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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Enhanced crested porcupine optimizer for numerical optimization and wireless sensor network deployment.

Feng Zhao¹, Zenghe Li¹, Peng Guo²

¹Shijiazhuang University, 050053, Shijiazhuang, China.

Scientific Reports

|November 17, 2025

Summary

This summary is machine-generated.

The Enhanced Crested Porcupine Optimizer (ECPO) improves metaheuristic performance by integrating Sobol sequences, guided search, adaptive Lévy flights, and reverse learning. ECPO demonstrates superior convergence, accuracy, and stability in optimization tasks and wireless sensor network deployment.

Keywords:

Crested porcupine optimizer Exploration and exploitation Guided search strategy Metaheuristic algorithm Wireless sensor network

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Area of Science:

Computational Intelligence
Optimization Algorithms
Metaheuristics

Background:

Metaheuristic algorithms face challenges like slow convergence and local optima.
The Crested Porcupine Optimizer (CPO) shows promise but retains these limitations.
Complex problems like Wireless Sensor Network (WSN) deployment require enhanced optimization.

Purpose of the Study:

To develop an Enhanced Crested Porcupine Optimizer (ECPO) to overcome CPO's limitations.
To improve convergence speed, accuracy, and global search capability.
To validate ECPO's effectiveness on benchmark functions and WSN deployment.

Main Methods:

ECPO integrates Sobol sequences for initialization, guided search, adaptive Lévy flights, and reverse learning.
Population initialization ensures uniform distribution for enhanced global search.
Adaptive strategies and reverse learning improve diversity, accuracy, and convergence.

Main Results:

ECPO significantly outperformed classical, recent, and CEC-winning algorithms on benchmark suites (CEC2014-2022).
ECPO achieved superior convergence speed, accuracy, and stability across tests.
In WSN deployment, ECPO yielded higher coverage rates and better robustness.

Conclusions:

ECPO effectively addresses the limitations of the original CPO.
ECPO demonstrates superior performance compared to state-of-the-art optimizers.
ECPO is a high-performance algorithm suitable for complex numerical and engineering optimization problems.