Activation Function-Assisted Objective Space Mapping to Enhance Evolutionary Algorithms for Large-Scale Many-Objective Optimization

Large-scale many-objective optimization problems (LSMaOPs) pose great difficulties for traditional evolutionary algorithms due to their slow search for Pareto-optimal solutions in huge decision space and struggle to balance diversity and convergence among numerous locally optimal solutions. An objective space linear inverse mapping method has successfully achieved great saving in execution time in solving LSMaOPs. Linear mapping is a fast and straightforward way, but fails to characterize a complex functional relationship. If we can enhance the expressive capacity of a mapping model, and further obtain a more general function approximator, can the evolutionary search based on objective space mapping be more efficient? To answer this interesting question, this work proposes to employ nonlinear activation functions widely used in neural networks so as to enhance the efficiency of objective space inverse mapping, thus efficiently generating excellent offspring population. A new evolutionary optimization framework based on decision variable analysis is proposed to solve LSMaOPs. In order to demonstrate its performance, this work carries out empirical experiments involving massive decision variables and many objectives. Experimental results prove its superiority over some representative and updated ones.