A Modified Particle Swarm Optimization Based on Eagle Foraging Behavior

Abstract

Inspired by the eagle’s four distinctive foraging behaviors, namely searching, exploring, striking, and killing, we present a new variant of Particle Swarm Optimization (PSO), which is called the Eagle-Foraging Particle Swarm Optimization Algorithm (EFPSO). The EFPSO hybridizes the PSO algorithm, a space identification scheme, and a local search technique. We chose nine benchmark problems, each with different functional characteristics, to validate the EFPSO performance. First, we solve these problems with 10 and 30 dimensions. The results show that the EFPSO can find the solution that is closest to each problem’s exact solution. Second, we perform EFPSO for several highly challenging functions with dimensions up to 500. Finally, we use the EFPSO to design a cantilevered beam with 10 variables to minimize the volume of material required to construct the beam.