Design optimizations of carbon fibre reinforced polymer isogrid lower limb prosthesis using particle swarm optimization and Lichtenberg algorithm

Francisco, Matheus Brendon; Junqueira, Diego Morais; Oliver, Guilherme Antônio; Pereira, João Luiz Junho; Cunha, Sebastião Simões da; Gomes, Guilherme Ferreira

doi:10.1080/0305215x.2020.1839442

Cited by 29 publications

(12 citation statements)

References 35 publications

(1 reference statement)

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“…MOLA as a hybrid algorithm differs from many in the literature since it combines the population and trajectory-based search approaches. The Lichtenberg algorithm (LA) was applied in several optimization problems, such as damage detection [38], designing of the isogrid lower limb prosthesis from the carbon fiber reinforced polymer [39], among others. MOLA is based on Lichtenberg's Figures, which are created utilizing the theory of diffusion-limited aggregation [40], and the different points covered by the figure are assessed.…”

Section: Multi-objective Lichtenberg Algorithm (Mola)mentioning

confidence: 99%

Multi-objective optimal allocation of multiple capacitors and distributed generators considering different load models using Lichtenberg and thermal exchange optimization techniques

Elseify

Kamel

Nasrat

2023

Neural Comput & Applic

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Integrating distributed generations (DGs) into the radial distribution system (RDS) are becoming more crucial to capture the benefits of these DGs. However, the non-optimal integration of renewable DGs and shunt capacitors may lead to several operational challenges in distribution systems, including high energy losses, poor voltage quality, reverse power flow, and lower voltage stability. Therefore, in this paper, the multi-objective optimization problem is expressed with precisely selected three conflicting goals, incorporating the reduction in both power loss and voltage deviation and improvement of voltage stability. A new index for voltage deviation called root mean square voltage is suggested. The proposed multi-objective problems are addressed using two freshly metaheuristic techniques for optimal sitting and sizing multiple SCs and renewable DGs with unity and optimally power factors into RDS, presuming several voltage-dependent load models. These optimization techniques are the multi-objective thermal exchange optimization (MOTEO) and the multi-objective Lichtenberg algorithm (MOLA), which are regarded as being physics-inspired techniques. The MOLA is inspired by the physical phenomena of lightning storms and Lichtenberg figures (LF), while the MOTEO is developed based on the concept of Newtonian cooling law. The MOLA as a hybrid algorithm differs from many in the literature since it combines the population and trajectory-based search approaches. Further, the developed methodology is implemented on the IEEE 69-bus distribution network during several optimization scenarios, such as bi- and tri-objective problems. The fetched simulation outcomes confirmed the superiority of the MOTEO algorithm in achieving accurate non-dominated solutions with fewer outliers and standard deviation among all studied metrics.

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Section: Multi-objective Lichtenberg Algorithm (Mola)mentioning

confidence: 99%

Multi-objective optimal allocation of multiple capacitors and distributed generators considering different load models using Lichtenberg and thermal exchange optimization techniques

Elseify

Kamel

Nasrat

2023

Neural Comput & Applic

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“…The Lichtenberg algorithm (LA) will be used in this step. The LA has had excellent results in several published works; for example, Francisco et al (2022) evaluated the multi-objective design optimization of the double arrowhead auxetic model using LA, fabrication and mechanical characterization of red mud-based AL2025-T6 MMC using the Lichtenberg optimization algorithm and the whale optimization algorithm (Challan et al, 2021), a powerful Lichtenberg optimization algorithm: a damage identification case study (Pereira et al, 2020), Lichtenberg optimization algorithm applied to crack tip identification in thin plate-like structures (Pereira et al, 2020) and design optimizations of carbon fiber-reinforced polymer isogrid lower limb prostheses using particle swarm optimization and the Lichtenberg algorithm (Francisco et al, 2020).…”

Section: Ec 409/10mentioning

confidence: 99%

Multi-objective design optimization of reentrant auxetic model using Lichtenberg algorithm based on metamodel

