Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

Kanak Kalita; Janjhyam Venkata Naga Ramesh; Robert Čep; Sundaram B Pandya; Pradeep Jangir; Laith Abualigah

doi:10.1016/j.heliyon.2024.e26665

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

Heliyon. 2024 Mar 2;10(5):e26665. doi: 10.1016/j.heliyon.2024.e26665. eCollection 2024 Mar 15.

Authors

Kanak Kalita^{1

2}, Janjhyam Venkata Naga Ramesh³, Robert Čep⁴, Sundaram B Pandya⁵, Pradeep Jangir⁶, Laith Abualigah^{7

8

9

10

11

12

13

14}

Affiliations

¹ Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, 600 062, India.
² University Centre for Research & Development, Chandigarh University, Mohali, 140413, India.
³ Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, 522502, India.
⁴ Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70800, Ostrava, Czech Republic.
⁵ Department of Electrical Engineering, Shri K.J. Polytechnic, Bharuch, 392 001, India.
⁶ Department of Biosciences, Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences, Chennai, 602 105, India.
⁷ Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan.
⁸ Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan.
⁹ MEU Research Unit, Middle East University, Amman, 11831, Jordan.
¹⁰ Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon.
¹¹ School of Computer Sciences, Universiti Sains Malaysia, Pulau, Pinang, 11800, Malaysia.
¹² School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya, 27500, Malaysia.
¹³ Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan.
¹⁴ Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia.

Abstract

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

Keywords: Engineering design optimization; Liver cancer algorithm; MOLCA; Multi objective optimization; Non-dominated solution; Pareto front; Pareto solution.