Search Results - (( evolution optimization path algorithm ) OR ( variable classification model algorithm ))

The Development Of A Robust Algorithm For Uav Path Planning In 3d Environment by Kok, Kai Yit

“…Significant research has been conducted on Unmanned Aerial Vehicle (UAV) path planning using evolutionary algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), and Biogeographic-Based Optimization (BBO). …”
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Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking by Shen, Jiazheng, Hong, Tang Sai, Fan, Luxin, Zhao, Ruixin, Mohd Ariffin, Mohd Khairol Anuar, As’arry, Azizan

“…The EPDE-GWO algorithm is compared with Genetic Algorithm (GA), Simulated Annealing (SA), Dung Beetle Optimizer (DBO), and Particle Swarm Optimization (PSO). …”
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Path optimization using genetic algorithm evolution by Low C.Y., Chong K.H., Salleh K., Johnny K.S.P.

Finding minimum path by using Genetic Algorithm (GA)/ Siti Zuraifah Hashim by Hashim, Siti Zuraifah

“…Roadways and telephone systems are the examples of them. Genetic Algorithms (GA), pioneered by John Holland, applies the principle of evolution found in nature to the problem of finding an optimal solution. …”
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Differential evolution optimization for constrained routing in Wireless Mesh Networks by Sanni, Mistura Laide, Hassan Abdalla Hashim, Aisha, Hassan, Wan Haslina, Ahmed, Gharib Subhi Mahmoud, Anwar, Farhat, Zakaria, Omar

“…This solution addresses efficient and optimal routing path construction for cost and quality metrics of the application. …”
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Classification for large number of variables with two imbalanced groups by Ahmad Hakiim, Jamaluddin

“…This study proposed two algorithms of classification namely Algorithm 1 and Algorithm 2 which combine resampling, variable extraction, and classification procedure. …”
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Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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Literature Review of Optimization Techniques for Chatter Suppression In Machining by A. R., Yusoff, Mohamed Reza Zalani, Mohamed Suffian, Mohd Yusof, Taib

“…Various algorithms can be applied in the optimization of machining problems; however, Differential Evolution is the most appropriate for use in chatter suppression, being less time consuming, locally optimal, and more robust than both Genetic Algorithms, despite their wide applications, and Sequential Quadratic Programming, which is a famous conventional algorithm.…”
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Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous values and simultaneously solve the two problems.This paper presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features.…”
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Comparative analysis of spiral dynamic algorithm and artificial bee colony optimization for position control of flexible link manipulators by Nor Maniha, Abdul Ghani, Nizaruddin, M. Nasir, Azrul Azim, Abdullah Hashim

“…By integrating the ABC algorithm into the manipulator's control system, the goal is to enhance its ability to plan paths and optimize trajectories. …”
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Mixed variable ant colony optimization technique for feature subset selection and model selection by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

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Development of a syncope classification algorithm from physiological signals acquired in tilt-table test by Gan, Ming Hong

“…There are 8 set of feature selection model has built and a total of 24 set of classifiers with 3 different type of classification techniques were developed. …”
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Empirical Analysis of Intra vs. Inter-Subject Variability in VR EEG-Based Emotion Modelling by N.S. Suhaimi, J. Teo, J. Mountstephens

“…Secondly, the data will then be tested and trained with KNN and SVM algorithms. We conduct subject-dependent as well as subject-independent classifications in order to compare intra-against inter-subject variability, respectively in VR EEG-based emotion modeling. …”
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Hybrid genetic algorithm with multi-parents recombination for job shop scheduling problems / Ong Chung Sin by Ong, Chung Sin

“…This mechanism is restricted to search the possible solutions in a critical path. Modification on the path by using neighborhood search significantly reduces the total length of the makespan. …”
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A Hybrid Adaptive Leadership GWO Optimization with Category Gradient Boosting on Decision Trees Algorithm for Credit Risk Control Classification by Suihai, Chen, Chih How, Bong, Po Chan, Chiu

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Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms by Ridha, Hussein Mohammed

“…The IEM algorithm uses the attraction-repulsion mechanism to change the positions of solutions towards the optimality. …”
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New Algorithm of Location Model based on Robust Estimators and Smoothing Approach by Hamid, Hashibah

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Application of Decision Trees in Athlete Selection: A Cart Algorithm Approach by Riska Wahyu, Romadhonia, A'yunin, Sofro, Danang, Ariyanto, Dimas Avian, Maulana, Junaidi Budi, Prihanto

“…This study investigates the application of Decision Trees (DTs), a non-parametric supervised learning method, renowned for its simplicity, interpretability, and wide applicability in various domains, including machine learning for classification and regression tasks. The focus of this study is on the use of DTs, employing the Classification and Regression Trees (CART) algorithm, in the initial screening of athletes. …”
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Using fuzzy association rule mining in cancer classification by Mahmoodian, Sayed Hamid, Marhaban, Mohammad Hamiruce, Abdul Rahim, Raha, Rosli, Rozita, Saripan, M. Iqbal

“…Interpretability of the model, which is developed by fuzzy rules and linguistic variables in this study, has been rarely considered. …”
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Backpropagation vs. radial basis function neural model : Rainfall intensity classification for flood prediction using meteorology data by Chai, S.S., Wong, W.K., Goh, K.L.

“…The influence of the number of training data on the classification results was also analyzed. Results obtained showed, in term of classification accuracy, BPN model performed better than the RFN model. …”
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