Optimization problems have become increasingly complex, stretching the limits of conventional methods such as convex optimization, Newton-Raphson method, and others. To address this challenge, numerou..
Optimization problems have become increasingly complex, stretching the limits of conventional methods such as convex optimization, Newton-Raphson method, and others. To address this challenge, numerous metaheuristic algorithms, often inspired by nature, have emerged due to their adaptability and robustness (for example, Gray Wolf Algorithm and Ant Colony Optimization). However, functions that are non-symmetrical or have unique profiles such as basins, valleys, or plates remain challenging for these algorithms. Moreover, most of these existing algorithms have global knowledge, which is unrealistic for some real-world problems such as underground mining and spacecraft trajectory. To bridge this gap, a new biologically inspired optimization algorithm named Sandpiper Food Search Algorithm (SFSA) is proposed in this research. This algorithm is inspired by the food search behavior of sandpipers at the beach where each agent (sandpiper) explores the problem space to find the optimal area by exploiting the local search for candidate solutions around them. Moreover, this algorithm includes the wave action that forces these birds to shift from their current best position and how they would find their way back to their best position if their shift does not give a better solution. The waves’ occurrence will be based on a Poisson distribution with size of 60% of the search space. The performance of the algorithm is evaluated using the Holder Table benchmark function. This research provides a conceptual design of the new Sandpiper Food Search Algorithm and an initial evidence of the accuracy of the algorithm.
