A Constrained Multi/Many-Objective Particle Swarm Optimization Algorithm With a Two-Level Balance Scheme

Constrained multi-objective optimization problems are common in practical engineering and more difficult to handle than unconstrained problems. In general, it is necessary find a balance between the convergence diversity of solutions as well feasibility. For constrained multi/many-objective problem, particle swarm algorithm based on two-level strategy proposed. contrast existing views, first level proposed algorithmic framework emphasizes convergence, while feasibility considered together second-level scheme. An ensemble fitness ranking was used improve algorithm. To solution feasibility, selected by combining angles using constraint dominance principle. A penalty-based boundary-crossing approach utility function calculate populations, which compared with six state-of-the-art evolutionary algorithms multiple test suites, experimental results show that highly competitive most Furthermore, illustrate effect different functions performance algorithm, Chebyshev decomposition method employed former, need be chosen cope characteristics.