Dynamic optimization in binary search spaces via weighted superposition attraction algorithm

Optimization in dynamic environments is a fast developing research area. Several outstanding metaheuristic algorithms were proposed to solve dynamic optimization problems (DOPs) in the past decade. However, most of the effort is devoted to real-valued DOPs. Although, great majority of real-life problems has discrete and binary spaces, research in binary DOPs is still lacking. Accordingly, the present study introduces the first binary DOP application of Weighted Superposition Attraction Algorithm (WSA), which is a new generation swarm intelligence-based metaheuristic algorithm. As a distinctive feature from the existing literature, the introduced binary version of WSA (bWSA) does not require transfer functions for converting floating numbers to binary, whereas they are commonly employed in binary modifications of various metaheuristic algorithms. Additionally, some new extensions of bWSA are also developed in the present study. For comparative analysis, first, some state-of-the-art algorithms including Particle Swarm Optimization and Genetic Algorithm are adopted. As secondarily, another new-generation hot optimizer, namely, Firefly Algorithm (FA), which has already been shown to be quite promising in DOPs, is employed in the present work. Moreover, all algorithms implemented here are enhanced by using dualism-based search, triggered random immigrants and adaptive hill climbing strategies. Dynamic modifications of the well-known binary benchmarking problems such as One-Max, Plateau, Royal Road and Deceptive Functions are used in the computational study. Performances of the proposed algorithms are compared in detail. Finally, non-parametric statistical tests are employed to validate the results. Findings point out superiority of bWSA in binary DOPs.