A new flower pollination algorithm with improved convergence and its application to engineering optimization

Ong, Kok Meng; Ong, Pauline; Kiong, Sia Chee

doi:10.1016/j.dajour.2022.100144

Cited by 14 publications

(5 citation statements)

References 65 publications

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“…

where

is the pollen;

is the best available solution;

is the scaling factor [0, 1];

and

are the levy step size and gamma function; and

and

are the pollen from the different flowers but the same plant species. Further details about FPO can be found in [ 83 ]. Algorithm 5 shows the pseudo code of FPO.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study

Zafar

Hussain

Ali

et al. 2023

Sensors

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In recent decades, the brain–computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset’s dimensionality, increase the computing effectiveness, and enhance the BCI’s performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates (p < 0.01) of 94.83 ± 5.5%, 92.57 ± 6.9%, and 85.66 ± 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications.

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“…

where

is the pollen;

is the best available solution;

is the scaling factor [0, 1];

and

are the levy step size and gamma function; and

and

are the pollen from the different flowers but the same plant species. Further details about FPO can be found in [ 83 ]. Algorithm 5 shows the pseudo code of FPO.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

“…Algorithm 5 shows the pseudo code of FPO. Algorithm 5 Pseudo code of FPO [ 83 ] 1. Generate random pollinators and flowers 2.

…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study

Zafar

Hussain

Ali

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The primary goal of the problem [68] is to minimize the amount of oil consumed when the piston lever is tilted from 0 • to 45 • under four constraints, thus determining H, B, D, and K. The schematic is seen in Figure 19. The mathematical expression of the problem is Equation (23).…”

Section: Piston Lever Designmentioning

confidence: 99%

An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy

2023

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Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic algorithm derived from the distant sense of hearing of sand cats, which shows excellent performance in some large-scale optimization problems. However, the SCSO still has several disadvantages, including sluggish convergence, lower convergence precision, and the tendency to be trapped in the topical optimum. To escape these demerits, an adaptive sand cat swarm optimization algorithm based on Cauchy mutation and optimal neighborhood disturbance strategy (COSCSO) are provided in this study. First and foremost, the introduction of a nonlinear adaptive parameter in favor of scaling up the global search helps to retrieve the global optimum from a colossal search space, preventing it from being caught in a topical optimum. Secondly, the Cauchy mutation operator perturbs the search step, accelerating the convergence speed and improving the search efficiency. Finally, the optimal neighborhood disturbance strategy diversifies the population, broadens the search space, and enhances exploitation. To reveal the performance of COSCSO, it was compared with alternative algorithms in the CEC2017 and CEC2020 competition suites. Furthermore, COSCSO is further deployed to solve six engineering optimization problems. The experimental results reveal that the COSCSO is strongly competitive and capable of being deployed to solve some practical problems.

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“…May require fine-tuning of parameters Bat Algorithm [29] Inspired by echolocation of bats Robust, adaptive, good exploration capabilities May converge slowly on complex landscapes Grey Wolf Optimizer [16] Inspired by social hierarchy of grey wolves Fast convergence, global search capability Parameter sensitivity Flower Pollination Algorithm [30]…”

Section: Fast Convergence Simple Implementationmentioning

confidence: 99%

Unifying Optimization Forces: Harnessing the Fine-Structure Constant in an Electromagnetic-Gravity Optimization Framework

Akhtar,

Kumar,

Verma

et al. 2023

Preprint

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The Electromagnetic-Gravity Optimization (EMGO) framework is a novel optimization technique that integrates the Fine-Structure Constant and leverages electromagnetism and gravity principles to achieve efficient and robust optimization solutions. Through comprehensive performance evaluation and comparative analyses against state-of-the-art optimization techniques, EMGO demonstrates superior convergence speed and solution quality. Its unique balance between exploration and exploitation, enabled by the interplay of electromagnetic and gravity forces, makes it a powerful tool for finding optimal or near-optimal solutions in complex problem landscapes. The research contributes by introducing EMGO as a promising optimization approach with diverse applications in engineering, decision support systems, machine learning, data mining, and financial optimization. EMGO's potential to revolutionize optimization methodologies, handle real-world problems effectively, and balance global exploration and local exploitation establishes its significance. Future research opportunities include exploring adaptive mechanisms, hybrid approaches, handling high-dimensional problems, and integrating with machine learning techniques to further enhance its capabilities. EMGO gives a novel approach to optimization, and its efficacy, advantages, and potential for extensive adoption open new paths for advancing optimization in many scientific, engineering, and real-world domains.

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A new flower pollination algorithm with improved convergence and its application to engineering optimization

Cited by 14 publications

References 65 publications

Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study

Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study

An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy

Unifying Optimization Forces: Harnessing the Fine-Structure Constant in an Electromagnetic-Gravity Optimization Framework

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