Hybrid Grey Wolf and Al-Biruni Earth Radius Optimization

for Sustainable Electric Vehicle Routing

Mostafa Abotaleb1,*

1 Engineering School of Digital Technologies, Yugra State University, Khanty-Mansiysk, Russia

Email: abotalebmostafa@bk.ru

Received: January 28, 2026 Revised: March 19, 2026 Accepted: May 28, 2026 ⋆ Corresponding author

ABSTRACT

In this chapter, a novel hybrid optimization algorithm, which integrates the Grey Wolf Optimizer (GWO) with the

Al-Biruni Earth Radius (BER) method to address the problem of electric vehicle (EV) path planning, is introduced.

By incorporating the simulation of wolf social hierarchies for efficient exploration alongside the spatial analysis

capabilities of BER, the proposed approach effectively determines optimal, energy-efficient routes across diverse

terrains. The hybrid approach systematically considers key factors such as distance, terrain characteristics, and energy

consumption to guide vehicle movements, thereby minimizing energy usage, enhancing travel efficiency, and ensuring

smooth traffic flow. Experimental results demonstrate the effectiveness of the proposed hybrid method compared to

conventional metaheuristic approaches, achieving an average error of 0.530218. This chapter presents the technical

framework of this hybrid approach, its applications in EV route optimization, and its broader implications for

sustainable mobility and real-world transportation challenges.

Keywords: Electric Vehicle Path Planning Hybrid Optimization Algorithms Grey Wolf Optimizer (GWO) Al-Biruni

Earth Radius (BER)

1. INTRODUCTION

The advancement of electric vehicles (EVs) represents a crucial

milestone in sustainable transportation, contributing to

reducing greenhouse gas emissions, decreased dependence

on fossil fuels, improved oil displacement efficiency, and potential

cost savings. With the global increase in EV adoption,

driven by government policies and industrial advancements,

enhancing the efficiency and reliability of EV systems has become

a critical area of research [1, 2]. Among these systems,

the problem of electric vehicle path planning (EVPP) problem

is paramount, as it significantly influences energy consumption,

travel efficiency, and user satisfaction. Effective path

planning enables EVs to navigate complex and dynamic environments

while accounting for various factors such as terrain

variability, traffic conditions, energy constraints, and the availability

of charging stations [3]. Addressing these challenges

necessitates sophisticated optimization techniques for exploring

vast multidimensional solution spaces and identifying

near-optimal paths.

Optimization algorithms, particularly metaheuristic methods,

have proven powerful tools for solving complex and nonlinear

problems such as EVPP. Unlike traditional optimization

techniques, metaheuristic algorithms excel at avoiding local

optima and discovering global solutions within large and

dynamic problem spaces [4]. This chapter explores the development

of a hybrid optimization approach that integrates the

capabilities of two state-of-the-art optimization algorithms:

the Grey Wolf Optimizer (GWO) and the Al-Biruni Earth

Radius (BER) method. By combining these algorithms into

a synergistic framework, this hybrid approach effectively

addresses the unique challenges associated with EV path