An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer Problem

Abstract

Artificial rabbits optimization (ARO) is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature. However, for solving optimization problems, the ARO algorithm shows slow convergence speed and can fall into local minima. To overcome these drawbacks, this paper proposes chaotic opposition-based learning ARO (COARO), an improved version of the ARO algorithm that incorporates opposition-based learning (OBL) and chaotic local search (CLS) techniques. By adding OBL to ARO, the convergence speed of the algorithm increases and it explores the search space better. Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently, since their ergodicity and non-repetitive properties. The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions. The outcomes have been compared with the most recent optimization algorithms. Additionally, the COARO algorithm's problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms. This study also introduces a binary variant of the continuous COARO algorithm, named BCOARO. The performance of BCOARO was evaluated on the breast cancer dataset. The effectiveness of BCOARO has been compared with different feature selection algorithms. The proposed BCOARO outperforms alternative algorithms, according to the findings obtained for real applications in terms of accuracy performance, and fitness value. Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.