Francisco,

Pereira,

Oliveira

et al. 2023

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PurposeThe present paper aims at the multi-objective optimization of a reentrant hexagonal cell auxetic structure. In addition, a parametric analysis will be carried out to verify how each of the design factors impact each of the responses.Design/methodology/approachThe multi-objective optimization of five different responses of an auxetic model was considered: mass, critical buckling load under compression effort, natural frequency, Poisson's ratio and failure load. The response surface methodology was applied, and a new meta-heuristic of optimization called the multi-objective Lichtenberg algorithm was applied to find the optimized configuration of the model. It was possible to increase the failure load by 26.75% in compression performance optimization. Furthermore, in the optimization of modal performance, it was possible to increase the natural frequency by 37.43%. Finally, all 5 responses analyzed simultaneously were optimized. In this case, it was possible to increase the critical buckling load by 42.55%, the failure load by 28.70% and reduce the mass and Poisson's ratio by 15.97 and 11%, respectively. This paper addresses something new in the scientific world to date when evaluating in a multi-objective optimization problem, the compression and modal performance of an auxetic reentrant model.FindingsIt was possible to find multi-objective optimized structures. It was possible to increase the critical buckling load by 42.82%, and the failure load in compression performance by 26.75%. Furthermore, in the optimization of modal performance, it was possible to increase the natural frequency by 37.43%, and decrease the mass by 15.97%. Finally, all 5 responses analyzed simultaneously were optimized. In this case, it was possible to increase the critical buckling load by 42.55%, increase the failure load by 28.70% and reduce the mass and Poisson's ratio by 15.97 and 11%, respectively.Originality/valueThere is no work in the literature to date that performed the optimization of 5 responses simultaneously of a reentrant hexagonal cell auxetic structure. This paper also presents an unprecedented statistical analysis in the literature that verifies how the design factors impact each of the responses.

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“…The meta-heuristic, also developed at GEMEC, has been successful in identifying cracks (Pereira et al [2020]) and delaminations (Pereira et al [2021]), isogrid tube optimization (Francisco et al [2020]; Pereira et al [2022a]), hydrogen production optimization (Souza et al [2022]), wind forecast optimization (Tian & Wang [2022]), parameter optimization in manufacturing processes (Challan et al [2022]; Mohanty et al [2022]), and multi-objective optimization (Pereira et al [2022]), among other applications. The LA algorithm in the mono-objective version has already been well explained in previous chapters.…”

Section: Lichtenberg and Multi-objective Lichtenberg Algorithmsmentioning

confidence: 99%

Sensor Placement Optimization of a Helicopter Main Rotor Blade

Pereira¹,

Francisco²,

Jorge³

et al. 2022

Uncertainty Modeling: Fundamental Concepts and Models

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This chapter describes the importance of one of the major areas within Structural HealthMonitoring and Health and Usage Monitoring Systems: Sensor Placement Optimization (SPO). In this sense, the most important SPO metrics are presented and discussed with the reader. The methodological procedure of the optimization problem statement is also considered and discussed. he significance of rotary-wing aircraft, damages that can occur in the blade, works related to damage identification, modern methods and algorithms that have been used in this context, and a comprehensive SPO study for a helicopter main rotor blade will be presented. Finally, a new technique that considers the number of sensors as an objective associated with some of the main metrics used in SPO. The technique uses the Multiobjective Lichtenberg Algorithm and Feature Selection, an important area used in Data Mining.

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Design optimizations of carbon fibre reinforced polymer isogrid lower limb prosthesis using particle swarm optimization and Lichtenberg algorithm

Cited by 29 publications

References 35 publications

Multi-objective optimal allocation of multiple capacitors and distributed generators considering different load models using Lichtenberg and thermal exchange optimization techniques

Multi-objective optimal allocation of multiple capacitors and distributed generators considering different load models using Lichtenberg and thermal exchange optimization techniques

Multi-objective design optimization of reentrant auxetic model using Lichtenberg algorithm based on metamodel

Sensor Placement Optimization of a Helicopter Main Rotor Blade

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