An Improved Cuckoo Search Algorithm for the Capacitated Green Vehicle Routing Problem

Karagül, Kenan; Şahin, Yusuf

doi:10.1007/978-3-031-09835-2_21

Cited by 1 publication

(1 citation statement)

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“…The implementation of the CSA spans a wide array of fields, demonstrating its versatility and effectiveness. This includes applications in vehicle routing [106,107], neural networks [108], scheduling [109,110], medical fields [111,112], cloud computing [113], and notably in robotics, particularly in the navigation of smart vehicles. Table 13 showcases a range of hybrid approaches combining Cuckoo Search with other methods to address path planning challenges.…”

Section: C/c++ Languagementioning

confidence: 99%

Optimizing Path Planning for Smart Vehicles: A Comprehensive Review of Metaheuristic Algorithms

Mbah,

Zeeshan

2023

JEMSE

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In the realm of smart vehicle navigation, both in known and unknown environments, the crucial aspects encompass the vehicle's localization using an array of technologies such as GPS, cameras, vision systems, laser, and ultrasonic sensors. This process is pivotal for effective motion planning within the vehicle's free configuration space, enabling it to adeptly avoid obstacles. The focal point of such navigation systems lies in devising a path from an initial to a target configuration, striving to minimize the path length and the time taken, while simultaneously circumventing obstacles. The application of metaheuristic algorithms has been pivotal in this regard. These algorithms, characterized by their ability to exploit initial solutions and explore the environment for feasible pathways, have been extensively utilized. A significant body of research in robotics and automation has focused on evaluating the efficacy of population-based algorithms including Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization Algorithm (WOA). Additionally, trajectory-based methods such as Tabu Search (TS) and Simulated Annealing (SA) have been scrutinized for their proficiency in identifying short, feasible paths among the plethora of solutions. There has been a surge in the enhancement and modification of these algorithms, with a multitude of hybrid metaheuristic algorithms being proposed. This review meticulously examines various metaheuristic algorithms and their hybridizations, specifically in their application to the path planning challenges faced by smart vehicles. The exploration extends to the comparison of these algorithms, highlighting their distinct advantages and limitations. Furthermore, the review delves into potential future directions in this evolving field, emphasizing the continual refinement of these algorithms to cater to the increasingly complex demands of smart vehicle navigation.

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