Learning-Based Multi-agent System for Solving Combinatorial Optimization Problems: A New Architecture

Lotfi, Nasser; Acan, Adnan

doi:10.1007/978-3-319-19644-2_27

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…The suggested method HEFT-NSGA [15] was compared to HEFT [28] and CPGA algorithms [29]. Results are reported over the Gaussian Elimination and Fast Fourier Transformation graphs [34]. One more comparison metric was considered in [15], Schedule Length Ration (SLR), which is calculated as follows:…”

Section: Evaluation and Experimental Resultsmentioning

confidence: 99%

Ensemble of multi-objective metaheuristics for multiprocessor scheduling in heterogeneous distributed systems: a novel success-proportionate learning-based system

Lotfi¹

2019

SN Appl. Sci.

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Multi-objective multiprocessor scheduling problem in heterogeneous distributed systems is a remarkable NP-hard problem in parallel processing environments. This study proposes a novel ensemble system comprising six population-based metaheuristics for solving multi-objective multiprocessor scheduling problem. The novelty of this study is mainly related to proposed cooperation strategy. In proposed strategy, solutions and metaheuristics are selected based on dominancerank and success-rate values respectively. All metaheuristics work on a common population of solutions and during the running time performance of metaheuristics is evaluated and used as success rates of the metaheuristics. Choosing the next metaheuristic based on success rate values leads to the fact that better metaheuristics are used frequently while the poor ones are employed rarely. The advantage of using different metaheuristics is that each metaheuristic covers inabilities of others to discover more promising regions of solution space. Likewise to keep extracted non-dominated solutions, local archives and a common global archive are used. The performance of proposed system is evaluated using well-known benchmarks reported in state-of-the-art literature. The evaluations are done over Gaussian elimination, fast Fourier transformation and internal rate of return (IRR) task graphs. Evaluation outcomes prove the robustness of the proposed system and exhibits that ensemble system outperforms its competitors. As instance, the proposed system finds 52, 19 and 43 as the values of three objectives namely makespan, average flow time and reliability for graph presented in Fig. 2 which are better than results obtained by MFA and NSGAII. Also the results show that ensemble system works better than all individual metaheuristics in which it reaches 475, 523 and 381 as values of three objectives for IRR graph.

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Section: Evaluation and Experimental Resultsmentioning

confidence: 99%

Ensemble of multi-objective metaheuristics for multiprocessor scheduling in heterogeneous distributed systems: a novel success-proportionate learning-based system

Lotfi¹

2019

SN Appl. Sci.

Self Cite

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“…Due to the advantages of RL algorithms, researchers have explored how to implement RL in combination with metaheuristics with the objective of identifying more efficient methods for solving OR problems [50][51][52][53][54][55]. Recently, some studies have shown promising results of using RL for solving combinatorial optimization problems [56].…”

Section: Reinforcement Learning and Operations Researchmentioning

confidence: 99%

Deep Reinforcement Learning for Procedural Content Generation of 3D Virtual Environments

López

Cunningham

Ashour

et al. 2020

Journal of Computing and Information Science in Engineering

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This work presents a deep reinforcement learning (DRL) approach for procedural content generation (PCG) to automatically generate three-dimensional (3D) virtual environments that users can interact with. The primary objective of PCG methods is to algorithmically generate new content in order to improve user experience. Researchers have started exploring the use of machine learning (ML) methods to generate content. However, these approaches frequently implement supervised ML algorithms that require initial datasets to train their generative models. In contrast, RL algorithms do not require training data to be collected a priori since they take advantage of simulation to train their models. Considering the advantages of RL algorithms, this work presents a method that generates new 3D virtual environments by training an RL agent using a 3D simulation platform. This work extends the authors’ previous work and presents the results of a case study that supports the capability of the proposed method to generate new 3D virtual environments. The ability to automatically generate new content has the potential to maintain users’ engagement in a wide variety of applications such as virtual reality applications for education and training, and engineering conceptual design.

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Exploring the Potential of Agent Systems for Metaheuristics

Cuevas,

Zaldívar,

Pérez-Cisneros

2023

Intelligent Systems Reference Library

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Learning-Based Multi-agent System for Solving Combinatorial Optimization Problems: A New Architecture

Cited by 6 publications

References 16 publications

Ensemble of multi-objective metaheuristics for multiprocessor scheduling in heterogeneous distributed systems: a novel success-proportionate learning-based system

Ensemble of multi-objective metaheuristics for multiprocessor scheduling in heterogeneous distributed systems: a novel success-proportionate learning-based system

Deep Reinforcement Learning for Procedural Content Generation of 3D Virtual Environments

Exploring the Potential of Agent Systems for Metaheuristics

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