Search Results - (( using vector problems algorithm ) OR ( evolution optimisation based algorithm ))

Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage by Huy P.D., Ramachandaramurthy V.K., Yong J.Y., Tan K.M., Ekanayake J.B.

Performance evaluation and benchmarking of an extended computational model of ant colony system for DNA sequence design by Zuwairie, Ibrahim, Mohd Falfazli, Mat Jusof, Mohd Zaidi, Mohd Tumari

“…Ant colony system (ACS) algorithm is one of the biologically inspired algorithms that have been introduced to effectively solve a variety of combinatorial optimisation problems. …”
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A competitive co-evolutionary approach for the nurse scheduling problem by Mohamad Nazri, Maizatul Farhana, Mohd Yusoh, Zeratul Izzah, Basiron, Halizah, Daud, Azlina

“…The competitive approach further exhibits smoother convergence behaviour across generations, indicating stronger optimisation dynamics and improved robustness. These findings demonstrate that competitive co-evolution provides an effective and practical alternative to static fitness-based evolutionary methods for nurse scheduling, with broader applicability to healthcare scheduling and constraint optimisation problems.…”
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Vector Evaluated Gravitational Search Algorithm (VEGSA) for multi-objective optimization problems by Zuwairie, Ibrahim, Badaruddin, Muhammad, Kamarul Hawari, Ghazali, Lim, Kian Sheng, Sophan Wahyudi, Nawawi, Zulkifli, Md. Yusof

“…The proposed algorithm, which is called Vector Evaluated Gravitational Search Algorithm (VEGSA), uses a number of populations of particles. …”
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Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions by Kian, Sheng Lim, Zuwairie, Ibrahim, Salinda, Buyamin, Anita, Ahmad, Faradila, Naim, Kamarul Hawari, Ghazali, Norrima, Mokhtar

“…The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. …”
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A Method For Solving Mult-Objective Optimization Problem: Vector Evaluated Genetic Algorithm (Vega) by Tan, Tun Tai

“…However, it is impractical to solve MOOP by using classical methods due to its complexity. Genetic Algorithms (GAs) are a powerful stochastic search in solving optimization problems. …”
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A Test Vector Minimization Algorithm Based On Delta Debugging For Post-Silicon Validation Of Pcie Rootport by Toh , Yi Feng

“…To solve the problem, a test vector minimizer algorithm is proposed to eliminate redundant test vectors that do not contribute to reproduction of a test failure, hence, improving the debug throughput. …”
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Using the bees algorithm to optimise a support vector machine for wood defect classification by Pham, D.T, Muhammad, Zaidi, Mahmuddin, Massudi, Ghanbarzadeh, Afshin, Koc, Ebubekir, Otri, Sameh

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Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction by Zuriani, Mustaffa

“…The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, Fakhrud Din, Shah Khalid, Kamal Zuhairi Zamli, Aftab Alam

“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya 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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A Multi-objective Evolutionary Algorithm Based On Decomposition For Continuous Optimization Using A Step-function Technique by Chuah, How Siang

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Improving performance of mobile ad hoc networks using efficient Tactical On Demand Distance Vector (TAODV) routing algorithm by Uddin, Mueen, Abdul Rahman, Azizah, Alarifi, Abdulrahman, Talha, Muhammad, Shah, Asadullah, Iftikhar, Mohsin, Zomaya, Albert

“…The proposed TAODV algorithm performs better for solving routing problems in Mobile Ad Hoc networks. …”
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Convergence and Diversity Evaluation for Vector Evaluated Particle Swarm Optimization by Mohd Zaidi, Mohd Tumari, Zuwairie, Ibrahim, Ismail, Ibrahim, Mohd Falfazli, Mat Jusof, Faradila, Naim, Kamarul Hawari, Ghazali, Lim, Kian Sheng, Salinda, Buyamin, Anita, Ahmad

“…An extended PSO algorithm called Vector Evaluated Particle Swarm Optimization (VEPSO) has been introduced to solve multi-objective optimization problems. …”
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Solving Support Vector Machine Model Selection Problem Using Continuous Ant Colony Optimization by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…Ant Colony Optimization has been used to solve Support Vector Machine model selection problem.Ant Colony Optimization originally deals with discrete optimization problem.In applying Ant Colony Optimization for optimizing Support Vector Machine parameters which are continuous variables, there is a need to discretize the continuously value into discrete value.This discretize process would result in loss of some information and hence affect the classification accuracy and seeking time.This study proposes an algorithm that can optimize Support Vector Machine parameters using Continuous Ant Colony Optimization without the need to discretize continuous value for Support Vector Machine parameters.Eight datasets from UCI were used to evaluate the credibility of the proposed hybrid algorithm in terms of classification accuracy and size of features subset.Promising results were obtained when compared to grid search technique, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM.…”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, ., Fakhrud, Din, Shah, Khalid, Kamal Z., Zamli, Alam, Aftab

“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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Hand gesture recognition for autism diagnosis using Support Vector Machine (SVM) Algorithm / Muhammad Asyraf Mohamad Zain by Mohamad Zain, Muhammad Asyraf

“…To counter this problem, a system has been proposed to detect the hand gesture using one of the machine learning technique which is Support Vector Machine (SVM) Algorithm. …”
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Stock market turning points rule-based prediction / Lersak Photong … [et al.] by Photong, Lersak, Sukprasert, Anupong, Boonlua, Sutana, Ampant, Pravi

“…Finally, rule-based optimisation techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE) and Grey Wolf Optimizer (GWO) were used to minimise the amount of time employed in the stock market turning points prediction. …”
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An Improved VEPSO Algorithm for Multi-objective Optimisation Problems by Kamarul Hawari, Ghazali, Zuwairie, Ibrahim, Faradila, Naim, Kian, Sheng Lim, Salinda, Buyamin, Anita, Ahmad, Sophan Wahyudi, Nawawi, Norrima, Mokhtar

“…The vector evaluated particle swarm optimisation algorithm is widely used for such purpose, where this algorithm optimised one objective using one swarm of particles by the guidance from the best solution found by another swarm. …”
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Extending the decomposition algorithm for support vector machines training by Zaki, N,M., Deris, S., Chin, K.K.

“…One can design a dedicated optimizer that will take full advantage of the specific nature of the QP problem in SVM training. The decomposition algorithm developed by Osuna et al. (1997a) reduces the training cost to an acceptable level. …”
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