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Evolutionary Algorithms, Active Elitism, Multi-objective Optimization

Classical (or passive) elitism mechanisms in the literature have a holding/sending back structure. In the active elitism mechanism proposed in this paper, a set of elite individuals is excited by genetic operators (crossover/mutation) in archive in the hope of generating better and more diverse individuals than themselves. If a set of excited elites are any better than originals, then archive can be viewed as a place of active solution provider rather than a static storage place. The main motivation behind this approach is that elite individuals are inherently the closest individuals to the solution (of any optimization problem on hand) and exciting those individuals can likely generate more significant outcomes than a far away one. The proposed active elitism mechanism is embedded into well-known multi-objective SPEA and SPEA2 methods (named ACE_SPEA and ACE_SPEA2 respectively) and compared to the original methods using four benchmarks. The active elitist versions of SPEA and SPEA2 maintain better spread and convergence properties than the original methods. The proposed active elitism mechanism can easily be integrated into existing multi-objective evolutionary algorithms.