Optimizing daylight in west-facing facades for LEED V4.1 compliance using metaheuristic approach DOI Creative Commons
Vu Hong Son Pham,

Vo Thi Bich Huyen

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Dec. 11, 2023

Abstract This study introduces an optimized design approach for west-facing room façades to improve daylighting while adhering LEED v4.1 sustainability criteria. Employing parametric modeling, metaheuristic optimization, and validated daylight simulations, the research highlights African Vulture Optimization Algorithm's success in achieving 100% compliance superior performance over random models sufficiency glare reduction. Light-colored materials transparent glazing emerged as beneficial points. Despite computational limitations need empirical validation, this method offers architects versatile sustainable solutions. Comparative analysis reveals algorithm's strong performance, although opportunities exist refinement. Future directions include contrasting algorithm with other optimization methods, focusing on backing, assessing environmental human-centric impacts, adapting varied building types conditions, examining diverse geographical material factors. work advances daylight-integrated façade design, suggesting a more comprehensive framework optimization.

Language: Английский

A novel hippo swarm optimization: for solving high-dimensional problems and engineering design problems DOI Creative Commons
Guoyuan Zhou,

Jiaxuan Du,

Jia Guo

et al.

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(3), P. 12 - 42

Published: April 10, 2024

Abstract In recent years, scholars have developed and enhanced optimization algorithms to tackle high-dimensional engineering challenges. The primary challenge of lies in striking a balance between exploring wide search space focusing on specific regions. Meanwhile, design problems are intricate come with various constraints. This research introduces novel approach called Hippo Swarm Optimization (HSO), inspired by the behavior hippos, designed address real-world HSO encompasses four distinct strategies based hippos different scenarios: starvation search, alpha margination, competition. To assess effectiveness HSO, we conducted experiments using CEC2017 test set, featuring highest dimensional problems, CEC2022 constrained problems. parallel, employed 14 established as control group. experimental outcomes reveal that outperforms well-known algorithms, achieving first average ranking out them CEC2022. Across classical consistently delivers best results. These results substantiate highly effective algorithm for both

Language: Английский

Citations

6
Enhancing optimal sizing of stand-alone hybrid systems with energy storage considering techno-economic criteria based on a modified artificial rabbits optimizer DOI
Abdelazim G. Hussien, Hoda Abd El-Sattar, Fatma A. Hashim

et al.

Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 78, P. 109974 - 109974

Published: Dec. 20, 2023

Language: Английский

Citations

11
Strategies for Humanitarian Logistics and Supply Chain in Organizational Contexts: Pre- and Post-Disaster Management Perspectives DOI Creative Commons
Amir Aghsami, Simintaj Sharififar, Nader Markazi Moghaddam

et al.

Systems, Journal Year: 2024, Volume and Issue: 12(6), P. 215 - 215

Published: June 18, 2024

Every organization typically comprises various internal components, including regional branches, operations centers/field offices, major transportation hubs, and operational units, among others, housing a population susceptible to disaster impacts. Moreover, organizations often possess resources such as staff, vehicles, medical facilities, which can mitigate human casualties address needs across affected areas. However, despite the importance of managing disasters within organizational networks, there remains research gap in development mathematical models for scenarios, specifically incorporating offices external stakeholders relief centers. Addressing this gap, study examines an optimization model both before after planning humanitarian supply chain logistical framework organization. The areas are defined stakeholders, facilities. A mixed-integer nonlinear is formulated minimize overall costs, considering factors penalty costs untreated injuries demand, delays rescue item distribution operations, waiting injured emergency vehicles air ambulances. implemented using GAMS software 47.1.0 test problems different scales, with Grasshopper Optimization Algorithm proposed larger-scale scenarios. Numerical examples provided show effectiveness feasibility validate metaheuristic approach. Sensitivity analysis conducted assess model’s performance under conditions, key managerial insights implications discussed.

