Evolutionary and adaptive inheritance enhanced Grey Wolf Optimization algorithm for binary domains

Abstract

This paper introduces a new binary Grey Wolf Optimization (GWO) algorithm, which is one of the recent swarm intelligence-based metaheuristic algorithms. Its various extensions have been reported in the related literature. Despite numerous successful applications in real-valued optimization problems, the canonical GWO algorithm cannot directly handle binary optimization problems. To this end, transformation functions are commonly employed to map the real-valued solution vector to the binary values; however, this approach brings about the undesired problem of spatial disconnect. In this study, evolutionary and adaptive inheritance mechanisms are employed in the GWO algorithm so as to operate in the binary domain directly. To mimic the leadership hierarchy procedure of the GWO, multi-parent crossover with two different dominance strategies is developed while updating the binary coordinates of the wolf pack. Furthermore, adaptive mutation with exponentially decreasing step-size is adopted to avoid premature convergence and to establish a balance between intensification and diversification. The performance of the proposed algorithm is tested on the well-known binary benchmark suites comprised of the Set-Union Knapsack Problem (SUKP) that extends the 0–1 Knapsack Problem and the Uncapacitated Facility Location Problem (UFLP). Comprehensive experimental study including real-life applications and statistical analyses demonstrate the effectiveness of the proposed algorithm.