A Decomposition based Multi-Objective Heat Transfer Search algorithm for structure optimization

Kumar, Sumit; Jangir, Pradeep; Tejani, Ghanshyam G.; Premkumar, M.

doi:10.1016/j.knosys.2022.109591

Cited by 31 publications

(15 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The benefit of over-passing wind turbine data is to guarantee the wind turbine will always operate at its actual MPP, regardless of disparities in the blades external characteristics or other influencing parameters. Though the unique characteristics drive HCS to be the optimal selection in MPPT control at various WECS environments; thus is suitable for wind speed conditions that change slowly [7], [45]. Applying large step size perturbations can enhance the convergence speed but at the negative impact of affecting efficiency which is called a trade-off.…”

Section: Development Of Hill Climbing Search Algorithm a Conventional...mentioning

confidence: 99%

Performance Evaluation of Incremental Conductance and Adaptive HCS MPPT Algorithms for WECS

Badawi,

Ali,

M. Elzein

et al. 2024

IJCDS

View full text Add to dashboard Cite

This paper presents a novel Maximum Power Point Tracking (MPPT) algorithm designed for Wind Energy Conversion Systems (WECS) to achieve optimal power extraction (P max ). The controllers employed in this study utilize a Direct Power Control (DPC) framework to assess efficiency and performance, particularly under uncertain and rapid variations in wind speed profiles. The research aims to evaluate the effectiveness of the Incremental Conductance (INC) and adaptive Hill-Climbing Search (HCS) algorithms for MPPT in WECS under such conditions. The modeling of the WECS system utilizes a Permanent Magnet Synchronous Generator (PMSG) due to its reliability and robustness. Simulation results demonstrate the significant impact of wind speed on rotor speed and electromagnetic torque, highlighting the proportional relationship between wind speed parameters and power output. The controller performance is evaluated using INC and adaptive HCS, with the latter demonstrating superior efficiency under rapid wind speed changes. Additionally, simulation results show that the INC algorithm exhibits rapid tracking capability in approaching the peak maximum power point. Overall, this study provides valuable insights into the performance of MPPT algorithms in WECS, particularly under varying wind conditions.

show abstract

Section: Development Of Hill Climbing Search Algorithm a Conventional...mentioning

confidence: 99%

Performance Evaluation of Incremental Conductance and Adaptive HCS MPPT Algorithms for WECS

Badawi,

Ali,

M. Elzein

et al. 2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…Most real-life issues belong to multi-objective optimization problems rather than a single objective due to having a mutual conflict in the objectives, for example, a minimization of mass may cause conflict with the strength of the structure, a maximization of strength causes a high price, etc. [51][52][53]. A typical multi-objective optimization problem can be formulated as follows:…”

Section: Multi-objective Reliability-based Partial Topology Optimizat...mentioning

confidence: 99%

Multi-Objective Reliability-Based Partial Topology Optimization of a Composite Aircraft Wing

2023

Self Cite

View full text Add to dashboard Cite

Reliability-based partial topology optimization (RBPTO) is a new approach for aircraft structural design; however, it brings computational complexity and makes aeroelasticity analysis quite challenging. Therefore, the present study proposed the multi-objective reliability-based partial topology optimization of a composite aircraft wing using a fuzzy-based metaheuristic (MRBPTOFBMH) approach. The objective is to obtain an optimal layout including partial topology and sizing of the aircraft wing structure. Here, an optimal aeroelastic structure is designed by taking into account the uncertain nature of material properties and the permitted transverse displacement. To increase computational efficiency in the design process, a non-probabilistic approach called a possibilistic safety index-based design optimization (PSIBDO) with fuzzy uncertainties is proposed to quantify the uncertainties due to aeroelastic and structural constraints. Various optimum partial topological shapes and sizing of aircraft wing structures with various PSI values in the range of [0.001, 1.0] have been obtained in a single optimization run. These outcomes, including deterministic and reliable optimal aircraft wing structures, demonstrate the high effectiveness of the proposed MRBPTOFBMH technique to alleviate the complexity of unconventional aircraft wing structure design. The findings also reveal the ease in cooperation of the suggested technique with a high-performance multi-objective evolutionary algorithm (MOEA) and its application in real-world multi-objective design optimization (MODO) problems with the least computational requirements against the traditional method’s multiple runs. Furthermore, the proposed methodology can generate potential aircraft wing structures in a range of m = [89.38–127.84] kg, and flutter speed = [285.61–632.78] m/s, that adhere to all the constraints requirements.

show abstract

“…Other popular multi-objective (MO) Algorithms include MO ant lion optimizer (MOALO) 43 , MO equilibrium optimizer (MOEO) 44 , MO slime mould algorithm (MOSMA) 45 , MO arithmetic optimization algorithm (MOAOA) 46 , non-dominated sorting ions motion algorithm (NSIMO) 47 , MO marine predator algorithm (MOMPA) 48 , multi-objective multi-verse optimization (MOMVO) 49 , non-dominated sorting grey wolf optimizer (NS-GWO) 50 , MO gradient-based optimizer (MOGBO) 51 , MO plasma generation optimizer (MOPGO) 52 , non-dominated sorting Harris hawks optimization (NSHHO) 53 , MO thermal exchange optimization (MOTEO) 54 , decomposition based multi-objective heat transfer search (MOHTS/D) 55 , Decomposition-Based Multi-Objective Symbiotic Organism Search (MOSOS/D) 56 , MOGNDO Algorithm 57 , Non-dominated sorting moth flame optimizer (NSMFO) 58 , Non-dominated sorting whale optimization algorithm (NSWOA) 59 , Non-Dominated Sorting Dragonfly Algorithm (NSDA) 60 , a reference vector based multiobjective evolutionary algorithm with Q-learning for operator adaptation 61 , a many-objective evolutionary algorithm based on hybrid dynamic decomposition 62 and use of two penalty values in multiobjective evolutionary algorithm based on decomposition 63 .…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

A Decomposition based Multi-Objective Heat Transfer Search algorithm for structure optimization

Cited by 31 publications

References 68 publications

Performance Evaluation of Incremental Conductance and Adaptive HCS MPPT Algorithms for WECS

Performance Evaluation of Incremental Conductance and Adaptive HCS MPPT Algorithms for WECS

Multi-Objective Reliability-Based Partial Topology Optimization of a Composite Aircraft Wing

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

Contact Info

Product

Resources

About