Multi-Objective Search Group Algorithm for engineering design problems

Huy, Truong Hoang Bao; Nallagownden, Perumal; Truong, Khoa Hoang; Kannan, Ramani; Vo, Dieu Ngoc; Ho, Nguyen

doi:10.1016/j.asoc.2022.109287

Cited by 13 publications

(3 citation statements)

References 39 publications

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“…Pareto Efficiency: Pareto optimality is a concept of efficiency used in the social sciences, including economics and political science. The Pareto optimal state is defined as a state where it is not possible to make a single objective better without making at least another one worse [55]. In engineering, it is used when more than one parameter needs to be optimized (multi-objective optimization problem).…”

Section: ) Network Optimizationmentioning

confidence: 99%

5G mmWave Network Planning Using Machine Learning for Path Loss Estimation

Santana,

Martinez Alonso,

Guillen Nieto

et al. 2024

IEEE Open J. Commun. Soc.

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New frequency bands, such as the mmWave at 28 GHz for fifth Generation networks in Frequency Range 2, are being introduced to accomplish the required throughput for new services such as remote surveillance, object tracking, and factory automation. These high-frequency bands are sensible to reflections and diffraction. Therefore, accurate path loss calculation relies on advanced models and ray tracing. However, this is time-consuming, and evaluating a large set of candidate solutions is no longer possible when planning the optimal number and location of base stations in the network planning process. This paper investigates the use of machine learning to approximate a complex mmWave ray-tracingbased path loss model in indoor scenarios. The much lower calculation time allows us to approximate the ray-tracer path loss estimation well and to apply a genetic algorithm for realizing network planning. The machine learning model is trained and validated for two buildings and tested with another, with an average of the Mean Absolute Error of 2.8 dB over all cases. It is shown that the combination of Machine Learning and Genetic Algorithm is able to find a network deployment in the FR2 band accounting for the minimum number of access points and minimum electromagnetic exposure, while still providing a predefined coverage percentage inside the building.

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Section: ) Network Optimizationmentioning

confidence: 99%

5G mmWave Network Planning Using Machine Learning for Path Loss Estimation

Santana,

Martinez Alonso,

Guillen Nieto

et al. 2024

IEEE Open J. Commun. Soc.

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“…They strive to determine the Pareto optimal solutions post the completion of the optimization. Evolutionary algorithms like genetic algorithms, particle swarm optimization and simulated annealing fall under this category, as do Multi-objective Genetic Algorithms such as NSGA-II, SPEA2, MOEA/D and NSGA-II 19 . Interactive techniques: these strategies necessitate human engagement throughout the optimization phase.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

Kalita,

Ramesh,

Cepova

et al. 2024

Sci Rep

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The exponential distribution optimizer (EDO) represents a heuristic approach, capitalizing on exponential distribution theory to identify global solutions for complex optimization challenges. This study extends the EDO's applicability by introducing its multi-objective version, the multi-objective EDO (MOEDO), enhanced with elite non-dominated sorting and crowding distance mechanisms. An information feedback mechanism (IFM) is integrated into MOEDO, aiming to balance exploration and exploitation, thus improving convergence and mitigating the stagnation in local optima, a notable limitation in traditional approaches. Our research demonstrates MOEDO's superiority over renowned algorithms such as MOMPA, NSGA-II, MOAOA, MOEA/D and MOGNDO. This is evident in 72.58% of test scenarios, utilizing performance metrics like GD, IGD, HV, SP, SD and RT across benchmark test collections (DTLZ, ZDT and various constraint problems) and five real-world engineering design challenges. The Wilcoxon Rank Sum Test (WRST) further confirms MOEDO as a competitive multi-objective optimization algorithm, particularly in scenarios where existing methods struggle with balancing diversity and convergence efficiency. MOEDO's robust performance, even in complex real-world applications, underscores its potential as an innovative solution in the optimization domain. The MOEDO source code is available at: https://github.com/kanak02/MOEDO.

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“… 33 introduces the Adaptive Geometry Estimation-based Multi-objective Differential Evolution (AGE-MODE) method, tailored for optimizing power flow in hybrid systems that include thermal, wind, and solar energy sources, effectively addressing Multi-Objective Optimal Power Flow (MOOPF) challenges with multiple objectives. Concurrently, 34 – 36 discuss the Multi-Objective Search Group Algorithm (MOSGA), an evolution of the Search Group Algorithm (SGA) that incorporates elitist non-dominated sorting techniques and crowding distance strategies. This algorithm excels at identifying Pareto optimal solutions and optimizing power flow within systems powered by renewable energy by accounting for uncertainties in wind speed and solar irradiance, with its effectiveness validated through 25 case studies and IEEE systems.…”

Section: Introductionmentioning

confidence: 99%

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

Fettah,

Guia,

Salhi

et al. 2024

Sci Rep

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In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

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Multi-Objective Search Group Algorithm for engineering design problems

Cited by 13 publications

References 39 publications

5G mmWave Network Planning Using Machine Learning for Path Loss Estimation

5G mmWave Network Planning Using Machine Learning for Path Loss Estimation

Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

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