1. : What is the primary goal of search algorithms in discrete optimization within Parallel and Distributed Computing?
(A) Minimize communication overhead
(B) Minimize computation time
(C) Maximize parallelism
(D) Minimize load balancing
2. : Which search algorithm is commonly used for discrete optimization problems in a parallel setting?
(A) Breadth-First Search (BFS)
(B) Depth-First Search (DFS)
(C) Genetic Algorithms (GA)
(D) A* Search Algorithm
3. : What is dynamic load balancing in the context of parallel computing for discrete optimization?
(A) Allocating more tasks to faster processors
(B) Adjusting the distribution of computational load during runtime
(C) Minimizing the number of processors used
(D) Balancing the memory usage across processors
4. : Which factor is critical for the effectiveness of dynamic load balancing in parallel discrete optimization?
(A) Number of processors
(B) Communication latency
(C) Type of optimization problem
(D) Initial load distribution
5. : In the context of parallel discrete optimization, what is a potential drawback of using a centralized search algorithm?
(A) Increased parallelism
(B) Lower communication overhead
(C) Bottleneck at the central node
(D) Faster convergence
6. : Which technique is commonly employed to address the challenge of load imbalance in distributed computing environments?
(A) Task replication
(B) Task migration
(C) Task termination
(D) Task serialization
7. : What is a key advantage of using a hybrid approach that combines both local and global search algorithms in parallel discrete optimization?
(A) Reduced parallelism
(B) Improved exploration-exploitation balance
(C) Higher communication overhead
(D) Faster convergence
8. : Which load balancing strategy aims to evenly distribute the computational load based on the historical performance of processors in discrete optimization?
(A) Static Load Balancing
(B) Dynamic Load Balancing
(C) Genetic Load Balancing
(D) Heuristic Load Balancing