Bi-criteria ant colony optimization algorithm for minimizing makespan and energy consumption on parallel batch machines

As one of the three pillars of information technology, wireless sensor networks (WSNs) have been widely used in environmental detection, healthcare, military surveillance, industrial data sampling, and many other fields due to their unparalleled advantages in deployment cost, network power consumption, and versatility. The advent of the 5G standard and the era of Industry 4.0 have brought new opportunities for the development of wireless sensor networks. However, due to the limited power capacity of the sensor nodes themselves, the harsh deployment environment will bring a great difficulty to the energy replenishment of the sensor nodes, so the energy limitation problem has become a major factor limiting its further development; how to improve the energy utilization efficiency of WSNs has become an urgent problem in the scientific and industrial communities. Based on this, this paper researches the routing technology of wireless sensor networks, from the perspective of improving network security, and reducing network energy consumption, based on the study of ant colony optimization algorithm, further studies the node trust evaluation mechanism, and carries out the following research work: (1) study the energy consumption model of wireless sensor networks; (2) basic ant colony algorithm improvement; (3) multiobjective ant colony algorithm based on wireless sensor routing algorithm optimization. In this study, the NS2 network simulator is used as a simulation tool to verify the performance of the research algorithm. Compared with existing routing algorithms, the simulation results show that the multiobjective ant colony optimization algorithm has better performance in evaluation indexes such as life cycle, node energy consumption, node survival time, and stability compared with the traditional algorithm and the dual cluster head ant colony optimization algorithm.

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Machine Learning based Energy Management at Internet of Things Network Nodes

Journal of Trends in Computer Science and Smart Technology - September 2019 ◽

10.36548/jtcsst.2020.3.001 ◽

2020 ◽

Vol 2 (3) ◽

pp. 127-133

Author(s):

Dr. Joy Iong Zong Chen ◽

Kong-Long Lai

Keyword(s):

Machine Learning ◽

Energy Consumption ◽

Internet Of Things ◽

Ant Colony Optimization ◽

Optimization Algorithm ◽

High Energy ◽

Ant Colony ◽

Wireless Sensor ◽

Ant Colony Optimization Algorithm ◽

Network Nodes

The Internet of Things networks comprising wireless sensors and controllers or IoT gateways offers extremely high functionalities. However, not much attention is paid towards energy optimization of these nodes and enabling lossless networks. The wireless sensor networks and its applications has industrialized and scaled up gradually with the development of artificial intelligence and popularization of machine learning. The uneven network node energy consumption and local optimum is reached by the algorithm protocol due to the high energy consumption issues relating to the routing strategy. The smart ant colony optimization algorithm is used for obtaining an energy balanced routing at required regions. A neighbor selection strategy is proposed by combining the wireless sensor network nodes and the energy factors based on the smart ant colony optimization algorithm. The termination conditions for the algorithm as well as adaptive perturbation strategy are established for improving the convergence speed as well as ant searchability. This enables obtaining the find the global optimal solution. The performance, network life cycle, energy distribution, node equilibrium, network delay and network energy consumption are improved using the proposed routing planning methodology. There has been around 10% energy saving compared to the existing state-of-the-art algorithms.

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Sequence Planning for Selective Disassembly Aiming at Reducing Energy Consumption Using a Constraints Relation Graph and Improved Ant Colony Optimization Algorithm

Energies ◽

10.3390/en11082106 ◽

2018 ◽

Vol 11 (8) ◽

pp. 2106 ◽

Cited By ~ 3

Author(s):

Bingtao Hu ◽

Yixiong Feng ◽

Hao Zheng ◽

Jianrong Tan

Keyword(s):

Energy Consumption ◽

Ant Colony Optimization ◽

Optimization Algorithm ◽

Ant Colony Algorithm ◽

Ant Colony ◽

Ant Colony Optimization Algorithm ◽

Number Of Components ◽

Disassembly Sequence ◽

Selective Disassembly ◽

Relation Graph

With environmental pollution and the shortage of resources becoming increasingly serious, the disassembly of certain component in mechanical products for reuse and recycling has received more attention. However, how to model a complex mechanical product accurately and simply, and minimize the number of components involved in the disassembly process remain unsolved problems. The identification of subassembly can reduce energy consumption, but the process is recursive and may change the number of components to be disassembled. In this paper, a method aiming at reducing the energy consumption based on the constraints relation graph (CRG) and the improved ant colony optimization algorithm (IACO) is proposed to find the optimal disassembly sequence. Using the CRG, the subassembly is identified and the number of components that need to be disassembled is minimized. Subsequently, the optimal disassembly sequence can be planned using IACO where a new pheromone factor is proposed to improve the convergence performance of the ant colony algorithm. Furthermore, a case study is presented to illustrate the effectiveness of the proposed method.

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An Energy-Aware Task Scheduling in the Cloud Computing Using a Hybrid Cultural and Ant Colony Optimization Algorithm

International Journal of Cloud Applications and Computing ◽

10.4018/ijcac.2017100102 ◽

2017 ◽

Vol 7 (4) ◽

pp. 20-40 ◽

Cited By ~ 16

Author(s):

Poopak Azad ◽

Nima Jafari Navimipour

Keyword(s):

Cloud Computing ◽

Energy Consumption ◽

Ant Colony Optimization ◽

Task Scheduling ◽

Optimization Algorithm ◽

Heterogeneous Computing ◽

Heuristic Algorithms ◽

Ant Colony ◽

Ant Colony Optimization Algorithm ◽

Energy Aware

In a cloud environment, computing resources are available to users, and they pay only for the used resources. Task scheduling is considered as the most important issue in cloud computing which affects time and energy consumption. Task scheduling algorithms may use different procedures to distribute precedence to subtasks which produce different makespan in a heterogeneous computing system. Also, energy consumption can be different for each resource that is assigned to a task. Many heuristic algorithms have been proposed to solve task scheduling as an NP-hard problem. Most of these studies have been used to minimize the makespan. Both makespan and energy consumption are considered in this paper and a task scheduling method using a combination of cultural and ant colony optimization algorithm is presented in order to optimize these purposes. The basic idea of the proposed method is to use the advantages of both algorithms while avoiding the disadvantages. The experimental results using C# language in cloud azure environment show that the proposed algorithm outperforms previous algorithms in terms of energy consumption and makespan.

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Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

Finance and Credit ◽

10.24891/fc.26.11.2427 ◽

2020 ◽

Vol 26 (11) ◽

pp. 2427-2447

Author(s):

S.N. Yashin ◽

E.V. Koshelev ◽

S.A. Borisov

Keyword(s):

Ant Colony Optimization ◽

Optimization Algorithm ◽

Population Decline ◽

Innovation System ◽

Federal District ◽

Original Model ◽

Ant Colony ◽

Industrial Agglomeration ◽

Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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