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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Red piranha optimization (RPO): a natural inspired meta-heuristic algorithm for solving complex optimization

Asmaa H Rabie1, Ahmed I Saleh1, Nehal A Mansour1

  • 1Computers and Control Department, Faculty of Engineering Mansoura University, Mansoura, Egypt.

Journal of Ambient Intelligence and Humanized Computing
|May 25, 2023
PubMed
Summary
This summary is machine-generated.

A new Red Piranha Optimization (RPO) algorithm, inspired by piranha hunting behavior, effectively solves complex optimization problems. RPO demonstrates superior performance in feature selection for Covid-19 diagnosis compared to existing bio-inspired methods.

Keywords:
AlgorithmsBio-inspiredMeta-heuristicOptimizationPiranha

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Area of Science:

  • Computational Intelligence
  • Swarm Intelligence
  • Optimization Algorithms

Background:

  • Optimization algorithms are crucial for finding optimal solutions in complex problems.
  • Nature-inspired meta-heuristic algorithms leverage swarm intelligence for enhanced problem-solving.
  • Existing algorithms face challenges in efficiency and avoiding local optima.

Purpose of the Study:

  • To introduce a novel nature-inspired optimization algorithm, Red Piranha Optimization (RPO).
  • To model RPO based on the cooperative hunting behavior of Red Piranha.
  • To evaluate RPO's efficacy in feature selection for classification tasks, specifically Covid-19 diagnosis.

Main Methods:

  • Developed the Red Piranha Optimization (RPO) algorithm with three phases: prey searching, encircling, and attacking.
  • Provided a mathematical model for each phase of the RPO algorithm.
  • Applied RPO and compared it with recent bio-inspired optimization algorithms for feature selection in Covid-19 diagnosis.

Main Results:

  • The Red Piranha Optimization (RPO) algorithm is simple, easy to implement, and effective at avoiding local optima.
  • RPO demonstrated superior performance in feature selection for Covid-19 diagnosis.
  • Experimental results showed RPO outperformed other bio-inspired techniques in accuracy, execution time, precision, recall, and f-measure.

Conclusions:

  • The proposed Red Piranha Optimization (RPO) algorithm is a viable and effective tool for complex optimization problems.
  • RPO shows significant potential for application in critical areas like medical diagnosis through feature selection.
  • The algorithm's simplicity and ability to bypass local optima make it a valuable addition to the field of computational intelligence.