| Abstrakt: |
Researchers from different domains have developed several metaheuristic algorithms that are inspired by the biological phenomenon. In this work, we propose a novel algorithm that is inspired by the prey–predator interaction of animals. In the proposed algorithm, two random populations are considered as a predator and preys. The energy gain for both predators and preys calculated based on their body mass and the interaction between predators and their mutual preys. The best predator (i.e., predator with the highest energy gain) performs a local search (exploitation). The other solution members in prey population facilitate the exploration of the search space. The proposed algorithm is evaluated by three phases. Firstly, a set of 16 mathematical functions are considered to test different characteristics of predator–prey optimization (PPO) algorithm as a optimizer. Secondly, it is evaluated by seven datasets as a solver for feature selection problem. The proposed algorithm as an global optimizer has been compared with other metaheuristic algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), ant lion optimizer (ALO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), differential evolution (DE), sine–cosine algorithm (SCA), moth flame optimization (MFO), and salp swarm algorithm (SSA). The results indicate that PPO improves convergence rate by 33.8%, 24.2%, 37.3%, 10.5%, 12.4%, 22.7%, 18.6%, 30.7%, and 29.1% in average compared to GA, PSO, ALO, GWO, WOA, DE, SCA, MFO, and SSA, respectively. Furthermore, PPO obtains a higher Performance Index around 5–10% than competitor approaches with respect to exploration–exploitation balance and a high degree of stability. Secondly, the performance of PPO for feature selection issues demonstrates the applicability PPO algorithm in solving real problems with unknown search spaces. [ABSTRACT FROM AUTHOR] |
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