Correlation with the fundamental PSO and PSO modifications to
be hybrid swarm optimization

Hasan, Raed Abdulkareem; Shahab, Suhail Najm; Ahmed, Munef Abdullah

doi:10.52866/ijcsm.2021.02.02.004

Cited by 13 publications

(9 citation statements)

References 13 publications

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“…Secondly, the "geometric" component of a GIS is merely one among many. In [22]- [25] presents four perspectives on GIS in a survey for cartographers, geometers should consider these perspectives when judging the relevance of problems inspired by GIS: i) automated mapping: enabling the creation of standard maps, ii) map analysis: provides cheaper overlay and measurement tools compared to the conventional methods, iii) inventory: provides geographic access capabilities to the existing corporate and governmental databases, and iv) spatial analysis and spatial decision support: facilitating new ways of using old data via provision of analysis and query tools for the users. Hence, this work will rely on the use of the spatial map analysis.…”

Section: Geometric Problems In Geographic Information Systemsmentioning

confidence: 99%

“…GIS-T are integrated technologies, software, people, data, firms, and institutional frameworks for gathering, storing, analyzing, and disseminating specific earth-related information [25]- [27]. Geographic regions and transportation systems that impact or are affected by these systems are the specific forms of information.…”

Section: Geographic Information Systems-transportation Data Modelsmentioning

confidence: 99%

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Improved GIS-T model for finding the shortest paths in graphs

Raed Abdulkareem Hasan,

Mustafa Mahmood Akawee,

Tole Sutikno

2023

Babylonian Journal of Machine Learning

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A system of models and methods is proposed for the stated problem, which is the development of software for searching the shortest paths in graphs. These models and methods are based on a comparative analysis of various types of geographic information system-transportation (GIS-T) and optimization methods that pertain to searching for the shortest paths in a graph. The goal of this research is to find a solution to the problem. The fundamental ideas behind GIS-T and computational geometry (CG), in addition to the approach of combinatorial optimization, were utilized in this endeavor. Compilation of the newly developed software takes place in the Java Eclipse development environment. Examples of graphic interfaces and potential solutions are used to illustrate its capabilities in relation to the quest for the shortest routes possible within the confines of a particular geographic area. The compiled geographic information system (GIS) tool known as "route" determines the routes that are the shortest distance between existing settlements as well as those that have been defined by the user. In addition to this, it enables the problem to be solved in circumstances in which certain roads become inoperable or certain settlements may be reached via the specified field roads.

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Section: Geometric Problems In Geographic Information Systemsmentioning

confidence: 99%

Section: Geographic Information Systems-transportation Data Modelsmentioning

confidence: 99%

Improved GIS-T model for finding the shortest paths in graphs

Raed Abdulkareem Hasan,

Mustafa Mahmood Akawee,

Tole Sutikno

2023

Babylonian Journal of Machine Learning

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“…The dataset [20,10] is used to validate the convergence of the network. The parameter combination for the reward function is (α, β) = (1, 0.5).…”

Section: Convergence Analysismentioning

confidence: 99%

“…Up to now, there have been numerous metaheuristic works on cloud/edge computing scheduling [8]. Examples of such methods include Particle Swarm Optimization (PSO) [9,10], Ant Colony Optimization (ACO) [11], and Differential Evolution (DE) [12], which have been adopted to optimize the makespan or cost (e.g., resource cost) while satisfying other QoS constraints. It is worth noting that the makespan and cost of executing a workflow on the cloud are two conflicting optimization objectives.…”

Section: Introductionmentioning

confidence: 99%

Time-Sensitive and Resource-Aware Concurrent Workflow Scheduling for Edge Computing Platforms Based on Deep Reinforcement Learning

