Sandpiper Food Search Algorithm: A New Optimization Approach for Agents with Limited Knowledge
Abstract
Optimization problems in mechanical engineering drive advancements in system designs, performance enhancement, and maximizing efficiency across various applications. While conventional methods face limitations with increasingly complex problems, metaheuristic algorithms inspired by nature offer promising solutions. However, many existing algorithms such as the Firefly Algorithm, Particle Swarm Optimization, Generic Algorithm, Bath Algorithm, and Cuckoo Search lack realism in handling localized knowledge, crucial for certain real-world complex systems such as underground mining and spacecraft trajectory. To bridge this gap, we introduce the Sandpiper Food Search Algorithm, inspired by sandpipers' foraging behaviour 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 solution to increase exploration of the solution space. Our evaluation was performed using four standard benchmark functions in comparison with the Firefly Algorithm as it shares similar parameterization characteristics, and its use of decreasing light brightness with distance between fireflies mirrors the limitation of knowledge imposed by the visibility radius in sandpipers. Our research reveals that the Sandpiper Food Search Algorithm has outperformed the Firefly Algorithm in three out of the four functions with at least 3% improvement in mean best solution and on average 38% more reliable at finding a solution at least 95% of the optimal.
Sandpiper Food Search Algorithm: A New Optimization Approach for Agents with Limited Knowledge
Optimization problems in mechanical engineering drive advancements in system designs, performance enhancement, and maximizing efficiency across various applications. While conventional methods face limitations with increasingly complex problems, metaheuristic algorithms inspired by nature offer promising solutions. However, many existing algorithms such as the Firefly Algorithm, Particle Swarm Optimization, Generic Algorithm, Bath Algorithm, and Cuckoo Search lack realism in handling localized knowledge, crucial for certain real-world complex systems such as underground mining and spacecraft trajectory. To bridge this gap, we introduce the Sandpiper Food Search Algorithm, inspired by sandpipers' foraging behaviour 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 solution to increase exploration of the solution space. Our evaluation was performed using four standard benchmark functions in comparison with the Firefly Algorithm as it shares similar parameterization characteristics, and its use of decreasing light brightness with distance between fireflies mirrors the limitation of knowledge imposed by the visibility radius in sandpipers. Our research reveals that the Sandpiper Food Search Algorithm has outperformed the Firefly Algorithm in three out of the four functions with at least 3% improvement in mean best solution and on average 38% more reliable at finding a solution at least 95% of the optimal.