A new and efficient multi-objective particle swarm optimization (MOPSO) algorithm based on invasive weed cloning

When the existing MOPSO algorithm is applied to optimizing the functions with the discontinuous Pareto front, its convergence and the diversity of its population are poor. To solve the problem, we propose our new IW-MOPSO (Invasive Weed MOPSO) algorithm, which we believe is more efficient than existing ones. Sections 1 through 2 of the full paper explain our new IWMOPSO algorithm. Section 1 presents the defects of the MOPSO algorithm. Section 2 explains how to reduce such defects to a minimum. Section 3 uses five benchmark test functions to compare the performance of our new IWMOPSO algorithm with those of the existing MOPSO and NSGA-II algorithms. The test results, given in Tables 1 and 2 and Fig. 7, and their analysis show preliminarily that both the convergence of our IWMOPSO algorithm and its diversity are enhanced by the improved file maintenance strategy and the unfeasible solutions, with the Pareto front obtained with our new algorithm very close to the real Pareto front, thus being more efficient than both the MOPSO and NSGA-II algorithms.