HEMO: A Sustainable Multi-objective Evolutionary Optimization Framework

The capability of multi-objective evolutionary algorithms (MOEAs) to handle premature convergence is critically important when applied to realworld problems. Their highly multi-modal and discrete search space often makes the required performance out of reach to current MOEAs. Examining the fundamental cause of premature convergence in evolutionary search has led to proposing of a generic framework, named Hierarchical Fair Competition (HFC), for robust and sustainable evolutionary search. Here an HFC-based Hierarchical Evolutionary Multi-objective Optimization framework (HEMO) is proposed, which is characterized by its simultaneous maintenance of individuals of all degrees of evolution in hierarchically organized repositories, by its continuous inflow of random individuals at the base repository, by its intrinsic hierarchical elitism and hyper-grid-based density estimation. Two experiments demonstrate its search robustness and its capability to provide sustainable evolutionary search for difficult multi-modal problems. HEMO makes it possible to do reliable multi-objective search without risk of premature convergence. The paradigmatic transition of HEMO to handle premature convergence is that instead of trying to escape local optima from converged high fitness populations, it tries to maintain the opportunity for new optima to emerge from the bottom up as enabled by its hierarchical organization of individuals of different fitnesses.