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MOSRS: An engineering multi-objective optimization through Einsteinian concept.

Vahid Goodarzimehr1, João Luiz Junho Pereira2, Nima Khodadadi3

  • 1Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

Plos One
|July 29, 2025
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Summary
This summary is machine-generated.

Introducing the Multi-objective Special Relativity Search (MOSRS), a novel metaheuristic algorithm. MOSRS efficiently finds optimal Pareto Fronts (PFs) for complex problems with minimal user input and lower computational cost.

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Area of Science:

  • * Computational intelligence and mathematical optimization.
  • * Application of physics principles to algorithm design.

Background:

  • * Metaheuristics are vital for solving complex multi-objective optimization problems.
  • * Existing algorithms often require extensive parameter tuning and struggle with convergence and coverage.
  • * There is a need for efficient algorithms that deliver superior Pareto Fronts (PFs) at reduced computational expense.

Purpose of the Study:

  • * To introduce the novel Multi-objective Special Relativity Search (MOSRS) algorithm.
  • * To demonstrate MOSRS's ability to self-adapt parameters based on special relativity principles.
  • * To evaluate MOSRS's performance against established metaheuristics on challenging benchmark and real-world problems.

Main Methods:

  • * Development of the MOSRS algorithm inspired by special relativity physics.
  • * Application of MOSRS to the CEC 2009 test functions and 21 real-world constrained problems.
  • * Comparative analysis using performance indicators: Inverted Generational Distance, Spacing, Maximum Spread, and Hypervolume.
  • * Benchmarking against eleven state-of-the-art metaheuristics (NSGA-II, NSGA-III, MOEA/D, MOPSO, MOGWO, ARMOEA, TiGE2, CCMO, ToP, AnD).

Main Results:

  • * MOSRS demonstrated robust performance in identifying optimal Pareto Fronts (PFs) across a majority of the tested problems.
  • * The algorithm achieved competitive or superior results compared to eleven other metaheuristics.
  • * MOSRS requires minimal user intervention, primarily needing only the number of iterations and particles.

Conclusions:

  • * MOSRS is a highly effective and efficient metaheuristic for multi-objective optimization.
  • * The algorithm's self-adaptive parameter tuning based on special relativity principles contributes to its performance.
  • * MOSRS offers a promising alternative for researchers and practitioners seeking advanced optimization solutions with reduced complexity.