A dynamic screening algorithm for multiple objective simulated annealing optimization

This work proposes new multiple objective optimization (MOO) technology, using a Monte Carlo-based algorithm stemmed from simulated annealing (SA). Since the expected result in MOO tasks is usually a set of Pareto-optimal solutions, the optimization problem states assumed here are themselves sets of solutions. The stochastic search follows a series of reversible state transitions at constant probability, to enjoy convergence properties of stationary Markov processes. The proposed technology is tested against the optimal design of a process system involving equipment placed in a serial/parallel arrangement, with three optimization objectives: the system cost, reliability and weight.