A Novel Approach for Optimization in Dynamic Environments Based on Modified Artificial Fish Swarm Algorithm

Swarm intelligence algorithms are amongst the most e±cient approaches toward solving optimization problems. Up to now, most of swarm intelligence approaches have been proposed for optimization in static environments. However, numerous real-world problems are dynamic which could not be solved using static approaches. In this paper, a novel approach based on arti ̄cial ̄sh swarm algorithm (AFSA) has been proposed for optimization in dynamic environments in which changes in the problem space occur in discrete intervals. The proposed algorithm can quickly ̄nd the peaks in the problem space and track them after an environment change. In this algorithm, arti ̄cial ̄sh swarms are responsible for ̄nding and tracking peaks and several behaviors and mechanisms are employed to cope with the dynamic environment.