Analysis of Marine Predators Algorithm using BIAS toolbox and Generalized Signature Test

Abstract

The Marine Predators Algorithm (MPA) is a prominent Nature-Inspired Optimization Algorithm (NIOA) that has garnered significant research interest due to its effectiveness. It draws inspiration from the foraging behaviors of marine predators, predominantly using the Lévy or Brownian approach for its foraging strategy. Despite its acclaim, the structural bias within MPA has not been thoroughly investigated, marking a significant gap in the current research. This absence of targeted research forms the core rationale behind initiating this study. Structural bias has recently been identified in NIOAs, causing the population to revisit specific regions of the search space without gaining new information. As a result, it may lead to increased computational costs and slow down the rate of convergence. Therefore, identifying structural bias is essential to better understand the search mechanism of MPA. To ascertain the presence of any structural bias, two recently introduced models are employed: the BIAS toolbox and the Generalized Signature Test. These examinations reveal a notable structural bias in MPA, predominantly towards the center of the search space. Also, possible future research directions for MPA are discussed. Our findings provide valuable insights into the search dynamics of the algorithm, fostering the development of new, unbiased, and efficient algorithms.