Multi-objective evolutionary algorithms for MILP and MINLP in process synthesis

Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady- state idea in steady-state genetic algorithms (SSGA) and the fitness assign ment strategy of non-dominated sorting genetic algorithm (NSGA). The fitnes s assignment strategy is improved and a new self-adjustment scheme of sigma (share) is proposed. This algorithm is proved to be very efficient both co mputationally and in terms of the quality of the Pareto fronts produced wit h five test problems including GA difficult problem and GA deceptive one. F inally, SNSGA is introduced to solve multi-objective mixed integer linear p rogramming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.