Nature Inspired Clustering – Use Cases of Krill Herd Algorithm and Flower Pollination Algorithm

Kowalski, Piotr; Łukasik, Szymon; Charytanowicz, Małgorzata; Kulczycki, Piotr

doi:10.1007/978-3-030-01632-6_6

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…Clustering was performed via the K-means algorithm [73], which is provided as part of scikit-learn's clustering module. This algorithm has the advantage of providing high quality clusters (e.g., [74,75]), while also being straightforward and interpretable [76,77]. Furthermore, many of the drawbacks of K-means, such as its sensitivity to outliers and computational inefficiency [78,79], were not applicable to our use case, because all of the laterality measures were on a scale from 1 to 5, and the relatively small size of the dataset renders any runtime inefficiencies extremely minimal.…”

Section: Clusteringmentioning

confidence: 99%

Exploring Footedness, Throwing Arm, and Handedness as Predictors of Eyedness Using Cluster Analysis and Machine Learning: Implications for the Origins of Behavioural Asymmetries

Rodway,

Schepman

2024

Symmetry

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Behavioural asymmetries displayed by individuals, such as hand preference and foot preference, tend to be lateralized in the same direction (left or right). This may be because their co-ordination conveys functional benefits for a variety of motor behaviours. To explore the potential functional relationship between key motor asymmetries, we examined whether footedness, handedness, or throwing arm was the strongest predictor of eyedness. Behavioural asymmetries were measured by self-report in 578 left-handed and 612 right-handed individuals. Cluster analysis of the asymmetries revealed four handedness groups: consistent right-handers, left-eyed right-handers, consistent left-handers, and inconsistent left-handers (who were left-handed but right-lateralized for footedness, throwing and eyedness). Supervised machine learning models showed the importance of footedness, in addition to handedness, in determining eyedness. In right-handers, handedness was the best predictor of eyedness, followed closely by footedness, and for left-handers it was footedness. Overall, predictors were more informative in predicting eyedness for individuals with consistent lateral preferences. Implications of the findings in relation to the origins and genetics of handedness and sports training are discussed. Findings are related to fighting theories of handedness and to bipedalism, which evolved after manual dexterity, and which may have led to some humans being right-lateralized for ballistic movements and left-lateralized for hand dexterity.

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Section: Clusteringmentioning

confidence: 99%

Exploring Footedness, Throwing Arm, and Handedness as Predictors of Eyedness Using Cluster Analysis and Machine Learning: Implications for the Origins of Behavioural Asymmetries

Rodway,

Schepman

2024

Symmetry

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“…The 122 articles published related to KH show its application in many areas, which can be classified into continuous optimization, combinatorial optimization, constrained optimization, multi-objective optimization, dynamic, and noisy environment engineering, and fuzzy systems. Table 1 gives a summary of these application areas and the number of publications in each [9,[57][58][59][60][61][62][63][64][65][66][67][68][69][70]. The clustered column chart, presented in Figure 3, visually summarizes and compares the number of publications across the various application areas identified in Table 1.…”

Section: B Application Areas Of Khmentioning

confidence: 99%

Influence of Initializing Krill Herd Algorithm With Low-Discrepancy Sequences

Agushaka

Ezugwu

2020

IEEE Access

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The krill herd (KH) algorithm is a global metaheuristic algorithm that was initially proposed for solving continuous optimization problems. The KH algorithm, since inception, has generated considerable real-world application interests in the research community. The standard algorithm solution implementation steps follow the initialization mechanism, which relies mainly on educated guesses or random initialization solution generation. Therefore, to improve the performance of the KH algorithm, the current study is set to investigate the influence of initializing the KH algorithm with three low-discrepancy sequences, such as the Faure sequence, Sobol sequence, and Van der Corput sequence. These low-discrepancy sequences are known to be more uniformly distributed across the problem search space than the commonly used random number initialization method. The study also evaluates the influence of population size on the performance of the proposed variants of the improved KH algorithms. The experimental results show significant improvements for the enhanced KH algorithms in terms of performance and the quality of solutions obtained; particularly on standard benchmarked high-dimension test problem instances, where the enhanced KH variants outperformed the existing basic KH algorithm for all the test functions evaluated. Similarly, the results for low dimension test cases showed less sensitivity to the initialization schemes, as the performance of our proposed improved scheme was comparable to that of the basic KH algorithm. However, in most cases, as the problem dimension was scaled up, the enhanced KH outperformed the basic KH. Evaluation results based on the population size of the algorithm, revealed that when the number of Krill is set at 25, the Sobol based KH initialization scheme performed better than did the other methods. Although, the Van der Corput and Faure based KH initialization schemes showed similar sensitivity when the dimension was set at 20. As we varied the population size of Krill, it was observed that the performance of the Sobol based KH initialization scheme deteriorated, whereas the other two methods showed superior performance. Overall, the findings from this study revealed that there are significant improvements in the performance of KH algorithm when initialized with low-discrepancy sequences.

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“…The heuristic optimization plays a very important role in the design of complex, interconnected systems. One of the most significant heuristic optimization algorithms are swarm intelligence algorithms, which includes the following swarming based solutions: ant colony optimization (ACO) [67], firefly algorithm (FFA) [68], black hole optimization (BHO) [69], bee colony algorithm (BCA) [70], bacteria algorithm (BA) [71], krill herd algorithm (KHA) [72], bat algorithm (BAT-A) [73], wasp swarm algorithm (WSA) [74], adaptive culture model (ACM) [75] and flower pollination algorithm (FPA) Figure 7. The model of a single source solution (one sequence is supplied by one supplier).…”

Section: Flower Pollination Algorithm-based Optimization and Its Valimentioning

confidence: 99%

Optimization of Just-In-Sequence Supply: A Flower Pollination Algorithm-Based Approach

et al. 2019

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The just-in-sequence inventory strategy, as an important part of the supply chain solutions in the automotive industry, is based on feedback information from the manufacturer. The performance, reliability, availability and cost efficiency are based on the parameters of the members of the supply chain process. To increase the return on assets (ROA) of the manufacturer, the optimization of the supply process is unavoidable. Within the frame of this paper, the authors describe a flower pollination algorithm-based heuristic optimization model of just-in-sequence supply focusing on sustainability aspects, including fuel consumption and emission. After a systematic literature review, this paper introduces a mathematical model of just-in-sequence supply, including assignment and scheduling problems. The objective of the model is to determine the optimal assignment and schedule for each sequence to minimize the total purchasing cost, which allows improving cost efficiency while sustainability aspects are taken into consideration. Next, a flower pollination algorithm-based heuristic is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to increase cost-efficiency in just-in-sequence solutions.

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Nature Inspired Clustering – Use Cases of Krill Herd Algorithm and Flower Pollination Algorithm

Cited by 7 publications

References 24 publications

Exploring Footedness, Throwing Arm, and Handedness as Predictors of Eyedness Using Cluster Analysis and Machine Learning: Implications for the Origins of Behavioural Asymmetries

Exploring Footedness, Throwing Arm, and Handedness as Predictors of Eyedness Using Cluster Analysis and Machine Learning: Implications for the Origins of Behavioural Asymmetries

Influence of Initializing Krill Herd Algorithm With Low-Discrepancy Sequences

Optimization of Just-In-Sequence Supply: A Flower Pollination Algorithm-Based Approach

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