Novel Adaptive Simulated Annealing Algorithm for Constrained Multi-Objective Optimization

In recent years, simulated annealing algorithms have been extensively developed and utilized to solve multi-objective optimization problems. In order to obtain better optimization performance, this paper proposes a Novel Adaptive Simulated Annealing (NASA) algorithm for constrained multi-objective optimization based on Archived Multi-objective Simulated Annealing (AMOSA). For handling multi-objective, NASA makes improvements in three aspects: sub-iteration search, sub-archive and adaptive search, which effectively strengthen the stability and efficiency of the algorithm. For handling constraints, NASA introduces corresponding solution acceptance criterion. Furthermore, NASA has also been applied to optimize TD-LTE network performance by adjusting antenna parameters; it can achieve better extension and convergence than AMOSA, NS-GAII and MOPSO. Analytical studies and simulations indicate that the proposed NASA algorithm can play an important role in improving multi-objective optimization performance.