skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: A Schedule of Duties in the Cloud Space Using a Modified Salp Swarm Algorithm
Cloud computing is a concept introduced in the information technology era, with the main components being the grid, distributed, and valuable computing. The cloud is being developed continuously and, naturally, comes up with many challenges, one of which is scheduling. A schedule or timeline is a mechanism used to optimize the time for performing a duty or set of duties. A scheduling process is accountable for choosing the best resources for performing a duty. The main goal of a scheduling algorithm is to improve the efficiency and quality of the service while at the same time ensuring the acceptability and effectiveness of the targets. The task scheduling problem is one of the most important NP-hard issues in the cloud domain and, so far, many techniques have been proposed as solutions, including using genetic algorithms (GAs), particle swarm optimization, (PSO), and ant colony optimization (ACO). To address this problem, in this paper one of the collective intelligence algorithms, called the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The performance of the proposed algorithm has been compared with that of GAs, PSO, continuous ACO, and the basic SSA. The results show that our algorithm has generally higher performance than the other algorithms. For example, compared to the basic SSA, the proposed method has an average reduction of approximately 21% in makespan.  more » « less
Award ID(s):
2142360
PAR ID:
10508693
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Springer Nature Switzerland
Date Published:
Page Range / eLocation ID:
62 to 75
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, IEEE 2025 6th International Conference on Artificial Intelligence, Robotics, and Control)
    The challenge of optimizing personalized learning pathways to maximize student engagement and minimize task completion time while adhering to prerequisite constraints remains a significant issue in educational technology. This paper applies the Salp Swarm Algorithm (SSA) as a new solution to this problem. Our approach compares SSA against traditional optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results demonstrate that SSA significantly outperforms these methods, achieving a lower average fitness value of 307.0 compared to 320.0 for GA and 315.0 for PSO. Furthermore, SSA exhibits greater consistency, with a lower standard deviation and superior computational efficiency, as evidenced by faster execution times. The success of SSA is attributed to its balanced approach to exploration and exploitation within the search space. These findings highlight the potential of SSA as an effective tool for optimizing personalized learning experiences 
    more » « less
  2. ; ; ; (, Heat and Mass Transfer)
    In this paper, a multilayer perceptron (MLP)-type artificial neural network model with a back-propagation training algorithm is utilized to model the bubble growth and bubble dynamics parameters in nucleate boiling with a non-uniform electric field. The influences of the electric field on different parameters that describe bubble’s behaviors including bubble waiting time, bubble departure frequency, bubble growth time, and bubble departure diameter are considered. This study models single bubble dynamic behaviors of R113 created on a heater in an inconsistent electric field by utilizing a MLP neural network optimized by four different swarm-based optimization algorithms, namely: Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Artificial Bee Colony (ABC) algorithm, and Particle Swarm Optimization (PSO). For evaluating the model effectiveness, the MSE value (Mean-Square Error) of the artificial neural network model with various optimization algorithms is measured and compared. The results suggest that the optimal networks in the two-hidden layer and three-hidden layer models for the bubble departure diameter improve MSE by 33.85% and 35.27%, respectively, when compared with the best response in the one-hidden layer model. Additionally, for bubble growth time, the networks with two hidden layers and three hidden layers have the 44.51% and 45.85% reduction in error, when compared with the network with one hidden layer, respectively. For the departure frequency, the error reduction in the two-layer and three-layer networks is 46.85% and 62.32%, respectively. For bubble waiting time, the best networks in the two hidden-layer and three hidden-layer models improve MSE by 52.44% and 62.27% compared with the best 1HL model response, respectively. Also, the two algorithms of SSA and GWO are able to compete well (comparable MSE) with the PSO and ABC algorithms. 
