Search Results - (( java application optimization algorithm ) OR ( using guided optimisation algorithm ))
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Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions
Published 2013“…This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. …”
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Article -
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An Improved VEPSO Algorithm for Multi-objective Optimisation Problems
Published 2015“…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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Book Chapter -
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Dual-head marking performance optimisation via evolutionary solutions
Published 2023“…This paper presents a new approach to optimise the performance of a multi-head marking system in terms of its marking speed This processing method named as MMA (Molecular Marking Optimisation algorithm) will adopt the use of Genetic Algorithm. …”
Conference paper -
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Optimization of blood vessel detection in retina images using multithreading and native code for portable devices
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Conference or Workshop Item -
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Performance evaluation of real-time multiprocessor scheduling algorithms
Published 2016“…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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Conference or Workshop Item -
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Improving Vector Evaluated Particle Swarm Optimisation using Multiple Nondominated Leaders
Published 2014“…The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. …”
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Article -
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Route Optimization System
Published 2005“…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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Final Year Project -
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Hybrid genetic algorithm for uncapacitated university examination timetabling problem
Published 2015“…All proposed algorithms are coded in C using Microsoft Visual C++ 6.0 as the compiler. …”
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Thesis -
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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Improvement Of Stereo Matching Algorithm Based On Sum Of Gradient Magnitude Differences And Semi-Global Method With Refinement Step
Published 2018“…A new stereo matching algorithm which uses improved matching cost computation and optimisation using the semi-global method (SGM) is proposed.The absolute difference is sensitive to low textured regions and high noise on the stereo images with radiometric distortions. …”
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Optimisation of distributed generation in electric power systems using fuzzy-genetic algorithm approach
Published 2011“…This model takes into account, the peculiarities of radial distribution networks, such as high R/X (resistance/reactance) ratio, voltage dependency and composite nature of loads.To solve the proposed models, Genetic algorithm (GA) is used as an optimisation technique. …”
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Thesis -
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Hybrid genetic algorithm for university examination timetabling problem
Published 2016“…These new local optimisation techniques will arrange the timeslots and exams using new explicit equations, if and only if, the modification will reduce the penalty cost function. …”
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Article -
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Ant colony optimization algorithm for load balancing in grid computing
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Monograph -
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Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT
Published 2006“…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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Thesis -
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User-friendly tool for power flow analysis and distributed generation optimisation in radial distribution networks
Published 2017“…Three objective functions are formulated into a single optimisation problem and solved with fuzzy genetic algorithm to simultaneously obtain DG optimal size and location. …”
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