An Intelligent Artificial System : Artificial Immune based Hybrid Genetic Algorithm for the Vehicle Routing Problem

Hsu, L.; Hsu, Cheng–Che; Lin, Tsai-Duan

doi:10.12785/amis/080332

Cited by 16 publications

(7 citation statements)

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“…In [54], [59] authors have presented a hybrid approach merging GA with an iterated local search for efficient location-routing problem also the later also considered time window. Hsu et al [56]suggested a hybrid genetic algorithm for vehicle routing having lesser capacity. On the other hand Cai et al [97] provided solution to a vehicle routing problem with various depos and coincident pick up and delivery time windows.…”

Section: Vehicle Routingmentioning

confidence: 99%

Optimization Algorithm in Supply Chain Management

Bandekar*¹,

C*²

2019

IJITEE

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 Abstract: Optimization in the field of Operations Research has applications in various industries, be it medicine, business, analytics or education. Likewise Supply Chain Management (SCM) is required in every industry and with the need comes various challenges to get the optimized and best quality solution. There are stochastic, analytical models working on attaining optimization in various sub events involved in SCM. Supply Chain is a network at global level used for delivering of products and services from unprocessed materials to consumers through well-structured and planned flow of information, physical distribution and money. The process of managing this supply chain is Supply Chain Management. A major work on the previous research done using various mathematical models, be it mixed integer linear, nonlinear programming or evolutionary have been depicted in this paper. The aim is to get the best result and comparative approach is focused. This article provides a detailed study on various techniques, algorithms and mathematical models in optimization of SCM and in particular it focuses on Genetic Algorithm (GA) in SCM.

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Section: Vehicle Routingmentioning

confidence: 99%

Optimization Algorithm in Supply Chain Management

Bandekar*¹,

C*²

2019

IJITEE

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“…In this section, a brief but comprehensive literature review of a special bioinspired algorithm, the artificial immune systems, is provided. AIS was applied in different contexts with positive results: when solving combinatorial problems [17,18], to detect intrusions in wireless sensor networks [19], or even to generate chord progressions [20]. The major goal of this section is to review, identify, evaluate, and analyze the performance of AIS in power systems and model research.…”

Section: Ais Applications In Energymentioning

confidence: 99%

“…For the purposes of this paper, it is assumed that there is no uncertainty in predicting the electrical loads of must-run services (17). As in the storage water heater, the maximum of the load shedding L shed mrs t must always be less than the energy consumed L mrs (t) (18).…”

Section: Storage Water Heatermentioning

confidence: 99%

An Evaluation of a Metaheuristic Artificial Immune System for Household Energy Optimization

et al. 2018

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Devices in a smart home should be connected in an optimal way; this helps save energy and money. Among numerous optimization models that can be found in the literature, we would like to highlight artificial immune systems, which use special bioinspired algorithms to solve optimization problems effectively. The aim of this work is to present the application of an artificial immune system in the context of different energy optimization problems. Likewise, a case study is performed in which an artificial immune system is incorporated in order to solve an energy management problem in a domestic environment. A thorough analysis of the different strategies is carried out to demonstrate the ability of an artificial immune system to find a successful optima which satisfies the problem constraints.

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“…An artificial immune system (AIS) model consisting of a set of B-cells and links between them was proposed in [40][41][42]. A single data item is represented by a B-cell object, which is being used for learning.…”

Section: T-cells Also Affect B-cells As the Helper T-cells Activates mentioning

confidence: 99%

Bio‐inspired packet dropping for ad‐hoc social networks

Liaqat

Xia

Yang

et al. 2014

Int J Communication

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Ad-hoc social networks (ASNETs) explore social properties of nodes in communications. The usage of various social applications in a resource-scarce environment and the dynamic nature of the network create unnecessary congestion that might degrade the quality of service dramatically. Traditional approaches use drop-tail or random-early discard techniques to drop data packets from the intermediate node queue. Nonetheless, because of the unavailability of the social properties, these techniques are not suitable for ASNETs. In this paper, we propose a Bio-inspired packet dropping (BPD) algorithm for ASNETS. BPD imitates the matching procedure of receptors and epitopes in immune systems to detect congestions. The drop probability settings depend on the selection of data packets, which is based on node popularity level. BPD selects the most prioritized node through social properties, which is inspired by the B-cell stimulation in immune systems. To fairly prioritize data packets, two social properties are used: (1) similarity and (2) closeness centrality between nodes. Extensive simulations are carried out to evaluate and compare BPD to other existing schemes in terms of mean goodput, mean loss rate, throughput, delay, attained bandwidth, and overhead ratio. The results show that the proposed scheme outperforms these existing schemes.(QoS) and overhead cost. Therefore, to overcome scheduling problems, the consideration of socially aware properties provide high advantages in minimum QoS with less overhead cost [9]. Moreover, another reason for congestion in intermediate node 7 is the unavailability of receivers due to node mobility. Therefore, to avoid congestion-related losses in ASNETs, an accurate active queue management (AQM) policy is required [10]. The systemic constraints, such as poor link utilization and data packet drop rates in networks, result in delay queuing processes with increment of congested intermediate node. However, to solve the issue, some conflicts arise. In trying to reduce queuing delay, the size of buffer should be reduced, whereas it leads to low utilization of link at the same time, resulting in a high packet loss rate. To have an efficient queue management, accurate detection and dropping of targeted data packets are necessary because queue management strongly depends on dropping policies.Additionally, due to social behaviors of nodes in ASNET environments, dropping probabilities also depend on social properties of nodes. Primarily, there are multiple AQM schemes available to identify prioritization of data packets for dropping [11][12][13]. The non-prioritized data packets are dropped in an intermediate node due to less availability of the next node and resource constraint characteristics of nodes. Data packet dropping is broadly used to overcome issues present in the intermediate node, and it provides availability of prioritized data packets. However, every data packet dropping in an intermediate node is a kind of resource consumption. According to the concept of ASNET, dropped data packet...

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An Intelligent Artificial System : Artificial Immune based Hybrid Genetic Algorithm for the Vehicle Routing Problem

Cited by 16 publications

References 0 publications

Optimization Algorithm in Supply Chain Management

Optimization Algorithm in Supply Chain Management

An Evaluation of a Metaheuristic Artificial Immune System for Household Energy Optimization

Bio‐inspired packet dropping for ad‐hoc social networks

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