Search Results - (( intelligence model scheduling algorithm ) OR ( intelligence based e algorithm ))

African Buffalo Optimization (ABO): A New Metaheuristic Algorithm by Odili, Julius Beneoluchi, M. N. M., Kahar

“…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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Process Planning Optimization In Reconfigurable Manufacturing Systems by Musharavati, Farayi

“…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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Self-configured link adaptation using channel quality indicator-modulation and coding scheme mapping with partial feedback for green long-term evolution cellular systems by Salman, Mustafa Ismael

“…Consequently, a modified downlink scheduler based on a Packet Prediction Mechanism (PPM) is conducted at the eNodeB. …”
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Home Energy Management Systems: A Review of the Concept, Architecture, and Scheduling Strategies by Han B., Zahraoui Y., Mubin M., Mekhilef S., Seyedmahmoudian M., Stojcevski A.

“…This paper reveals the trend from simple to complex in the architecture and functionality of HEMSs, discusses the challenges for future improvements in modeling and scheduling, and shows the development of various modeling and scheduling methods. …”

Optimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: An intelligent multi-objective optimization approach by Premkumar M., Hashim T.J.T., Ravichandran S., Sin T.C., Chandran R., Alsoud A.R., Jangir P.

“…The results, benchmarked against several advanced metaheuristic algorithms, reveal the proposed algorithm's superior performance. …”

Optimal Maintenance Scheduling for Multi-Component E-Manufacturing System by Arab, Ali

“…Then, using genetic algorithm-based software which is called SimRunner and has been embedded by ProModel, the scheduling optimization procedure is run to find optimum maintenance schedule. …”
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Semantic-Based Scalable Decentralized Grid Resource Discovery by Mahamat Issa , Hassan, Azween, Abdullah

“…Currently most Grid RDs adopt a centralized or hierarchical model. However, this model is characterized by poor scalability, dynamism and load-balancing features. …”
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Sustainability in intelligent building environments using weighted priority scheduling algorithm by Shahi, Ahmad, Sulaiman, Md Nasir, Mustapha, Norwati, Perumal, Thinagaran, Meimandi Parizi, Reza

“…In this paper, we propose a new decision-making model with a weighted-priority scheduling algorithm that solves the conflicts to achieve efficient and sustainable communication response among heterogeneous systems. …”
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Gas turbines health prognostics: A short review by Tamiru, A.L., Fakhruldin, M.H., Mohd Amin, A.M., Ainul, A.M.

“…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 by Tamiru, A.L., Fakhruldin, M.H., Mohd Amin, A.M., Ainul, A.M.

“…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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Intelligent Optimization Systems for MaintenanceScheduling of Power Plant Generators by Ismail F.B., Randhawa G.S., Al-Bazi A., Alkahtani A.A.

“…This paper presents a Genetic Algorithm (GA) and Ant-Colony (AC) optimization model for power plant generators� maintenance scheduling. …”

Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing by Alobaedy, Mustafa Muwafak Theab

“…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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Application of an evolutionary algorithm-based ensemble model to job-shop scheduling by Tan, Choo Jun, Neoh, Siew Chin, Lim, Chee Peng, Hanoun, Samer, Wong, Wai Peng, Loo, Chu Kiong, Zhang, Li, Nahavandi, Saeid

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Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques by Hammid, Ali Thaeer

“…Secondly, this approach hybridizing the FA with the rough algorithm (RA), where RA is used to control the steps of randomness for the FA while optimizing the weights of the standard BPNN model. …”
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Intelligent optimization for charging scheduling of electric vehicle using exponential Harris Hawks technique by Devendiran R., Kasinathan P., Ramachandaramurthy V.K., Subramaniam U., Govindarajan U., Fernando X.

Modelling of heuristic distribution algorithm to optimize flexible production scheduling in Indian industry by Reddy, Guduru Ramakrishna, Singh, Harpreet, Domeika, Aurelijus, Manoj Kumar, Nallapaneni, Quanjin, Ma

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Managing risk in production scheduling under uncertain disruption by Sarker, Ruhul, Essam, Daryl, Kamrul Hasan, S.M., Karim, A.N. Mustafizul

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Energy management scheduling for microgrids in the virtual power plant system using artificial neural networks by Abdolrasol M.G.M., Hannan M.A., Suhail Hussain S.M., Ustun T.S., Sarker M.R., Ker P.J.

Series division method based on PSO and FA to optimize Long-Term Hydro Generation Scheduling by Hammid, Ali Thaeer, M. H., Sulaiman

“…To deal with this complicated problem, Series division method (SDM) based on the practical swarm optimization and the firefly algorithm is proposed in this paper. The SDM is to make a division on the Swarm Intelligence (SI) algorithm which is to be a number of particles searching collections that properly can be regarded as divisions. …”
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Enhancing Harmony Search Parameters Based On Step And Linear Function For Bus Driver Scheduling And Rostering Problems by Mansor, Nur Farraliza

“…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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