Crayfish optimization algorithm

Jia, Heming; Rao, Honghua; Wen, Changsheng; Mirjalili, Seyedali

doi:10.1007/s10462-023-10567-4

Cited by 104 publications

(47 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section presents the base Crayfish Optimization Algorithm (COA) 4 , as well as the inspiration behind the preparation of an altered version used for the purposes of our research. Subsequently, details and pseudocode of the modified algorithm are provided.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The COA 4 , a novel optimization metaheuristic emulates the foraging, avoidance, and social behavior patterns observed in crayfish populations 4 . This algorithm leverages principles from the biological realm to tackle optimization problems in various fields using three distinct operating phases.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Several metaheuristics are included in a comparative analysis of LSTM-ATT hyperparameter tuning. The introduced MCOA algorithm alongside the original COA 4 are tested. Several well-established algorithms are included in the comparison as well such as the GA 34 , PSO 35 , FA 36 , GWO 37 , BSO 38 and COLSHADE 39 algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…An extensive literature review suggests that there is a research gap concerning the application of optimized LSTM networks for Parkinson’s diagnosis. Additionally, the application of emerging optimizers such as the crayfish optimization algorithm (COA) 4 . has yet to be explored.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tuning attention based long-short term memory neural networks for Parkinson’s disease detection using modified metaheuristics

Cuk,

Bezdan,

Jovanovic

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Parkinson’s disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease include resting tremors, muscle rigidity, bradykinesia, and postural instability. Diagnosis relies mainly on clinical evaluation, lacking reliable diagnostic tests and being inherently imprecise and subjective. Early detection of PD is crucial for initiating treatments that, while unable to cure the chronic condition, can enhance the life quality of patients and alleviate symptoms. This study explores the potential of utilizing long-short term memory neural networks (LSTM) with attention mechanisms to detect Parkinson’s disease based on dual-task walking test data. Given that the performance of networks is significantly inductance by architecture and training parameter choices, a modified version of the recently introduced crayfish optimization algorithm (COA) is proposed, specifically tailored to the requirements of this investigation. The proposed optimizer is assessed on a publicly accessible real-world clinical gait in Parkinson’s disease dataset, and the results demonstrate its promise, achieving an accuracy of 87.4187 % for the best-constructed models.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tuning attention based long-short term memory neural networks for Parkinson’s disease detection using modified metaheuristics

Cuk,

Bezdan,

Jovanovic

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The experimental design of algorithm optimization performance comparison is as follows: In addition to the eight optimization algorithms mentioned above, including IBFO-A, Two novel and improved optimization algorithms for PSO and BFO :AWPSO 25 and ChaoticBFO 26 , as well as the two latest advanced optimization algorithms COA 63 and GO 64 , a total of 12 optimization algorithms were compared in 10 sets of different types of advanced benchmark functions in CEC2019. We set experimental parameters uniformly for all optimization algorithms.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

Meng,

Cong,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.

show abstract

Stochastic operation of Volt-VAr optimization and network reconfiguration-based energy management under different loading patterns

Lakra,

Bag

2024

Electr Eng

View full text Add to dashboard Cite

Crayfish optimization algorithm

Cited by 104 publications

References 83 publications

Tuning attention based long-short term memory neural networks for Parkinson’s disease detection using modified metaheuristics

Tuning attention based long-short term memory neural networks for Parkinson’s disease detection using modified metaheuristics

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

Stochastic operation of Volt-VAr optimization and network reconfiguration-based energy management under different loading patterns

Contact Info

Product

Resources

About