Data clustering using hybrid water cycle algorithm and a local pattern search method

Taib, Hasnanizan; Bahreininejad, Ardeshir

doi:10.1016/j.advengsoft.2020.102961

Cited by 19 publications

(9 citation statements)

References 62 publications

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“…The step sizes in this process are constantly adjusted to "zero in" on the respective optimum. Recently, positive-basis techniques to prove the convergence of another pattern-search method on specific classes of functions were developed by researchers [42][43][44]. Outside of such classes, pattern search is not an iterative method that converges to a solution; indeed, pattern-search methods can converge to non-stationary points on some relatively tame problems [45].…”

Section: Methodsmentioning

confidence: 99%

Identifying Unwanted Conditions through Chaotic Area Determination in the Context of Indonesia’s Economic Resilience at the City Level

2021

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The purpose of this research is to determine the unwanted condition as a strategic criterion in measuring the economic resilience of a city. A new approach in determining economic resilience was developed to overcome the weaknesses of the index method commonly used internationally. Based on the output of this research, the development priority program for each city becomes distinctive depending on the status of the city’s economic resilience. Quality improvement programs are used for cities that do not have resilience and retention programs for cities that already have economic resilience. Five piecewise linear regression parameters are applied to identify a statistical model between Income per capita and Pc as a concern variable and modifier variable, and a Z. Model is tested massively involving all 514 cities in Indonesia from 2015 to 2019, covering the components of the modifier variable: local revenue (PAD), poverty, unemployment and concern variable; GRDP and population. The value of the Fraction of variance unexplained (FVU) of the model is 40%. This value is obtained using the Rosenbrock Pattern Search estimation method with a maximum number of iterations of 200 and a convergence criterion of 0.0001. The FVU area is a condition of uncertainty and unpredictability, so that people will avoid this area. This condition is chaotic and declared as an unwanted condition. The chaotic area is located in the value of UZ less than IDR 5,097,592 and Pc < Pc (UZ) = 27,816,310.68, and thus the coordinates of the chaotic boundary area is (5,097,592: 27,816,310.68). FVU as a chaotic area is used as the basis for stating whether or not a city falls into unwanted conditions. A city is claimed not to be economically resilient if the modifier variable Z is in a chaotic boundary.

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Section: Methodsmentioning

confidence: 99%

Identifying Unwanted Conditions through Chaotic Area Determination in the Context of Indonesia’s Economic Resilience at the City Level

2021

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“…Generally, the smaller the intracluster distance or the larger the intercluster distance, the better the clustering performance [62]. In this paper, the sum of squared errors (SSE) is chosen as the objective function.…”

Section: Principle Of Clusteringmentioning

confidence: 99%

Gradient-based elephant herding optimization for cluster analysis

Duan

Liu

et al. 2022

Appl Intell

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Clustering analysis is essential for obtaining valuable information from a predetermined dataset. However, traditional clustering methods suffer from falling into local optima and an overdependence on the quality of the initial solution. Given these defects, a novel clustering method called gradient-based elephant herding optimization for cluster analysis (GBEHO) is proposed. A well-defined set of heuristics is introduced to select the initial centroids instead of selecting random initial points. Specifically, the elephant optimization algorithm (EHO) is combined with the gradient-based algorithm GBO for assigning initial cluster centers across the search space. Second, to overcome the imbalance between the original EHO exploration and exploitation, the initialized population is improved by introducing Gaussian chaos mapping. In addition, two operators, i.e., random wandering and variation operators, are set to adjust the location update strategy of the agents. Nine datasets from synthetic and real-world datasets are adopted to evaluate the effectiveness of the proposed algorithm and the other metaheuristic algorithms. The results show that the proposed algorithm ranks first among the 10 algorithms. It is also extensively compared with state-of-the-art techniques, and four evaluation criteria of accuracy rate, specificity, detection rate, and F-measure are used. The obtained results clearly indicate the excellent performance of GBEHO, while the stability is also more prominent.

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“…In addition, various metaheuristic algorithms have been presented by simulating nature phenomena, animal behaviors, human activities, or physical criteria, such as the genetic algorithm (GA) [13], differential evolution (DE) [14], particle swarm optimization (PSO) [15], artificial bee colony algorithm [16], water cycle algorithm (WCA) [17], squirrel search algorithm [18], gravitational search algorithm (GSA) [19], teaching-learning-based optimization (TLBO) [20], gaining-sharing knowledge-based algorithm [21], and so on. In detail, more metaheuristic algorithms can be found in [22], and they have been widely researched and successfully applied in many scientific fields and practical problems up to now, including data clustering [23,24], stock portfolios [25], knapsack optimization problems [26], multitask optimization [27], and multimodal optimization [28].…”

Section: Introductionmentioning

confidence: 99%

A Novel Discrete Differential Evolution with Varying Variables for the Deficiency Number of Mahjong Hand

Yan

2023

Mathematics

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The deficiency number of one hand, i.e., the number of tiles needed to change in order to win, is an important factor in the game Mahjong, and plays a significant role in the development of artificial intelligence (AI) for Mahjong. However, it is often difficult to compute due to the large amount of possible combinations of tiles. In this paper, a novel discrete differential evolution (DE) algorithm is presented to calculate the deficiency number of the tiles. In detail, to decrease the difficulty of computing the deficiency number, some pretreatment mechanisms are first put forward to convert it into a simple combinatorial optimization problem with varying variables by changing its search space. Subsequently, by means of the superior framework of DE, a novel discrete DE algorithm is specially developed for the simplified problem through devising proper initialization, a mapping solution method, a repairing solution technique, a fitness evaluation approach, and mutation and crossover operations. Finally, several experiments are designed and conducted to evaluate the performance of the proposed algorithm by comparing it with the tree search algorithm and three other kinds of metaheuristic methods on a large number of various test cases. Experimental results indicate that the proposed algorithm is efficient and promising.

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Data clustering using hybrid water cycle algorithm and a local pattern search method

Cited by 19 publications

References 62 publications

Identifying Unwanted Conditions through Chaotic Area Determination in the Context of Indonesia’s Economic Resilience at the City Level

Identifying Unwanted Conditions through Chaotic Area Determination in the Context of Indonesia’s Economic Resilience at the City Level

Gradient-based elephant herding optimization for cluster analysis

A Novel Discrete Differential Evolution with Varying Variables for the Deficiency Number of Mahjong Hand

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