Search Results - (( intelligence model computer algorithm ) OR ( intelligence based scheduling algorithm ))
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Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing
Published 2015“…One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. …”
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African Buffalo Optimization (ABO): A New Metaheuristic Algorithm
Published 2015“…The African Buffalo Optimization (A.B.0) algorithm simulates the African buffalos' behaviour by encapsulation in a mathematical model; which solves a number of discrete optimization problems using graph-based route planning, job scheduling and it extends Swarm Intelligence paradigms. …”
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Dynamic traffic light sequence algorithm using RFID
Published 2008“…Results: The simulation has shown that, the dynamic sequence algorithm has the ability to intelligently adjust itself even with the presence of some extreme cases. …”
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Process Planning Optimization In Reconfigurable Manufacturing Systems
Published 2008“…The five (5) AADTs include; a variant of the simulated annealing algorithm that implements heuristic knowledge at critical decision points, two (2) cooperative search schemes based on a “loose hybridization” of the Boltzmann Machine algorithm with (i) simulated annealing, and (ii) genetic algorithm search techniques, and two (2) modified genetic algorithms. …”
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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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Development of optimized maintenance scheduling model for coal-fired power plant boiler
Published 2023“…Computing intelligence is a soft-computing subset of artificial intelligence referring to the potential of a computer to gain knowledge from an experimental observations or specific task. …”
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A New Grid Resource Discovery Framework
“…Resource discovery (RD) is an important key issue in grid systems since resource reservation and task scheduling are based on it. This paper proposes a novel semantic-based scalable decentralized grid RD framework. …”
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A New Grid Resource Discovery Framework
Published 2011“…Resource discovery (RD) is an important key issue in grid systems since resource reservation and task scheduling are based on it. This paper proposes a novel semantic-based scalable decentralized grid RD framework. …”
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A New Grid Resource Discovery Framework
Published 2009“…Resource discovery (RD) is an important key issue in grid systems since resource reservation and task scheduling are based on it. This paper proposes a novel semantic-based scalable decentralized grid RD framework. …”
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Home Energy Management Systems: A Review of the Concept, Architecture, and Scheduling Strategies
Published 2024“…In addition, the common objectives and constraints related to scheduling optimization are classified, and several optimization methods in the literature, including various intelligent algorithms, have been introduced, compared, and critically analyzed. …”
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Semantic-Based Scalable Decentralized Resource Discovery
Published 2010“…Resource Discovery (RD) is a key issue in Grid systems since resource reservation and task scheduling are based on it. RD is about locating an appropriate resource type that match the user’s application requirements. …”
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Multi agent quality of service routing based on scheme ant colony optimization algorithm
Published 2014“…The important new aspect of ACR-QoS is the combination of constraint-based routing and DiffServ architecture, in Swarm Intelligence (SI) structure where a set of artificial ants is used to determine the optimal path for each class to construct class-based routing tables. …”
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Semantic-Based Scalable Decentralized Grid Resource Discovery
Published 2009“…Resource Discovery (RD) is a key issue in Grid systems since resource reservation and task scheduling are based on it. RD is about locating an appropriate resource type that matches the user’s application requirements. …”
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Gas turbines health prognostics: A short review
Published 2016“…The reviewed methods include regression methods, physics based models, computational intelligence (artificial neural network and fuzzy systems, evolutionary-based method), and hybrid approaches. …”
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Gas turbines health prognostics: A short review
Published 2016“…The reviewed methods include regression methods, physics based models, computational intelligence (artificial neural network and fuzzy systems, evolutionary-based method), and hybrid approaches. …”
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