Wolf search algorithm with ephemeral memory

In computer science, a computational challenge exists in finding a globally optimized solution from a tremendously large search space. Heuristic optimization methods have therefore been created that can search the very large spaces of candidate solutions. These methods have been extensively studied in the past, and progressively extended in order to suit a wide range of optimization problems. Researchers recently have invented a collection of heuristic optimization methods inspired by the movements of animals and insects (e.g., Firefly, Cuckoos, Bats and Accelerated PSO) with the advantages of efficient computation and easy implementation. This paper proposes a new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA) that imitates the way wolves search for food and survive by avoiding their enemies. The contribution of the paper is twofold: 1. for verifying the efficacy of the WSA the algorithm is tested quantitatively and compared to other heuristic algorithms under a range of popular non-convex functions used as performance test problems for optimization algorithms; 2. The WSA is investigated with respective to its memory requirement. Superior results are observed in most tests.