Improving System Architectures Using Multi‐Objective Particle Swarm Optimization

ABSTRACT Architectural definition of complex systems requires analyzing competing objectives and constraints in high‐dimensional problems, due to the multitude of design choices. There are many optimization approaches that could be used for this process. Common approaches in the systems engineering literature are genetic algorithms (GA) and a multi‐objective extension, non‐dominated sorting genetic algorithm II (NSGA‐II). Simpler alternatives exist, potentially without sacrificing optimization performance. In this paper, simpler metaheuristic algorithms, specifically particle swarm optimization (PSO) and its multi‐objective extension MOPSO, were evaluated and compared with the common GA methods across single‐ and multi‐objective optimization (SOO, MOO) problems. The GA approach was compared with PSO for SOO and NSGA‐II with MOPSO for MOO. Results show that performance (lowest minima) for both algorithms in the SOO case was similar (H 0 rejected in 3 of 5 cases), suggesting that the simpler PSO approach may offer implementation and computational resource advantages. The simpler MOPSO algorithm showed similar performance to the NSGA‐II algorithm when comparing search space coverage via hypervolume (H 0 rejected in 2 of 5 cases) and via ε + ‐indication (H 0 rejected in 3 of 5 cases). An example of MOPSO and NSGA‐II applied to architecture definition is provided as a case study from the research literature. These findings suggest that PSO and MOPSO may have advantages relative to the GA and NSGA‐II approaches in terms of simplicity with similar performance. MOPSO could lead to improved systems engineering via simpler optimization, more complete architectural analysis, and improved understanding of the viable design options during architecture definition.