Accelerating Fireworks Algorithm with Adaptive Scouting Strategy

We propose an adaptive scouting strategy which is a refinement of our previous work to further improve the performance of the fireworks algorithm (FWA). The proposed strategy makes full use of the currently obtained fitness landscape information to avoid inefficient searches. Specifically, we introduce two new modifications to our previously proposed scouting strategy to more quickly adjust the balance between exploration and exploitation in the face of various optimization scenarios. The first modification is that the initial explosion center migrates with better generated spark individual instead of being fixed on initial firework individual, i.e., the next round of the initial explosion center will move to the recently generated spark individual when the current tracing direction has no potential. The other is to actively reduce the explosion amplitude of subsequent explosion operation when a potential spark individual is generated. Otherwise, increase the explosion amplitude to escape from the trapped local area quickly. To evaluate the performance of the new proposed strategy, we designed a series of comparative experiments and used 28 functions from the CEC 2013 test suite as the benchmark. The experimental results confirmed that the adaptive scouting strategy shows better performance and faster convergence speed especially for complex multimodal optimization problems.