A Multi-Objective Artificial Physics Optimization Algorithm Based on Two-Phase Search

Aiming at the problems of insufficient utilization of information about elite particles in archive and instability of particle motion in the population in the multi-objective artificial physics optimization algorithm (MOAPO) in solving multiobjective optimization problems, A multi-objective artificial physics optimization algorithm based on two-phase search (TPMOAPO) is proposed. To begin with, the algorithm improves the calculation of the mass of particles, so that the strength and weakness of the particles can be accurately transformed into the corresponding masses while improving the efficiency of particle mass calculation. Next, a two-phase search strategy is proposed, which makes the algorithm have strong exploration ability in the first phase, and the second phase gradually enhances the exploitation capability with iterations, which solves the problem of instability motion of particles in the search process. Finally, the simulated binary crossover (SBX) and polynomial-based mutation (PM) operators are adopted in the archive to further enhance the search capability of the algorithm. For verifying the performance of TP-MOAPO, 21 benchmark functions were selected to compare with the classical multi-objective particle swarm optimization algorithms: MOPSO, dMOPSO, SMPSO, MMOPSO, and NMPSO, and the experimental results show the superiority of TP-MOAPO in these functions.