Prairie Dog Optimization Algorithm

Ezugwu, Absalom E.; Agushaka, Jeffrey O.; Abualigah, Laith; Mirjalili, Seyedali; Gandomi, Amir H.

doi:10.1007/s00521-022-07530-9

Cited by 380 publications

(101 citation statements)

References 60 publications

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“…Further statistical analysis was carried out using mean, standard deviation, and Friedman and Wilcoxon tests. The details about the 12 engineering problems can be found at the following: The welded beam design problem [ 35 , 36 ] The compression spring design problem (CSD) [ 37 ] The pressure vessel design problem (PVD) [ 38 ] The speed reducer design problem (SRD) [ 39 ] The three-bar truss design problem (3-BTD) [ 40 ] The gear train design problem (GTD) [ 41 ] The cantilever beam design problem (CBD) [ 42 ] The optimal design of I-shaped beam (IBD) [ 43 ] The tubular column design (TCD) [ 44 ] The piston lever design problem (PLD) [ 43 ] The corrugated bulkhead design problem (Cbhd) [ 45 ] The reinforced concrete beam design problem (RCB) [ 46 ] …”

Section: Resultsmentioning

confidence: 99%

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

et al. 2022

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This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.

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Section: Resultsmentioning

confidence: 99%

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

et al. 2022

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“…Many proposed metaheuristic algorithms have provided optimal or near-optimal solutions to many real-world applications, including various feature selection problems [ 24 ]. Some of which include the Whale Optimization Algorithm (WOA) and its hybrid [ 16 , 25 , 26 ], Cuckoo Search Optimization Algorithm (CSO) [ 27 – 29 ], DragonFly Algorithm (DA) (Chantar et al, 2021; Cui et al, 2020; Sree Ranjini & Murugan, 2017) [ 30 – 32 ], Prairie Dog Optimization (PDO) Algorithm [ 33 ] and many more.…”

Section: Related Literaturementioning

confidence: 99%

Binary dwarf mongoose optimizer for solving high-dimensional feature selection problems

2022

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Selecting appropriate feature subsets is a vital task in machine learning. Its main goal is to remove noisy, irrelevant, and redundant feature subsets that could negatively impact the learning model’s accuracy and improve classification performance without information loss. Therefore, more advanced optimization methods have been employed to locate the optimal subset of features. This paper presents a binary version of the dwarf mongoose optimization called the BDMO algorithm to solve the high-dimensional feature selection problem. The effectiveness of this approach was validated using 18 high-dimensional datasets from the Arizona State University feature selection repository and compared the efficacy of the BDMO with other well-known feature selection techniques in the literature. The results show that the BDMO outperforms other methods producing the least average fitness value in 14 out of 18 datasets which means that it achieved 77.77% on the overall best fitness values. The result also shows BDMO demonstrating stability by returning the least standard deviation (SD) value in 13 of 18 datasets (72.22%). Furthermore, the study achieved higher validation accuracy in 15 of the 18 datasets (83.33%) over other methods. The proposed approach also yielded the highest validation accuracy attainable in the COIL20 and Leukemia datasets which vividly portray the superiority of the BDMO.

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“…Similarly, a recent metaheuristic algorithm named Prairie Dog Optimization (PDO) has been proposed to solve classical benchmark functions and real-life optimization problems. The algorithms were inspired by the natural behavioral pattern of the prairie dogs with respect to their foraging and burrowing lifestyle 79 .…”

Section: Related Workmentioning

confidence: 99%

Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models

Oyelade

Ezugwu

2022

Sci Rep

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Feature classification in digital medical images like mammography presents an optimization problem which researchers often neglect. The use of a convolutional neural network (CNN) in feature extraction and classification has been widely reported in the literature to have achieved outstanding performance and acceptance in the disease detection procedure. However, little emphasis is placed on ensuring that only discriminant features extracted by the convolutional operations are passed on to the classifier, to avoid bottlenecking the classification operation. Unfortunately, since this has been left unaddressed, a subtle performance impairment has resulted from this omission. Therefore, this study is devoted to addressing these drawbacks using a metaheuristic algorithm to optimize the number of features extracted by the CNN, so that suggestive features are applied for the classification process. To achieve this, a new variant of the Ebola-based optimization algorithm is proposed, based on the population immunity concept and the use of a chaos mapping initialization strategy. The resulting algorithm, called the immunity-based Ebola optimization search algorithm (IEOSA), is applied to the optimization problem addressed in the study. The optimized features represent the output from the IEOSA, which receives the noisy and unfiltered detected features from the convolutional process as input. An exhaustive evaluation of the IEOSA was carried out using classical and IEEE CEC benchmarked functions. A comparative analysis of the performance of IEOSA is presented, with some recent optimization algorithms. The experimental result showed that IEOSA performed well on all the tested benchmark functions. Furthermore, IEOSA was then applied to solve the feature enhancement and selection problem in CNN for better prediction of breast cancer in digital mammography. The classification accuracy returned by the IEOSA method showed that the new approach improved the classification process on detected features when using CNN models.

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Prairie Dog Optimization Algorithm

Cited by 380 publications

References 60 publications

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

Binary dwarf mongoose optimizer for solving high-dimensional feature selection problems

Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models

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