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MOCOVIDOA: a novel multi-objective coronavirus disease optimization algorithm for solving multi-objective

Asmaa M Khalid1, Hanaa M Hamza1, Seyedali Mirjalili2

  • 1Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Egypt.

Neural Computing & Applications
|June 26, 2023
PubMed
Summary
This summary is machine-generated.

A new multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA) effectively solves complex global optimization problems. This novel approach demonstrates superior performance compared to existing methods in benchmark and real-world engineering tests.

Keywords:
ConvergenceCoronavirusCoverageDominanceFrameshiftingMulti-objective

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Bio-inspired computing

Background:

  • Global optimization problems often involve multiple, conflicting objectives.
  • Existing multi-objective metaheuristics may face challenges in efficiently handling complex search spaces.
  • The need for robust algorithms inspired by natural processes is critical.

Purpose of the Study:

  • To introduce a novel multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA).
  • To address global optimization problems with up to three objective functions.
  • To enhance solution selection using a biologically inspired mechanism.

Main Methods:

  • Development of the multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA).
  • Utilization of an archive to store non-dominated solutions.
  • Implementation of a roulette wheel selection mechanism simulating viral frameshifting.
  • Evaluation on 27 multi-objective problems (21 benchmarks, 6 engineering designs).

Main Results:

  • MOCOVIDOA demonstrated superior performance across six evaluation metrics (IGD, GD, MS, SP, HV, delta p).
  • Statistical analysis (Wilcoxon rank-sum test) confirmed the algorithm's advantage over five common multi-objective metaheuristics.
  • The algorithm proved effective in solving both benchmark and real-world engineering design problems.

Conclusions:

  • The novel MOCOVIDOA algorithm offers a significant advancement in multi-objective optimization.
  • Its bio-inspired approach provides a robust and efficient method for tackling complex optimization tasks.
  • MOCOVIDOA shows strong applicability and potential for solving diverse multi-objective problems.