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Many heavier elements with smaller binding energies per nucleon can decompose into more stable elements that have intermediate mass numbers and larger binding energies per nucleon—that is, mass numbers and binding energies per nucleon that are closer to the “peak” of the binding energy graph near 56. Sometimes neutrons are also produced. This decomposition of a large nucleus into smaller pieces is called fission. The breaking is rather random with the formation of a large...
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The vacuum level denotes the energy threshold required for an electron to escape from a material surface. It is usually positioned above the conduction band of a semiconductor and acts as a benchmark for comparing electron energies within various materials.
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The fusion-fission optimization (FuFiO) algorithm.

Behnaz Nouhi1, Nima Darabi2, Pooya Sareh3

  • 1Department of Mathematical Sciences, University of Tabriz, Tabriz, Iran.

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|July 20, 2022
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Summary
This summary is machine-generated.

A new metaheuristic algorithm, Fusion-Fission Optimization (FuFiO), simulates nuclear stabilization processes. FuFiO demonstrates superior performance compared to other algorithms on benchmark test functions.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Nuclear Physics Analogy

Background:

  • Metaheuristic algorithms are crucial for solving complex optimization problems.
  • Existing algorithms often face challenges with convergence and solution quality.
  • A novel approach inspired by nuclear physics can offer new optimization strategies.

Purpose of the Study:

  • To introduce a new metaheuristic algorithm named Fusion-Fission Optimization (FuFiO).
  • To simulate nuclear stabilization processes for optimization.
  • To evaluate the performance of FuFiO against established algorithms.

Main Methods:

  • Developing the Fusion-Fission Optimization (FuFiO) algorithm based on nuclear fusion, fission, and beta-decay.
  • Utilizing a set of 120 mathematical benchmark test functions for evaluation.
  • Comparing FuFiO's performance with other metaheuristic algorithms using non-parametric statistical tests.
  • Analyzing results on complex problems from CEC-2017 and CEC-2019 competitions.

Main Results:

  • The FuFiO algorithm successfully simulates nuclear stabilization for optimization.
  • FuFiO demonstrated superior performance across a majority of the tested benchmark functions.
  • The algorithm's effectiveness was validated against other leading metaheuristic approaches.

Conclusions:

  • The proposed Fusion-Fission Optimization (FuFiO) algorithm is a promising new metaheuristic.
  • FuFiO offers an effective and superior approach for solving complex optimization problems.
  • The nuclear physics-inspired model provides a robust framework for computational intelligence.