Multi-objective particle swarm optimization with integrated fireworks algorithm and size double archiving

The multi-objective particle swarm optimization (MOPSO) is an optimization technique that mimics the foraging behavior of birds to solve difficult optimization problems. It is based on the theory of population intelligence.MOPSO is well known for its strong global search capability, which efficiently locates solutions that are close to the global optimum across a wide search domain. However, similar to many other optimization algorithms, the fast convergence property of MOPSO can occasionally lead to the population entering the local optimum too soon, obstructing researchers from investigating more efficient solutions. To address this challenge, the study proposes a novel framework that integrates the Fireworks Algorithm (FA) into MOPSO and establishes a size-double archiving mechanism to maintain population diversity. By preventing population homogenization, this mechanism promotes the retention of better solutions. Additionally, by fusing evolutionary data analysis with particle information, the study offers new individual optimal choices and adaptive parameter tuning to improve the algorithm's robustness and adaptability and better manage the complexity of multi-objective optimization problems (MOPs). The suggested algorithm is compared with several existing MOPSOs and multi-objective evolutionary algorithms (MOEAs) in simulation experiments. Standard test problems like ZDT, UF, and DTLZ are used in the experiments. The new algorithm performs exceptionally well in terms of improving convergence and population diversity, as well as demonstrating significant competitiveness for solving MOPs. Each algorithm's performance is thoroughly evaluated using the Friedman rank test.