Search Results - (( developing cost evolution algorithm ) OR ( java implementation path algorithm ))
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Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer
Published 2017“…The development tools used in developing this project is NetBeans by using Java for the implementation of the coding. The methodology that used for developing this system is the Dijkstra’s algorithm. …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization
Published 2019“…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment
Published 2021“…In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. …”
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Economic power dispatch solution with non-smooth cost functions using differential evolution: article / Muhammad Firdaus Abd Rahim
Published 2011“…This thesis proposes a solution for Economic Dispatch problems with non-smooth cost functions using Differential Evolution (DE) algorithm. …”
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Smart appointment organizer for mobile application / Mohd Syafiq Adam
Published 2009“…The main component of this prototype is the use of Dijkstra algorithm to compute the shortest path from source of appointment to the 6 points of destinations within UiTM Shah Alam. …”
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Economic power dispatch solution with non-smooth cost functions using differential evolution / Muhammad Firdaus Abd Rahim
Published 2011“…This thesis proposes a solution for Economic Dispatch problems with non-smooth cost functions using Differential Evolution (DE) algorithm. …”
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Special protection and control scheme based on generation rescheduling using differential evolution and electromagnetism-like algorithm
Published 2016“…The performance of the proposed DEAM algorithm is validated with both the normal DE and Genetic Algorithm (GA) in terms of the fitness convergence and generation fuel cost. …”
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The Development Of A Robust Algorithm For Uav Path Planning In 3d Environment
Published 2016“…Thus, a new algorithm known as infection evolution (IE) was developed in this study. …”
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Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity
Published 2018“…Consequently, this paper proposes a ranking self-adaptive differential evolution (rsaDE) feature selection algorithm. The proposed algorithm is capable of selecting the optimal feature subsets while improving the recognition of acceleration activity using a minimum number of features. …”
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Hybrid Meta-Heuristic Algorithms For Solving Vehicle Routing Problems
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Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff
Published 2018“…This thesis proposed an efficient algorithm, namely, the Quality of Service (QoS) and Energy Efficient Aware (QEEA). …”
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Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff
Published 2017“…The results showed that the QEEA algorithm outperformed the other algorithms as it could achieve up to 18% of maximum throughput, 27% reduction in ECR, and 36% improvement in EE in terms of radius ranging from 200 m to 1000 m. …”
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Optimization of an extended H-infinity controller for unmanned helicopter control using multiobjective differential evolution (MODE)
Published 2015“…Purpose – The purpose of this paper is to develop a multiobjective differential evolution (MODE)-based extended H-infinity controller for autonomous helicopter control. …”
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Evolutionary cost cognizant regression test prioritization for object-oriented programs based on fault dependency
Published 2018“…However, costs of test and severities of faults varied. Furthermore, they did not consider incorporating evolution process such as applying genetic algorithms to their technique. …”
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Self-configured link adaptation using channel quality indicator-modulation and coding scheme mapping with partial feedback for green long-term evolution cellular systems
Published 2015“…To achieve this objective, an iterative approach based on swarm intelligence is used to find the optimal CQI threshold at which the competing criteria are optimized. Since the developed downlink scheduler and the partial feedback scheme affect the QoS, self-configured versions of both algorithms are developed to provide QoS provisioning. …”
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Identification and predictive control of spray tower system using artificial neural network and differential evolution algorithm
Published 2015“…This includes the use of an artificial neural network (ANN) based predictive control strategy and differential evolution (DE) optimization algorithm to determines the optimal control signal, uk (liquid droplet size, dD) by minimizing the cost function such that the output is set below the allowable PM concentration. …”
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Automatic generation of neural game controller using single and bi-objective evolutionary optimization algorithms for RTS Game
Published 2015“…The proposed EC methods are Genetic Algorithm (GA), Differential Evolution (DE), Evolutionary Programming (EP), and Pareto-based Differential Evolution (PDE). …”
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Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decisio...
Published 2023“…VL-WIDE was also integrated with the solution selection model based on the Analytical Hierarchical Process (AHP) that considers decision-maker preference for the optimized objectives. Comparing this developed algorithm with other algorithms shows its superiority in multi-objective optimization (MOO) evaluation metrics. …”
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Y-type Random 2-satisfiability In Discrete Hopfield Neural Network
Published 2024“…Additionally, the proposed Hybrid Differential Evolution Algorithm was implemented into the training phase to ensure that the cost function of the Discrete Hopfield Neural Network is minimized. …”
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