Limitation of Adapting to Continuously Changing Optimization Problem and Its Solution in Swarm Optimization

This study focuses on the dynamic optimization problems where a solution landscape "continuously" changes, unlike the conventional problems where a solution landscape "discretely" changes, which includes certain static (unchanged) periods before the landscape changes. Since such a problem becomes very difficult when the speed of changing the solution landscape is faster than the speed of evaluating all solutions, this paper proposes the swarm optimization method to tackle this problem. Concretely, our method excludes the landscape change detection mechanism unlike conventional approaches, but adapts to its change by exploring solutions according to the only current evaluation values. Through the experiment based on the high-frequency landscape change which is modeled by changing the multiple functions in the Moving Peaks Benchmark, the following implications have been revealed: (1) the proposed method outperforms the conventional methods even in the case of the high-frequency landscape change; and (2) not only the landscape change detection mechanism but also the past searched solution archive mechanism deteriorate the performance of the proposed method in the dynamic optimization problems based on the high-frequency landscape change.