Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework

This study introduces the Borg multiobjective evolutionary algorithm (MOEA) for many-objective, multimodal optimization. The Borg MOEA combines ǫ-dominance, a measure of convergence speed named ǫ-progress, randomized restarts and auto-adaptive multioperator recombination into a unified optimization framework. A com-parative study on 33 instances of 18 test problems from the DTLZ,WFG, and CEC 2009 test suites demonstrates Borg meets or exceeds 6 state-of-the-art MOEAs on the major-ity of the tested problems. Performance for each test problem is evaluated using a 1000 point Latin hypercube sampling of each algorithm’s feasible parameterization space. The statistical performance of every sampledMOEA parameterization is evaluated us-ing 50 replicate random seed trials. The Borg MOEA is not a single algorithm; instead it represents a class of algorithms whose operators are adaptively selected based on the problem. The adaptive discovery of key operators is of particular importance for benchmarking how variation operators enhance search for complex many-objective problems