A problem transformation-based and decomposition-based evolutionary algorithm for large-scale multiobjective optimization

Abstract

For large-scale multi-objective optimization problems, the search space becomes exceptionally vast, resulting in increased complexity in the search process. The search space usually contains several local optimal individuals, and the difficulty of finding the global optimal individuals increases greatly. A problem transformation-based and decomposition-based large-scale multi-objective evolutionary algorithm is proposed to solve the problem of reducing the dimensionality of the search space and the optimization of populations. Reducing the number of optimization problems in the decision space through problem transformation has lowered the complexity of multi-objective optimization problems and enhanced computational efficiency. In the decision space, employing two direction vectors adaptively guides the generation of promising individuals, thereby preventing the population from falling into local optima. Due to the dimensionality reduction of the decision space and the optimization of the individuals in the objective space, a set of optimal solutions are effectively obtained and uniformly distributed on the approximate Pareto optimal front. Experimental results show that the algorithm is highly competitive with five large-scale multi-objective evolutionary algorithms for large-scale multi-objective optimization test problems with up to 2000 decision variables.