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

Kanak Kalita1,2, Janjhyam Venkata Naga Ramesh3, Robert Čep4

  • 1Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, 600 062, India.

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Summary
This summary is machine-generated.

This study introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), inspired by tumor growth, for engineering design optimization. MOLCA enhances search capabilities and efficiently finds optimal solutions, outperforming existing algorithms on various benchmarks.

Keywords:
Engineering design optimizationLiver cancer algorithmMOLCAMulti objective optimizationNon-dominated solutionPareto frontPareto solution

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Area of Science:

  • Computational Intelligence
  • Bio-inspired Computing
  • Engineering Optimization

Background:

  • Multi-objective optimization problems (MOPs) are prevalent in engineering design.
  • Existing algorithms face challenges in balancing exploration and exploitation for Pareto front identification.
  • Novel bio-inspired approaches are needed to address complex MOPs.

Purpose of the Study:

  • Introduce the Multi-Objective Liver Cancer Algorithm (MOLCA) for solving MOPs.
  • Emulate liver tumor growth dynamics for enhanced optimization.
  • Improve convergence and distribution of solutions on the Pareto optimal front.

Main Methods:

  • MOLCA combines genetic operators with Random Opposition-Based Learning (ROBL).
  • Integrates elitist non-dominated sorting (NDS), information feedback mechanism (IFM), and Crowding Distance (CD) selection.
  • Evaluated on ZDT, DTLZ, Constraint (CONSTR, TNK, SRN, BNH, OSY, KITA), and real-world engineering problems.

Main Results:

  • MOLCA demonstrated competitive performance against NSGWO, MOMVO, NSGA-II, MOEA/D, and MOMPA.
  • Quantitative metrics (GD, IGD, SP, SD, HV, RT) show MOLCA's effectiveness in convergence and distribution.
  • Qualitative analysis via Pareto front plots visually confirms MOLCA's solution quality.

Conclusions:

  • MOLCA offers a novel and effective bio-inspired approach for multi-objective engineering optimization.
  • The algorithm shows superior performance in identifying the Pareto optimal front.
  • MOLCA provides a valuable tool for tackling complex engineering design challenges.