Search Results - (( problem implementation developing algorithm ) OR ( ais implementation plan algorithm ))
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Automatic generation of neural game controller using single and bi-objective evolutionary optimization algorithms for RTS Game
Published 2015“…After that, a bi-objectives algorithm is tested for comparing purposes and this contributed for the next two sub-objectives that is 3) to test the feasibility for implementing the PDE hybrid FFNN. 4) to compare single objective and multi-objective optimization algorithms performances. …”
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Modelling knowledge transfer of nursing students during clinical placement / Nor Azairiah Fatimah Othman
Published 2017“…In order to accelerate the determination of angle stability, particle swarm optimization (PSO) is proposed to be implemented in this study. The application of the proposed algorithm has been justified as the most accurate with lower computation time as compared to other optimization techniques such as evolutionary programming (EP) and artificial immune system (AIS). …”
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Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom
Published 2015“…However, in many cases, implementing the suitable hybrid algorithms for a given optimization problem is a considerably difficult, which in most cases, is time consuming. …”
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Quantum Processing Framework And Hybrid Algorithms For Routing Problems
Published 2010“…The focus of this study is developing a framework of QAPU and hybrid architecture for classical-quantum algorithms. …”
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Simulation of shortest path using a-star algorithm / Nurul Hani Nortaja
Published 2004“…As for WXES3l82, the thesis cover the development of programming code, the code implementation and testing and the evaluation including discussion about the problem/constraints meed in the coding program development. …”
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Computational intelligence based power system security assessment and improvement under multi-contingencies conditions / Nor Rul Hasma Abdullah
Published 2012“…A performance comparison with Artificial Immune System (A1S) in terms of SVSI and loss minimization was made and it is found that the proposed algorithm has been able to produce better results as compared to AIS. …”
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Computational intelligence based power system security assessment and improvement under multicontigencies conditions
Published 2012“…The minimizations of SVSJ and transmission loss are used as two separate objective functions for the development of optimization technique.The effect of reactive power load variation.on transmission loss in the system is also investigated.Consequently,the EP optimization technique is extended for the evaluation of the operating generator scheduling (OGS) to be applied on reactive power control in power system.The results obtained from the study can be used by the power system operators to make a decision either to achieve minimal SVSJ, minimal transmission loss or minimal installation cost.This has also avoided all generators to dispatch power at the same time.Finally,a novel multi-objective Constrained Reactive Power Control (CRPC) algorithm using the state-of-the-art of EP for voltage stability improvement has been developed.A performance comparison with Artificial Immune System (AIS) in terms of SVSJ and loss minimization was made and it is found that the proposed algorithm has been able to produce better results as compared to AIS.The contributions of the studies among the others are the development EP and AIS engine for CPP considered multi-contingencies (N-ni),the development of EP and AIS engine for FACTS installation considered multi-contingencies(N-m) for the determination of FACTS placement using SVSI and optimal sizing of FACTS using EP and AIS, the development of new technique for OGS based on EP optimization technique and the development of multi-objective EP and AIS engines for CRPC considered multi-contingencies (N-m).…”
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Enhancement of Ant System Algorithm for Course Timetabling Problem
Published 2009“…In order to develop a new algorithm for the course scheduling problem, this research follows the experimental research methodology that consist of problem analysis, designing algorithm, implementing algorithm as a computer program in order to examine the results, analyzing the results, and if necessary improving the algorithm by doing all those activities over and over again. …”
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Chaotic Immune Symbiotic Organisms Search algorithm for solving optimisation problem
Published 2023“…Good performance of these heuristics-based algorithm has been implied through various implementation in solving optimisation problems. …”
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Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom
Published 2015“…However, in many cases, implementing the suitable hybrid algorithms for a given optimization problem is a considerably difficult, which in most cases, is time consuming. …”
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12
Development of dynamic programming algorithm for maintenance scheduling problem
Published 2020“…Then, the data of the maintenance team from one of the utilities provider company in Malaysia was collected to be implemented in the development of a dynamic programming algorithm. …”
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Optimization of multi-agent traffic network system with Q-Learning-Tune fitness function
Published 2019“…This study aims to explore the potential of implementing multi-agent-based Genetic Algorithm (GA) with interactive metamodel to acquire regular optimisation on dynamic characteristic of traffic flow. …”
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Metric's thresholds for encoding evolutionary computing representation in software engineering problem
Published 2015“…This paper presents metrics thresholds encoding algorithm as an alternative to replace human experts in developing problem representation. …”
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Parallel distributed genetic algorithm development based on microcontrollers framework
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Faculty timetabling using genetic algorithm
Published 2011“…Faculty Timetabling using Genetic Algorithm (FTGA) is an application that generate optimum timetable for faculty.The target user of this application is faculty staff who responsible in generate timetable.The problem statement of the project is many clashing exist in the timetable.Faculty staff needs to solve the clashing manually.This will waste time and it is a problem for staff to solve the clashing.By implement GA,clashing will be reduced.The objective of the project is to develop aprototype in scheduling application for generates an optimum timetable for a faculty.Genetic algorithm will be implemented.The scope of FTGA is Faculty of Computer Systems & Software Engineering (FCSSE).The methodology use in this project is prototype model.The testing result show 95 out of 100 test cases achieved the maximum fitness value which means there is no clashing in the timetable.The maximum generation is set to 15 generation.Population for each generation is 3 populations.Percentage of FTGA solve the problem is 95%.…”
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Undergraduates Project Papers -
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An experimental study of neighbourhood based metaheuristic algorithms for test case generation satisfying the modified condition / decision coverage criterion
Published 2018“…We have chosen four neighborhood based algorithms which are commonly used in optimization problems and divided them in newly implemented and re-implemented category. …”
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Fast Finite Difference Time Domain Algorithms for Solving Antenna Application Problem
Published 2008“…Existing method, known as FDTD needs a very long processing time to solve this problem. The objective of the thesis is to develop new sequential and parallel algorithms that are faster than the standard Finite Difference Time Domain method. …”
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