Multiagent-Based Evolutionary Algorithm for Dynamic Knapsack Problem

A multiagent-based evolutionary algorithm(MAEA) is proposed to solve the dynamic knapsack problem,where the agents are co-evolving to simulate living organism features so as to find optimum solution.Existing in a grid-like environment,all agents shall compete with their neighborhood to enhance their energy and acquire knowledge through learning in according to statistical data.And the random immigration scheme is introduced into the algorithm to maintain the diversity of the population.Simulation results showed that the MAEA solution with random immigration scheme to dynamic knapsack problem can obtain a better performance than several other similiar genetic algorithms in according to the average-ofgeneration fitness taken as evaluation indices.