Zhang,

Wang,

Cheng

2023

Applied Sciences

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The workflow scheduling on edge computing platforms in industrial scenarios aims to efficiently utilize the computing resources of edge platforms to meet user service requirements. Compared to ordinary task scheduling, tasks in workflow scheduling come with predecessor and successor constraints. The solutions to scheduling problems typically include traditional heuristic methods and modern deep reinforcement learning approaches. For heuristic methods, an increase in constraints complicates the design of scheduling rules, making it challenging to devise suitable algorithms. Additionally, whenever the environment undergoes updates, it necessitates the redesign of the scheduling algorithms. For existing deep reinforcement learning-based scheduling methods, there are often challenges related to training difficulty and computation time. The addition of constraints makes it challenging for neural networks to make decisions while satisfying those constraints. Furthermore, previous methods mainly relied on RNN and its variants to construct neural network models, lacking a computation time advantage. In response to these issues, this paper introduces a novel workflow scheduling method based on reinforcement learning, which utilizes neural networks for direct decision-making. On the one hand, this approach leverages deep reinforcement learning, eliminating the need for researchers to define complex scheduling rules. On the other hand, it separates the parsing of the workflow and constraint handling from the scheduling decisions, allowing the neural network model to focus on learning how to schedule without the necessity of learning how to handle workflow definitions and constraints among sub-tasks. The method optimizes resource utilization and response time, as its objectives and the network are trained using the PPO algorithm combined with Self-Critic, and the parameter transfer strategy is utilized to find the balance point for multi-objective optimization. Leveraging the advantages of reinforcement learning, the network can be trained and tested using randomly generated datasets. The experimental results indicate that the proposed method can generate different scheduling outcomes to meet various scenario requirements without modifying the neural network. Furthermore, when compared to other deep reinforcement learning methods, the proposed approach demonstrates certain advantages in scheduling performance and computation time.

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“…Since the number of objectives to be minimized in generating floorplan is being increased, we still have space to improve the circuit performance by reducing peak temperature through designing an exact algorithm by mixing the best essence of different swarm-based meta-heuristic algorithms such as Ant Colony Optimization [27], Particle Swarm Optimization [28] and Firefly Optimization [29] algorithms. The traditional swarm-based optimization algorithms can be combined effectively with various supervised Machine Learning (ML) algorithms, such as Rao Optimization algorithm [30] and Support Vector Machine (SVM) [31], to improve the efficiency and accuracy of the results.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multicriteria Optimization Technique for VLSI Floorplanning Based on Hybridized Firefly and Ant Colony Systems

et al. 2023

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In VLSI circuit design, physical design is one of the main steps in placing the circuit into the chip area. Floorplanning is a crucial step in the physical design of IC design, which generates a blueprint for the placement of circuit modules into the chip. A floorplanning step accepts a netlist as its input, given by the circuit-partitioning step of physical design. The floorplanning step generates optimal placements for the circuit modules. The netlist contains the modules' dimensions, size, and interconnect information. During the floorplan generation, the chip area, wire length required for connecting modules and the heat generated by the chips can be estimated. A good floorplan makes placements and routing simple. In order to improve the circuit performance by minimizing chip area, wire length, and peak temperature, it is essential to generate an optimized floorplan by developing metaheuristic optimization algorithms. A novel Hybridized Multicriteria Ant Colony and Firefly Optimization (HMAC-FO) technique is introduced to generate an optimized floorplan. The primary focus of HMAC-FO technique is to design a model for generating efficient floorplanning. The standard MCNC benchmark dataset has a number of modules with their connections. Firefly optimization is the main algorithm in HMAC-FO, which has been used in generating efficient floorplan with the optimized area, wire length and thermal. The firefly algorithm initially requires some number of solutions as the fireflies' population. In the firefly algorithm, usually, the populations are generated randomly. But, to improve the firefly optimization algorithm's performance and obtain better optimum results, the proposed technique uses ACO to generate the initial population, which are all optimal solutions. The firefly algorithm uses the set of optimal solutions as the initial population and generates the globally optimal result. The proposed technique has experimented with the standard MCNC benchmark circuits, and the results prove that the proposed algorithm reduces the area by 3.48%, the wire length by 0.64% and the temperature by 3.33% than the best existing methodology. The proposed methodology generates a good floorplan with all the required optimization, such as area, wire length and heat generation.

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Correlation with the fundamental PSO and PSO modifications to be hybrid swarm optimization

Cited by 13 publications

References 13 publications

Improved GIS-T model for finding the shortest paths in graphs

Improved GIS-T model for finding the shortest paths in graphs

Time-Sensitive and Resource-Aware Concurrent Workflow Scheduling for Edge Computing Platforms Based on Deep Reinforcement Learning

A Novel Multicriteria Optimization Technique for VLSI Floorplanning Based on Hybridized Firefly and Ant Colony Systems

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