    more » « less
  3. ; ; ; ; (, ACM Transactions on Architecture and Code Optimization)
    This paper describes a new approach to register-pressure-aware instruction scheduling, using Ant Colony Optimization (ACO) . ACO is a nature-inspired optimization technique that researchers have successfully applied to NP-hard sequencing problems like the Traveling Salesman Problem (TSP) and its derivatives. In this work, we describe an ACO algorithm for solving the long-standing compiler optimization problem of balancing Instruction-Level Parallelism (ILP) and Register Pressure (RP) in pre-allocation instruction scheduling. Three different cost functions are studied for estimating RP during instruction scheduling. The proposed ACO algorithm is implemented in the LLVM open-source compiler, and its performance is evaluated experimentally on three different machines with three different instruction-set architectures: Intel x86, ARM, and AMD GPU. The proposed ACO algorithm is compared to an exact Branch-and-Bound (B&B) algorithm proposed in previous work. On x86 and ARM, both algorithms are evaluated relative to LLVM's generic scheduler, while on the AMD GPU, the algorithms are evaluated relative to AMD's production scheduler. The experimental results show that using SPECrate 2017 Floating Point, the proposed algorithm gives geometric-mean improvements of 1.13% and 1.25% in execution speed on x86 and ARM, respectively, relative to the LLVM scheduler. Using PlaidML on an AMD GPU, it gives a geometric-mean improvement of 7.14% in execution speed relative to the AMD scheduler. The proposed ACO algorithm gives approximately the same execution-time results as the B&B algorithm, with each algorithm outperforming the other on a substantial number of hard scheduling regions. ACO gives better results than B&B on many large instances that B&B times out on. Both ACO and B&B outperform the LLVM algorithm on the CPU and the AMD algorithm on the GPU. 
    more » « less
  4. ; ; ; ; ; ; ; (, Maritime Business Review)
    Purpose Marine transportation has been faced with an increasing demand for containerized cargo during the past decade. Marine container terminals (MCTs), as the facilities for connecting seaborne and inland transportation, are expected to handle the increasing amount of containers, delivered by vessels. Berth scheduling plays an important role for the total throughput of MCTs as well as the overall effectiveness of the MCT operations. This study aims to propose a novel island-based metaheuristic algorithm to solve the berth scheduling problem and minimize the total cost of serving the arriving vessels at the MCT. Design/methodology/approach A universal island-based metaheuristic algorithm (UIMA) was proposed in this study, aiming to solve the spatially constrained berth scheduling problem. The UIMA population was divided into four sub-populations (i.e. islands). Unlike the canonical island-based algorithms that execute the same metaheuristic on each island, four different population-based metaheuristics are adopted within the developed algorithm to search the islands, including the following: evolutionary algorithm (EA), particle swarm optimization (PSO), estimation of distribution algorithm (EDA) and differential evolution (DE). The adopted population-based metaheuristic algorithms rely on different operators, which facilitate the search process for superior solutions on the UIMA islands. Findings The conducted numerical experiments demonstrated that the developed UIMA algorithm returned near-optimal solutions for the small-size problem instances. As for the large-size problem instances, UIMA was found to be superior to the EA, PSO, EDA and DE algorithms, which were executed in isolation, in terms of the obtained objective function values at termination. Furthermore, the developed UIMA algorithm outperformed various single-solution-based metaheuristic algorithms (including variable neighborhood search, tabu search and simulated annealing) in terms of the solution quality. The maximum UIMA computational time did not exceed 306 s. Research limitations/implications Some of the previous berth scheduling studies modeled uncertain vessel arrival times and/or handling times, while this study assumed the vessel arrival and handling times to be deterministic. Practical implications The developed UIMA algorithm can be used by the MCT operators as an efficient decision support tool and assist with a cost-effective design of berth schedules within an acceptable computational time. Originality/value A novel island-based metaheuristic algorithm is designed to solve the spatially constrained berth scheduling problem. The proposed island-based algorithm adopts several types of metaheuristic algorithms to cover different areas of the search space. The considered metaheuristic algorithms rely on different operators. Such feature is expected to facilitate the search process for superior solutions. 
    more » « less
  5. ; ; ; (, 2020 IEEE Energy Conversion Congress and Exposition (ECCE))
    null (Ed.)
    The primary purpose of this paper is to obtain accurate analytical expressions of the dual active bridge (DAB) converter under the three-degree of freedom control technique. This technique, known as triple phase-shift (TPS) modulation, is utilized for the efficiency optimization of different operating zones and modes of the DAB. Three operating zones and modes have been retained to analyze the converter. Depending on the operating regions and due to the high nonlinearity of the obtained expressions, two optimization techniques have been used. The offline particle swarm optimization (PSO) method is utilized in local optimization (LO) and results in a numerical value. The Lagrange Multiplier (LM) is utilized in global optimization (GO) and results in a closed form expression. In the case of LO, the optimal duty cycles that minimize the power loss are not the optimal values for the minimum root-mean-square (RMS) current or peak current stress. Conversely, in the case of GO, the optimal duty cycles minimize the RMS, peak current and power loss at the same time for the entire power range. Detailed analyses and simulation results from MATLAB/Simulink are given to prove the effectiveness of the proposed method. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email