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Evolutionary Optimization of Entanglement Distillation Using Chialvo Maps
(Springer, 2023)
Ganesan, Timothy
;
Rodríguez Aguilar, Román
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Marmolejo Saucedo, José Antonio
;
Vasant, Pandian
In quantum information theory, entanglement distillation is a key component for designing quantum computer networks and quantum repeaters. In this work, the practical entanglement distillation problem is re-designed in a bilevel optimization framework. The primary goal of this work is to propose and test an effective optimization technique that combines evolutionary algorithms (differential evolution) and the Chialvo map - for solving the bilevel practical entanglement distillation problem. The primary idea is to leverage on the complex dynamical behavior of Chialvo maps to improve the optimization capabilities of the evolutionary algorithm. Analysis on the computational results and comparisons with a standard evolutionary algorithm implementation is presented. ©2023 springer, The authors.
Scopus© Citations 1 50
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Swarm-based intelligent strategies for charging plug-in hybrid electric vehicles
(2022)
Vasant, Pandian
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Banik, Anirban
;
Thomas, J. Joshua
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Marmolejo Saucedo, José Antonio
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Ganesan, Timothy
Presently, there is a significant emphasis on green technology in order to increase the usage of clean energy sources in the transportation sector while also reducing emissions. At this phase, a sufficient charge allocation strategy is needed to use plug-in hybrid power vehicles (PHEVs), including the implementation of smart charging infrastructure and intelligent grid networks. Daytime charging stations are needed for PHEV regular use, and at this stage, only adequate charging control and infrastructure management will contribute to broader PHEV adoption. The researchers are attempting to establish an effective control system for filling as well as promoting the penetration of upcoming PHEVs on highways. In this case, intelligent energy management necessitates the creation of statistical models over optimization strategy focused on computer intelligence. The state of charge of PHEVs was optimized employing particle swarm optimization (PSO), gravitational search algorithm (GSA), accelerated particle swarm optimization (APSO), and a combined form of PSO and GSA (PSOGSA). In this perspective, the individual and comparative performance of four techniques was defined in terms of convergence speed, computation time, and best fitness. © 2022 Elsevier Inc. All rights reserved.
Scopus© Citations 4 9 1
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Review on recent implementations of multiobjective and multilevel optimization in sustainable energy economics
(2022)
Ganesan, Timothy
;
Litvinchev, Igor
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Marmolejo Saucedo, José Antonio
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Thomas, J. Joshua
;
Vasant, Pandian
Rapid progress is currently being made globally in the sustainable energy industry. This trend has been seen to concentrate on specific focus areas in the global energy ecosystem. The integration of sustainability ideas into the existing energy ecosystem has given rise to various complexities, e.g., multilevel and multiobjective (MO) scenarios. This in return has generated various avenues for the implementation of mathematical optimization as well as state-of-the-art operations research methodologies on such real-world systems. This chapter aims to provide a concise review on recent implementations of MO and multilevel optimization on sustainable energy economic systems. Three key industrial areas are given emphasis—economic load/emission dispatch, bioenergy supply chains, and sustainable capacity planning. © 2022 Elsevier Inc. All rights reserved.
Scopus© Citations 1 18 1

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