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Random Point Evolution-Based Heuristic for Resource Efficiency in OCDMA Networks

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Abstract

Several practical engineering problems can be modeled as a multi-objective optimization (MOO) problems. In this paper we propose three new heuristics for MOO problems, namely based on the dominance of random points, the evolution dominance of random points, and evolution of one random point (EORP) to Pareto frontier. In particular, we explore the jointly energy efficiency (EE) and spectral efficiency (SE) of optical code division multiple access (OCDMA) communication systems. Such bi-objective optimization (BOO) heuristic approaches do not depend on merit functions, and are based on the Pareto’s dominance concept. The proposed heuristics generate, in acceptable computational time, a set of dominant solutions very close to the Pareto frontier, allowing the choice of a solution that satisfies specific interests. These heuristics are developed aiming to attain suitable EE-SE trade-offs in practical OCDMA network scenarios. Such trade-offs rely on the jointly EE and SE improvement. We first formulated the SE-EE optimization as a BOO problem with guarantee of minimum data-rate per user. As the three heuristics are applied in the context of OCDMA systems, we compare the performance-complexity of the proposed algorithms with two existing methods, the weighted sum (WS) combined with the evolutionary heuristic particle swarm optimization (PSO), and the exact optimization approach based on the sequential quadratic programming method (SQP). Extensive numerical results are carried out considering realistic OCDMA system scenarios with wide range of optical nodes, while the obtained solutions are represented in the Pareto frontier, corroborating the superiority of the proposed heuristics. Specifically, for larger OCDMA network configurations, the heuristic methods demonstrated convergence to the Pareto frontier, with clear superiority of the EORP method, attaining excellent quantity and well-distributed solution points in the Pareto front. The proposed EORP is promising, surpassing the exact WS-SQP optimization method in terms of performance-complexity trade-off. The proposed heuristics can be straightforwardly applied to any MOO problem.Kindly check and confrim the processed Abstract and Keywords are correct.It is fine. Please check and confirm the author names and initials are correct. Also, kindly confirm the details in the metadata are correct.it is correct.

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Funding

This work was supported in part by the National Council for Scientific and Technological Development (CNPq) of Brazil under Grant 10681/2019-7; in part by Londrina State University, Paraná State Government (UEL); and in part by Federal Technological University of Paraná, Cornélio Procópio Campus (UTFPR), Paraná, Brazil.

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Author notes

  1. André Luís Machado Martinez, Cristiane A. Pendeza Martinez and Taufik Abrão have contributed equally to this work.

Authors and Affiliations

  1. Academic Department of Mathematics, Universidade Tecnológica Federal do Paraná, Avenida Alberto Carazzai, 1640, Cornélio Procópio, PR, 86300-000, Brazil

    André Luís Machado Martinez & Cristiane A. Pendeza Martinez

  2. Department of Electrical Engineering, Londrina State University, Rod. Celso Garcia Cid - PR445, Po.Box 10.011, Londrina, PR, 86057-970, Brazil

    Taufik Abrão

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  1. André Luís Machado Martinez
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  3. Taufik Abrão
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All authors whose names appear on the submission Bullet made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work; Bullet drafted the work or revised it critically for important intellectual content; Bullet approved the version to be published; and Bullet agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Taufik Abrão.

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Martinez, A.L.M., Pendeza Martinez, C.A. & Abrão, T. Random Point Evolution-Based Heuristic for Resource Efficiency in OCDMA Networks. J Netw Syst Manage 31, 25 (2023). https://doi.org/10.1007/s10922-022-09711-2

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