metaQ01

What is the primary challenge of solving NP-hard problems?

• There is no known polynomial-time algorithm to solve them optimally
• They cannot be solved using heuristics
• They require infinite memory
• They are only applicable to small datasets

What distinguishes a metaheuristic algorithm from a traditional heuristic?

• Metaheuristics provide a general framework for solving different optimization problems, whereas heuristics are problem-specific
• Metaheuristics always find the global optimum
• Heuristics require more computational power than metaheuristics
• Heuristics use randomness, while metaheuristics do not

What is the main advantage of using a variable neighborhood search (VNS)?

• It systematically changes the neighborhood structure to escape local optima
• It only explores a single fixed neighborhood
• It always finds an exact solution in polynomial time
• It does not require any initial solution

Which metaheuristic algorithm is inspired by the process of metal cooling?

• Genetic Algorithm
• Simulated Annealing
• Particle Swarm Optimization
• Tabu Search

In a Genetic Algorithm, what is the purpose of selection?

• To choose the best individuals for reproduction based on fitness
• To introduce random variations in the population
• To combine characteristics from two parent solutions
• To eliminate solutions that do not meet constraints

What is the primary purpose of the fitness function in optimization algorithms?

• To evaluate how good a solution is relative to others
• To generate new candidate solutions
• To guarantee finding the global optimum
• To eliminate the need for mutation in genetic algorithms

Which optimization technique is based on principles of natural evolution?

• Simulated Annealing
• Genetic Algorithms
• Tabu Search
• Particle Swarm Optimization

In metaheuristics, what is the main purpose of diversification?

• To explore new areas in the solution space and avoid premature convergence
• To exploit the best solution found so far
• To speed up the search process by focusing only on a small area
• To eliminate the need for local search

What distinguishes Ant Colony Optimization (ACO) from Genetic Algorithms (GA)?

• ACO is inspired by how ants find food, while GA is inspired by natural selection
• ACO does not use any probabilistic mechanisms
• GA does not use a population of candidate solutions
• GA is primarily used for discrete optimization, while ACO is not

What is the main purpose of intensification in metaheuristic algorithms?

• To focus the search on the most promising areas of the solution space
• To explore new random solutions
• To restart the search from scratch
• To eliminate all solutions that do not meet a predefined criterion