The problem that we worked with was a dynamic scheduling problem. For this problem, we are given a set of tasks to be scheduled in an allotted time slot, so that the total value of the tasks done is maximized.
1.3Scheduling The task scheduling approach read the task set given and identifies set of tasks and its stages with requirement. Further, the method identifies the set of services and resources. According to the requirement of the tasks, the method computes the selection strategy measures and based...
Jena (Jena, 2015) Propose a Multi-objective Task Scheduling in Cloud Environment Using Nested Particle Swarm Optimization (PSO) Framework. The objective of this work is to optimize the energy and the processing time using a Multi-objective optimization method nested Particle Swarm optimization (TSPSO...
This paper proposes an improved multiobjective multi-verse optimizer (IMOMVO) as a novel population optimization technique to solve task scheduling problems. The IMOMVO is introduced to overcome the drawbacks risen in the original MVO and its latest enhanced version mMVO. The proposed method ...
Naouri Abdenacer has introduced the greedy task graph partitioning offloading algorithm. To help with job scheduling based on device processing power and reduce task communication costs, he employed greedy optimization techniques [4]. Mahenge Michael Pendo John proposed a task uninstallation scheme that...
The result shows that it can reduce about 40% of the energy consumption of the non-power-aware data center and reduce 1.7% energy consumption of the greedy scheduling algorithm in data center scheduling area [18]. Lin et al. used TD-error reinforcement learning to reduce the energy ...
Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple fact
DP is also used as a base method for computational sequence alignment. To further reduce the computational cost and to limit the number of candidate solutions, a greedy strategy is applied. Greedy approach does not take into account all candidate solutions in a single run. Recursion and ...
Greedy Iterative Particle Swarm Optimization is used to disrupt the particle locations. This disruption helps avoid getting stuck in local optima, enabling improved search space exploration and hypothetically generating better solutions. The first step in the method is to create a preliminary TS for cha...
ɛɛstart Initial parameter of ɛɛ-greedy method 0.99 ɛɛend The final parameter of ɛɛ-greedy method 0.01 Multi-objective policy adaptation (MOPA). We proposed a robust approach for policy optimization in the central agent of a federated reinforcement learning framework, particularly...