Orthogonal Dynamic Hill-Climbing Algorithm for Dynamic Optimization Problems

An orthogonal hill-climbing algorithm for dynamic optimization problems with continuous variables (labeled ODHC ) is proposed in present paper. The local peak climber is not a solution x, but rather a "niche", a small hyperrectangle. An orthogonal design method is employed on the niches for the niche to climb a potentially peak fast. An archive is used to store the latest found higher peaks for the ODHC algorithm learning from the past search. The randomly creating niches implement the global search. Numerical experiments show that the ODHC algorithm performs a lot better than the SOS (self organizing scouts) algorithm [J. Branke, T. Kaufler, C. Schmidt, and H. Schmeck. A multipopulation approach to dynamic optimization problems. Adaptive Computing in Design and Manufacturing. Springer, 2000.].