Region-Focused Memetic Algorithms With Smart Initialization for Real-World Large-Scale Waste Collection Problems

Lan, Wenxing; Ye, Ziyuan; Peijun, Ruan; Liu, Jialin; Yang, Peng; Yao, Xin

doi:10.1109/tevc.2021.3123960

Cited by 13 publications

(4 citation statements)

References 34 publications

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“…The local search algorithm is intended to determine the shortest path by performing a jump process to a new region to trace the shortest path effectively. In the memetic Algorithm (MA), [21] the number of individual nodes is created and counted to determine the best cluster head and the size of the cluster head node. In practice, 5% of nodes are randomly chosen as the cluster head and in turn, those temporary cluster head performs a local search to determine the actual cluster head.…”

Section: Clustering Process By Memetic Algorithmmentioning

confidence: 99%

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Manikandan¹,

Sakthivel²,

Vivekanandhan³

2023

Intelligent Automation &Amp; Soft Computing

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Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network. This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms. The efficiency of the sensor node is energy bounded, acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors. Network management plays a significant role in wireless sensor networks, which was obsessed with the factors like the reliability of the network, resource management, energy-efficient routing, and scalability of services. The topology of the wireless sensor networks acts driven factor for network efficiency which can be effectively maintained by performing the clustering process effectively. More solutions and clustering algorithms have been offered by various researchers, but the concern of reduced efficiency in the routing process and network management still exists. This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks. The memetic algorithm employs a local searching methodology to mitigate the premature convergence, while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain (i.e.,) best cluster head in the wireless sensor networks. The proposed hybrid algorithm is compared with the state of art clustering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time, energy consumption, throughput, etc.

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Section: Clustering Process By Memetic Algorithmmentioning

confidence: 99%

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Manikandan¹,

Sakthivel²,

Vivekanandhan³

2023

Intelligent Automation &Amp; Soft Computing

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“…Finally, the policies evolved by APTGP tend to be large and difficult to interpret. In future, we will improve APTGP to produce more interpretable policies and also, we plan to test our algorithm on larger real-world datasets, such as the real-world waste collection datasets in [69].…”

Section: Transferred Unique Individualsmentioning

confidence: 99%

Knowledge Transfer Genetic Programming With Auxiliary Population for Solving Uncertain Capacitated Arc Routing Problem

Ardeh

Mei

Zhang

et al. 2023

IEEE Trans. Evol. Computat.

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The uncertain capacitated arc routing problem is an NP-hard combinatorial optimisation problem with a wide range of applications in logistics domains. Genetic programming hyperheuristic has been successfully applied to evolve routing policies to effectively handle the uncertain environment in this problem. The real world usually encounters different but related instances due to events like season change and vehicle breakdowns, and it is desirable to transfer knowledge gained from solving one instance to help solve another related one. However, the solutions found by the genetic programming process can lack diversity, and the existing methods use the transferred knowledge mainly during initialisation. Thus, they cannot sufficiently handle the change from the source to the target instance. To address this issue, we develop a novel knowledge transfer genetic programming with an auxiliary population. In addition to the main population for the target instance, we initialise an auxiliary population using the transferred knowledge and evolve it alongside the main population. We develop a novel scheme to carefully exchange the knowledge between the two populations, and a surrogate model to evaluate the auxiliary population efficiently. The experimental results confirm that the proposed method performed significantly better than the state-of-the-art genetic programming approaches for a wide range of uncertain arc routing instances, in terms of both final performance and convergence speed.

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“…Most VRP research, including WaCo applications, target rather small problems with only a few hundred customers. In contrast, Lan et al (2022) consider multiple depots, disposal facilities and trips, and implement a memetic algorithm to solve instances with up to 3000 collection sites, 3 depots and 3 disposal facilities in their real-world case study in China. Nuortio et al (2006) deal with a PVRP for municipal WaCo in Finland with up to 3386 nodes and a planning horizon of up to four weeks.…”

Section: Vrps Of a Larger Sizementioning

confidence: 99%

Waste collection routing: a survey on problems and methods

Hess,

Dragomir,

Doerner

et al. 2023

Cent Eur J Oper Res

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Waste collection is a vital service performed all over the world, which heavily relies on vehicle routing. Due to regulations and local conditions, the problems and their characteristics often differ greatly. This literature survey aims to review the current state of the art overlap in waste collection and vehicle routing literature. The most notable papers are categorized according to their underlying problem type, examined and brought into relation based on their common problem characteristics. The problem types comprise general, node and arc routing problems, with vehicle routing problems being the most common, followed by arc and location routing problems. Besides the use of intermediate facilities, which is naturally very common in waste collection literature, the authors point out other interesting characteristics found in the literature and in practical problems, such as uncertain demand, personnel planning aspects, alternative collection systems or vehicle types, and characteristics related to risk or sustainability. Additionally, the authors highlight prominent scopes and objectives as well as recent developments in this area. Overall, this survey provides a selective overview and calls attention to research gaps and possible future research directions.

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Region-Focused Memetic Algorithms With Smart Initialization for Real-World Large-Scale Waste Collection Problems

Cited by 13 publications

References 34 publications

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Knowledge Transfer Genetic Programming With Auxiliary Population for Solving Uncertain Capacitated Arc Routing Problem

Waste collection routing: a survey on problems and methods

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