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Enhanced Prairie Dog Optimization with Differential Evolution for solving engineering design problems and network

Mohammad Alshinwan¹, Osama A Khashan², Mohammed Khader¹

¹Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan.

|September 16, 2024

Summary

This summary is machine-generated.

A new hybrid optimization algorithm, PDO-DE, combines Prairie Dog Optimization (PDO) and Differential Evolution (DE) for complex engineering and cybersecurity tasks. This robust method shows superior performance in speed and accuracy across various benchmarks and real-world applications.

Keywords:

Differential Evolution algorithm Engineering problems Optimization problems Prairie dog algorithm Real-world problems

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Area of Science:

Computational Intelligence
Optimization Algorithms
Swarm Intelligence

Background:

Complex optimization problems require advanced algorithms for engineering design and cybersecurity.
Existing methods may lack efficiency in exploration and exploitation for diverse problem domains.
The Prairie Dog Optimization (PDO) algorithm and Differential Evolution (DE) strategy are prominent in stochastic optimization.

Purpose of the Study:

To introduce and evaluate a novel hybrid optimization algorithm, PDO-DE.
To enhance the search capabilities of PDO by integrating DE's mutation and crossover mechanisms.
To demonstrate the algorithm's effectiveness in engineering design and network intrusion detection systems (NIDS).

Main Methods:

Integration of the Prairie Dog Optimization (PDO) algorithm with the Differential Evolution (DE) strategy.
Incorporation of DE's mutation and crossover for improved solution exploration and exploitation.
Rigorous testing on 23 benchmark functions, five engineering design problems, and a NIDS dataset.

Main Results:

PDO-DE demonstrated superior performance compared to state-of-the-art algorithms in convergence speed and accuracy.
The algorithm showed robustness and adaptability across benchmark functions and engineering problems.
In NIDS, PDO-DE achieved a 98.1% detection rate, 2.4% false alarm rate, and 96% accuracy.

Conclusions:

The PDO-DE algorithm offers a significant advancement in hybrid optimization techniques.
It provides a more effective approach for solving real-world problems demanding high precision.
The algorithm shows promise for applications in engineering and cybersecurity, including NIDS.