A quasi-gradient and cluster-based artificial immune system for dynamic optimization

This paper presents an artificial immune system for solving dynamic optimization problems. For effectively solving optimization problems in dynamic environments, search population should be able to fast converge in each environment and redistribute when change occurs and subsequently track the change. This paper presents three modifications made to the basic artificial immune system to meet these requirements. The first one is the modified gradient-based mechanism called quasi-gradient which is beneficial to speed up the convergence of population. The second one is clustering strategy which is adopted to effectively distribute the population. Memory mechanism is the last one which takes advantage of the historical information from the last scenario preparing for the upcoming new environment. Applications on classic simple test-case generator and moving peak problem validate the performance of the proposed algorithm.