New Multi-Objective Optimization Techniques and Their Application to Complex Chemical Engineering Problems

Abstract

In this paper, an objective-based gradient multi-objective optimization (MOO) technique, the Objective-Based Gradient Algorithm (OBGA), is proposed with the goal of defining the Pareto domain more accurately and efficiently than current MOO techniques. The performance of the OBGA in locating the Pareto domain was evaluated in terms of accuracy, computation time and number of objective function calls, and compared to two current MOO algorithms: Dual Population Evolutionary Algorithm (DPEA) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), using four test problems. For all test problems, the OBGA systematically produced a more accurate Pareto domain than DPEA and NSGA-II. With the adequate selection of the OBGA parameters, computation time required for the OBGA can be lower than that required for DPEA and NSGA-II. Results clearly show that the OBGA is a very effective and efficient algorithm for locating the Pareto domain.