Enhanced Innovized Repair Operator for Evolutionary Multi- and Many-objective Optimization
This work offers an incremental improvement in convergence for evolutionary multi- and many-objective optimization algorithms by leveraging historical solution data.
This paper introduces an innovized repair operator (IR2-RF) that uses a random forest model to learn modifications in design variables, improving the convergence of multi- and many-objective optimization algorithms. The operator, integrated with NSGA-II, NSGA-III, and MOEA/D, demonstrated improved convergence on test problems with two to five objectives.
"Innovization" is a task of learning common relationships among some or all of the Pareto-optimal (PO) solutions in multi- and many-objective optimization problems. Recent studies have shown that a chronological sequence of non-dominated solutions obtained in consecutive iterations during an optimization run also possess salient patterns that can be used to learn problem features to help create new and improved solutions. In this paper, we propose a machine-learning- (ML-) assisted modelling approach that learns the modifications in design variables needed to advance population members towards the Pareto-optimal set. We then propose to use the resulting ML model as an additional innovized repair (IR2) operator to be applied on offspring solutions created by the usual genetic operators, as a novel mean of improving their convergence properties. In this paper, the well-known random forest (RF) method is used as the ML model and is integrated with various evolutionary multi- and many-objective optimization algorithms, including NSGA-II, NSGA-III, and MOEA/D. On several test problems ranging from two to five objectives, we demonstrate improvement in convergence behaviour using the proposed IR2-RF operator. Since the operator does not demand any additional solution evaluations, instead using the history of gradual and progressive improvements in solutions over generations, the proposed ML-based optimization opens up a new direction of optimization algorithm development with advances in AI and ML approaches.