An effective modified binary particle swarm optimization (mBPSO) algorithm for multi-objective resource allocation problem (MORAP)

A modified binary particle swarm optimization (mBPSO) algorithm is proposed for solving the multi-objective resource allocation problem (MORAP). First, the generation mechanism for initial particles is established to guarantee that the algorithm can begin to search optimal particle in the feasible solution space. Second, we develop the update mechanism for iterative particles which includes setting up the memory array, modifying Sig function and verifying the constraint condition to assure that the regenerated particles meet the constraint and algorithm can quickly converge. Third, the selection mechanism for pbesti and gbest is proposed which uses the dynamic neighborhood strategy to ensure that the algorithm to find Pareto optimal solutions. Through comparing the example simulation results of our mBPSO with hGA and ACO published in references, we find that proposed mBPSO outperforms hGA and ACO. Finally, the effectiveness of the different improved methods is analyzed, and the synergism effect and the convergence behavior of the mBPSO algorithm show its good performances.