 12 ], such as image segmentation [ 13 ], wireless networks [ 14 ], unmanned aerial vehicles [ 15 ], target tracking [ 16 ], neural network [ 17 ], MRI classification [ 18 ], feature selection [ 19 ], and engineering problems [ 20 ], and vehicle design [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

et al. 2022

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Whale optimization algorithm (WOA) tends to fall into the local optimum and fails to converge quickly in solving complex problems. To address the shortcomings, an improved WOA (QGBWOA) is proposed in this work. First, quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions. Second, a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA. To verify the advantages of QGBWOA, comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10, 30, 50, and 100 and on CEC 2020 test with dimension 30. Furthermore, the performance results were tested using Wilcoxon signed-rank (WS), Friedman test, and post hoc statistical tests for statistical analysis. Convergence accuracy and speed are remarkably improved, as shown by experimental results. Finally, feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems. QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics. Supplementary Information The online version contains supplementary material available at 10.1007/s42235-022-00297-8.

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“…Intelligent optimization algorithm [3][4][5] has been proved to be e ective in solving parameter optimization problems [6][7][8] and has a good research and development prospect, including Sailed Fish Optimizer (SFO) [9,10], Butter y Optimization algorithm (BOA) [11,12], Equilibrium Optimizer (EO) [13,14], and Path nder Algorithm (PFA) [15,16].…”

Section: Introductionmentioning

confidence: 99%

Image Multithreshold Segmentation Method Based on Improved Harris Hawk Optimization

Dong

Chen

2022

Mathematical Problems in Engineering

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In order to improve the accuracy and performance of traditional image threshold segmentation algorithm, this paper proposes a multithreshold segmentation method named improved Harris hawk optimization (IMHHO). Firstly, IMHHO adopts Tent map and elite opposition-based learning to initialize population and enhance the diversity. Secondly, IMHHO uses quadratic interpolation to generate new individuals and enhance the local search ability. Finally, IMHHO adopts improved Gaussian disturbance method to disturb optimal solution, which coordinates the local and global search ability. Then, the performance of IMHHO is tested based on 14 benchmark functions. In image segmentation, different algorithms are tested to compare the comprehensive performance based on Otsu and Renyi entropy. Experiments show that IMHHO performs better in the three kinds of benchmark functions; the segmentation effect is directly proportional to the number of thresholds; compared with other algorithms, IMHHO has better comprehensive performance.

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Swarm intelligence-based optimisation algorithms: an overview and future research issues

Cited by 10 publications

References 0 publications

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

Image Multithreshold Segmentation Method Based on Improved Harris Hawk Optimization

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