Enhancing Fireworks Algorithms for Dynamic Optimization Problems

Dynamic optimization problems have been captivating the interest of the researchers, since most real world problems in different domains have various characteristics of dynamism. Different evolutionary and swarm intelligence techniques are proposed to solve dynamic optimization problems. Fireworks Algorithm (FWA) is a recently proposed swarm intelligence algorithm for global optimization of complex functions. This paper proposes two extensions on the FWA, called the EFWA_D1 and the EFWA_D2 algorithms, in order to adapt on dynamic optimization problems. We validate performance of the EFWA_D1 and the EFWA_D2 with the Moving Peaks Benchmark (MPB), a well-known synthetic dynamic optimization problem that generates and updates a multidimensional landscape consisting of several peaks. Experimental evaluation on various instances of MPB validates the applicability of our extensions on the FWA for a dynamic optimization problem