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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization.

Soleiman Kadkhoda Mohammadi1, Daryoush Nazarpour1,2, Mojtaba Beiraghi1

  • 1Department of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.

Neural Computing & Applications
|May 8, 2023
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Summary
This summary is machine-generated.

A new metaheuristic algorithm, the coronavirus metamorphosis optimization algorithm (CMOA), offers superior optimization for complex problems. CMOA effectively solves engineering challenges and maintains population diversity, outperforming existing methods.

Keywords:
Coronavirus metamorphosis optimization algorithm (CMOA)Engineering optimizationOptimizationOptimization algorithmsReal-world optimization functions

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Area of Science:

  • Computational intelligence and optimization science.
  • Development of novel metaheuristic algorithms for complex problem-solving.

Background:

  • Metaheuristic optimization algorithms, inspired by nature, are crucial for solving complex problems across diverse fields like medicine and engineering.
  • The increasing complexity of real-world problems necessitates the development of new, more effective optimization algorithms.
  • Existing metaheuristic methods face challenges in selecting the most appropriate approach for specific complex optimization tasks.

Purpose of the Study:

  • To introduce a novel and powerful metaheuristic optimization algorithm named the coronavirus metamorphosis optimization algorithm (CMOA).
  • To evaluate the performance and robustness of the proposed CMOA algorithm on benchmark functions and real-world engineering problems.

Main Methods:

  • The coronavirus metamorphosis optimization algorithm (CMOA) was developed, drawing inspiration from biological metabolism and transformation processes.
  • CMOA was rigorously tested and implemented on the comprehensive CEC2014 benchmark functions, which are designed to represent real-world challenges.
  • Comparative analysis was conducted against numerous state-of-the-art metaheuristic algorithms under identical experimental conditions.

Main Results:

  • The CMOA algorithm demonstrated superior performance compared to a wide array of recently developed metaheuristic algorithms on the CEC2014 benchmark functions.
  • Experimental results indicated that CMOA provides more optimized and suitable solutions, effectively preserving population diversity and avoiding local optima.
  • Application to three engineering design problems (welded beam, three-bar truss, pressure vessel) confirmed CMOA's high potential and effectiveness in finding global optima.

Conclusions:

  • The proposed coronavirus metamorphosis optimization algorithm (CMOA) is a robust, stable, and reliable metaheuristic method for addressing complex optimization problems.
  • CMOA significantly outperforms existing algorithms in terms of solution quality and efficiency for both benchmark and practical engineering applications.
  • The algorithm's ability to maintain population diversity and escape local optima makes it a valuable tool for expert systems and scientific research.