Search Results - (( discrete optimization based algorithm ) OR ( parameter optimization model algorithm ))
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Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems
Published 2011“…The main idea is the integration of optimal control and parameter estimation. In this work, a simplified model-based optimal control model with adjustable parameters is constructed. …”
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Assisted History Matching by Using Genetic Algorithm and Discrete Cosine Transform
Published 2014“…Apart from that, the manual way consume too much time, especially when dealing with thousands of well parameters. Hence, this project, which propose the usage of assisted history matching technique with Genetic Algorithm (GA) as the optimization tool and Discrete Cosine Transform (DCT) as the parameter reduction method is carried out in order to achieve the objective of minimizing the time taken to do history matching. …”
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Discrete-time system identification using genetic algorithm with single parent-based mating technique
Published 2024“…The successful validation in real-world systems marks a significant milestone, showing that the SPM technique can significantly improve model optimization in diverse and practical contexts . …”
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Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem
Published 2022“…Sine Cosine Algorithm (SCA) is a population-based metaheuristic method that widely used to solve various optimization problem due to its ability in stabilizing between exploration and exploitation. …”
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Enhancing Harmony Search Parameters Based On Step And Linear Function For Bus Driver Scheduling And Rostering Problems
Published 2018“…Optimization is a major challenge in numerous practical world problems.According to the “No Free Lunch (NFL)” theorem,there is no existing single optimizer algorithm that is able to resolve all issues in an effective and efficient manner.It is varied and need to be solved according to the specific capabilities inherent to certain algorithms making it hard to foresee the algorithm that is best suited for each problem.As a result,the heuristic technique is adopted for this research as it has been identified as a potentially suitable algorithm.Alternative heuristic algorithms are also suggested to obtain optimal solutions with reasonable computational effort.However,the heuristic approach failed to produce a solution that nears optimum when the complexity of a problem increases;therefore a type of nature-inspired algorithm known as meta-euristics which utilises an intelligent searching mechanism over a population is considered and consequently used.The meta-heuristic approach is widely used to substitute heuristic terms and is broadly applied to address problems with regards to driver scheduling.However,this meta-heuristic technique is still unable to address the fairness issue in the scheduling and rostering problems.Hence,this research proposes a strategy to adopt an amendment of the harmony search algorithm in order to address the fairness issue which in turn will escalate the level of fairness in driver scheduling and rostering.The harmony search algorithm is classified as a meta-heuristics algorithm that is capable of solving hard and combinatorial or discrete optimisation problems.In this respect,the three main operators in harmony search,namely the Harmony Memory Consideration Rate (HMCR),Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing local exploitation and global exploration.These parameters influence the overall performance of the HS algorithm,and therefore it is crucial to fine-tune them. …”
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A discrete event simulation for utility accrual scheduling in uniprocessor environment
Published 2011“…This research has focused on the proposed and the development of an event based discrete event simulator for the existing General Utility Scheduling (GUS) to facilitate the reuse of the algorithm under a common simulation environment. …”
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Conference or Workshop Item -
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A discrete event simulation framework for utility accrual scheduling algorithm in uniprocessor environment
Published 2011“…Approach: The study focused on the proposed and the development of an event based discrete event simulator for the existing General Utility Scheduling (GUS) to facilitate the reuse of the algorithm under a common simulation environment. …”
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Image watermarking optimization algorithms in transform domains and feature regions
Published 2012“…The achieved tradeoffs from these techniques between imperceptibility and robustness are controversial.To solve this problem,this study proposes the application of artificial intelligent techniques into digital watermarking by using discrete wavelet transform (DWT) and singular value decomposition (SVD).To protect the copyright information of digital images,the original image is decomposed according to two-dimensional discrete wavelet transform.Subsequently the preprocessed watermark with an affined scrambling transform is embedded into the vertical subband (HLm) coefficients in wavelet domain without compromising the quality of the image.The scaling factors are trained with the assistance of Particle Swarm Optimization (PSO).A new algorithmic framework is used to forecast feasibility of hypothesized watermarked images.In addition,the novelty is to associate the Hybrid Particle Swarm Optimization (HPSO),instead of a single optimization,as a model with SVD.To embed and extract the watermark,the singular values of the blocked host image are modified according to the watermark and scaling factors. …”
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Optimizing Visual Sensor Coverage Overlaps for Multiview Surveillance Systems
Published 2018“…Based on visual sensor parameters information, and the features of the area to be monitored, this paper uses a deterministic modeling approach to model the sensor coverage in a 2-D space. …”
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Optimizing Visual Sensor Coverage Overlaps for Multiview Surveillance Systems
Published 2018“…Based on visual sensor parameters information, and the features of the area to be monitored, this paper uses a deterministic modeling approach to model the sensor coverage in a 2-D space. …”
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Variable Neighborhood Descent and Whale Optimization Algorithm for Examination Timetabling Problems at Universiti Malaysia Sarawak
Published 2025“…A constructive algorithm was developed to generate an initial feasible solution, which was subsequently refined using two primary approaches to evaluate their efficiency: Iterative Threshold Pipe Variable Neighborhood Descent (IT-PVND), and a modified Whale Optimization Algorithm (WOA). …”
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Development of integrated models for distribution network design of perishable products
Published 2015“…A Lagrangian relaxationbased heuristic algorithm is developed to solve the model. The algorithm produces solutions that are within 0.027% of optimality gap.…”
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Optimization of hydropower reservoir system using genetic algorithm for various climatic scenarios
Published 2015“…In order to increase the system efficiency and maximize the power generation, constructed operation models were optimized. To determine the optimum solution in each policy, real coded genetic algorithm is used as an optimization technique. …”
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Machine-learning-based adaptive distance protection relay to eliminate zone-3 protection under-reach problem on statcom-compensated transmission lines
Published 2020“…The BayesNet provides the best integrated MLADR fault classifier model better at a 5 % significance level than other deployed algorithms in the intelligent supervised learning model realization. …”
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Rule-Based Multi-State Gravitational Search Algorithm for Discrete Optimization Problem
Published 2015“…In this study, rule-based multi-state gravitational search algorithm (RBMSGSA) algorithm is proposed to solve discrete combinatorial optimization problems. …”
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Conference or Workshop Item -
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Optimization and discretization of dragonfly algorithm for solving continuous and discrete optimization problems
Published 2024“…Based on the experimental results, the proposed optimized discrete DA has a higher effectiveness than the adapted discrete DA when the performance of the algorithms is compared in terms of both iteration number and computation cost. …”
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