Solving the 0/1 Knapsack Problem Using Metaheuristic and Neural Networks for the Virtual Machine Placement Process in Cloud Computing Environment

Table 2

Comparison of exploration and exploitation for knapsack problem methods.

Methods	Exploration	Exploitation
Genetic algorithm	Uses selection, crossover, and mutation to explore the search space	Focuses on refining existing solutions by selecting fittest individuals
Ant colony optimization	Uses pheromone trails to explore the search space	Increases pheromone level on good solutions to attract more ants
Simulated annealing	Accepts worse solutions with decreasing probability over time	Gradually converges towards the best solution found so far
Tabu search	Uses a tabu list to prevent revisiting explored solutions	Uses a neighborhood search to focus on improving current solution
Greedy search	Iteratively selects item with highest value-to-weight ratio	Adds item with highest value-to-weight ratio to the knapsack
Particle swarm optimization	Uses swarm of particles to explore search space	Adjusts particle position and velocity to improve current solution
Neural network	Uses training dataset to learn relationship between inputs and outputs	Adjusts weights of connections between neurons to minimize error