Liver Cancer Algorithm: A novel bio-inspired optimizer

• Published in: Computers in biology and medicine Vol. 165; p. 107389
• Main Authors: Houssein, Essam H., Oliva, Diego, Samee, Nagwan Abdel, Mahmoud, Noha F., Emam, Marwa M.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2023; Elsevier Limited
• Subjects: Algorithms; Amine oxidase (flavin-containing); Benchmarks; Bio-inspired; Biomedical data; Coronaviruses; Datasets; Evolutionary algorithms; Evolutionary computation; Feature selection; Feature selection (FS); Genetic algorithms; Herd immunity; Heuristic methods; Internal Medicine; Liver cancer; Liver Cancer Algorithm (LCA); Metaheuristic algorithms (MAs); Operators (mathematics); Optimization; Optimization algorithms; Other; Particle swarm optimization; Random opposition-based learning (ROBL); Runge-Kutta method; Support vector machines; Tumors; Liver Cancer Algorithm (LCA); Bio-inspired; Random opposition-based learning (ROBL); Feature selection (FS); Metaheuristic algorithms (MAs); Optimization
• DOI: 10.1016/j.compbiomed.2023.107389
• ISSN: 0010-4825; 1879-0534

This paper introduces a new bio-inspired optimization algorithm named the Liver Cancer Algorithm (LCA), which mimics the liver tumor growth and takeover process. It uses an evolutionary search approach that simulates the behavior of liver tumors when taking over the liver organ. The tumor’s ability to replicate and spread to other organs inspires the algorithm. LCA algorithm is developed using genetic operators and a Random Opposition-Based Learning (ROBL) strategy to efficiently balance local and global searches and explore the search space. The algorithm’s efficiency is tested on the IEEE Congress of Evolutionary Computation in 2020 (CEC’2020) benchmark functions and compared to seven widely used metaheuristic algorithms, including Genetic Algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE), Adaptive Guided Differential Evolution Algorithm (AGDE), Improved Multi-Operator Differential Evolution (IMODE), Harris Hawks Optimization (HHO), Runge–Kutta Optimization Algorithm (RUN), weIghted meaN oF vectOrs (INFO), and Coronavirus Herd Immunity Optimizer (CHIO). The statistical results of the convergence curve, boxplot, parameter space, and qualitative metrics show that the LCA algorithm performs competitively compared to well-known algorithms. Moreover, the versatility of the LCA algorithm extends beyond mathematical benchmark problems. It was also successfully applied to tackle the feature selection problem and optimize the support vector machine for various biomedical data classifications, resulting in the creation of the LCA-SVM model. The LCA-SVM model was evaluated in a total of twelve datasets, among which the MonoAmine Oxidase (MAO) dataset stood out, showing the highest performance compared to the other datasets. In particular, the LCA-SVM model achieved an impressive accuracy of 98.704% on the MAO dataset. This outstanding result demonstrates the efficacy and potential of the LCA-SVM approach in handling complex datasets and producing highly accurate predictions. The experimental results indicate that the LCA algorithm surpasses other methods to solve mathematical benchmark problems and feature selection. •An efficient LCA algorithm is proposed based on Liver cancer tumor.•CEC’20 test suite is utilized for verification of LCA performance.•LCA is proposed for biomedical classification tasks.•LCA is analyzed using various analysis metrics.•The performance of the LCA is better than other competitor algorithms.