- Evolutionary Scheduling and Combinatorial Optimisation | Yi Mei @ VUW
- Edinburgh Napier University
- Evolutionary Scheduling of Dynamic Multitasking Workloads for Big-data Analytics in Elastic Cloud
Niching genetic programming based hyper-heuristic approach to dynamic job shop scheduling: an investigation into distance metrics. A comprehensive analysis on reusability of GP-evolved job shop dispatching rules. A memetic algorithm-based indirect approach to web service composition. Evolutionary scheduling and combinatorial optimisation: Applications, challenges, and future directions.
Evolutionary web service composition: A graph-based memetic algorithm. Improving job shop dispatching rules via terminal weighting and adaptive mutation in genetic programming.source site
Evolutionary Scheduling and Combinatorial Optimisation | Yi Mei @ VUW
Many-objective genetic programming for job-shop scheduling. Genetic programming based hyper-heuristics for dynamic job shop scheduling: cooperative coevolutionary approaches.
Particle swarm optimisation with sequence-like indirect representation for web service composition. Particle swarm optimization for multi-objective web service location allocation. On investigation of interdependence between sub-problems of the travelling thief problem.
Evolving self-adaptive tabu search algorithm for storage location assignment problems. A restricted neighbourhood tabu search for storage location assignment problem. Heuristic evolution with genetic programming for traveling thief problem. Improving efficiency of heuristics for the large scale traveling thief problem.
Scaling up solutions to storage location assignment problems by genetic programming. A genetic programming-based hyper-heuristic approach for storage location assignment problem. Cooperative coevolution with route distance grouping for large-scale capacitated arc routing problems. Variable neighborhood decomposition for large scale capacitated arc routing problem.
Edinburgh Napier University
Decomposing large-scale capacitated arc routing problems using a random route grouping method. Evolutionary computation for dynamic capacitated arc routing problem. TDMA scheduling have been studied in terms of minimizing packet delay, improving fairness, maximizing parallel operation, minimizing the energy consumption, and shortening the total slots to finish a set of transmission tasks. Allowing the sensors to turn their radio off when not active is a common energy-saving strategy. Intelligent search algorithms were used to obtain an efficient scheduler In this work we look at the usage of 2 particular algorithms: evolutionary EA and particle swarm optimization PSO.
Then, we propose a hybrid algorithm that utilizes both EA and PSO algorithms using different optimization functions. The performance of the propped algorithm is evaluated using simulation.
- Ecstasy, The Complete Guide [MDMA];
- The Gate of Heaven: The Story of Congregation Shaar Hashomayim in Montreal, 1846-1996.
- Artificial Reefs in Fisheries Management.
- Evolutionary Scheduling.
The obtained simulation results demonstrated that the PSO different optimization functions will give different fitness values and results. Python Branch: master New pull request.
Evolutionary Scheduling of Dynamic Multitasking Workloads for Big-data Analytics in Elastic Cloud
Find File. Download ZIP. Sign in Sign up.
- Swipe to navigate through the chapters of this book.
- Diffusion in Semiconductors!
- The Italians One-Night Love-Child.
- Evaluation and Treatment of the Aging Face.
- Evolutionary Scheduling : Keshav Dahal : .
Launching GitHub Desktop Go back. Launching Xcode Launching Visual Studio Pull request Compare This branch is even with fdorssers:master.