Abstract
This paper develops the particle swarm optimization (PSO) method and uses a genetic algorithm (GA) by changing the distribution of articles in the initialization of the initial position. PSO at this time the search and speed of particles will always go to the best solution so that by narrowing the search area will be faster by updating the best position of PSO. While the Genetic algorithm plays a role to get an expanded search area for PSO solutions by utilizing crossover and mutation in GA. So that GA will expand the range of candidates for the best solution in PSO. From each of the advantages of PSO Update and GA will be combined to get Evolutionary PSO-GA (EVPGA) that can minimize error and speed up computation (itation) in finding the best solution. By using the Multidimensional Knapsack data set, the results of EVPGA get an average speed of 24.9s with an error of 1.49%.
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