Language: Английский

Citations

4
Improved African Vulture Optimization Algorithm Based on Random Opposition-Based Learning Strategy DOI Open Access

Xingsheng Kuang,

Junfa Hou,

Xiaotong Liu

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(16), P. 3329 - 3329

Published: Aug. 22, 2024

This paper proposes an improved African vulture optimization algorithm (IROAVOA), which integrates the random opposition-based learning strategy and disturbance factor to solve problems such as relatively weak global search capability poor ability balance exploration exploitation stages. IROAVOA is divided into two parts. Firstly, introduced in population initialization stage improve diversity of population, enabling more comprehensively explore potential solution space convergence speed algorithm. Secondly, at increase randomness algorithm, effectively avoiding falling local optimal allowing a better To verify effectiveness proposed comprehensive testing was conducted using 23 benchmark test functions, CEC2019 suite, engineering problems. The compared with seven state-of-the-art metaheuristic algorithms experiments five experiments. experimental results indicate that achieved mean values all functions significant improvement speed. It can also than other algorithms.

Language: Английский

Citations

4
The Crossover strategy integrated Secretary Bird Optimization Algorithm and its application in engineering design problems DOI Creative Commons
Xiongfa Mai,

Yan Zhong,

Ling Li

et al.

Electronic Research Archive, Journal Year: 2025, Volume and Issue: 33(1), P. 471 - 512

Published: Jan. 1, 2025

<p>An improved metaheuristic algorithm called the Crossover strategy integrated Secretary Bird Optimization Algorithm (CSBOA) is proposed in this work for solving real optimization problems. This logistic-tent chaotic mapping initialization, an differential mutation operator, and crossover strategies with (SBOA) a better quality solution faster convergence. To evaluate performance of CSBOA, two sets standard benchmark set, CEC2017 CEC2022, were applied first. The Wilcoxon rank sum test Friedman also used to statistically compare CSBOA seven common metaheuristics. comparisons demonstrated that more competitive than other algorithms on most functions. Additionally, was validated challenging engineering design case studies. Comparative results showed provides accurate solutions SBOA algorithms, suggesting viability dealing global problems.</p>

Language: Английский

Citations

0
The Crossover strategy integrated Secretary Bird Optimization Algorithm and its application in engineering design problems DOI Creative Commons
Xiongfa Mai,

Yan Zhong,

Ling Li

et al.

Electronic Research Archive, Journal Year: 2025, Volume and Issue: 33(1), P. 471 - 512

Published: Jan. 1, 2025

<p>An improved metaheuristic algorithm called the Crossover strategy integrated Secretary Bird Optimization Algorithm (CSBOA) is proposed in this work for solving real optimization problems. This logistic-tent chaotic mapping initialization, an differential mutation operator, and crossover strategies with (SBOA) a better quality solution faster convergence. To evaluate performance of CSBOA, two sets standard benchmark set, CEC2017 CEC2022, were applied first. The Wilcoxon rank sum test Friedman also used to statistically compare CSBOA seven common metaheuristics. comparisons demonstrated that more competitive than other algorithms on most functions. Additionally, was validated challenging engineering design case studies. Comparative results showed provides accurate solutions SBOA algorithms, suggesting viability dealing global problems.</p>

Language: Английский

Citations

0
Multi-objective Particle Swarm Optimization Algorithm for Task Allocation and Archived Guided Mutation Strategies DOI

Jianjie Chen,

Yanmin Liu, Yi Luo

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

Abstract In this paper, we propose a novel multi-objective particle swarm optimization algorithm with task allocation and archive-guided mutation strategy (TAMOPSO), which effectively solves the problem of inefficient search in traditional algorithms by assigning different evolutionary tasks to particles characteristics. First, TAMOPSO divides multiple subpopulations according distribution status each iteration population designs new mechanism improve efficiency. Second, adopts an adaptive Lévy flight growth rate, automatically increasing global variation probability expand range when converges enhancing local conduct fine disperses realize dynamics variations. Finally, measures contribution through evolution rate index filters out valuable historical solutions for subsequent reuse accelerate convergence speed; addition, improves individual optimal selection mechanism, changes bias algorithm, ensures that has equal opportunity, enhances fairness process. The process is enhanced at same time. performance compared ten existing on 22 standard test problems, experimental results show outperforms other several problems better solving problems.

Language: Английский

Citations

0
Quantum-inspired African vultures optimization algorithm with elite mutation strategy for production scheduling problems DOI Creative Commons
Bo Liu, Yongquan Zhou, Qifang Luo

et al.

Journal of Computational Design and Engineering, Journal Year: 2023, Volume and Issue: 10(4), P. 1767 - 1789

Published: July 4, 2023

Abstract The production scheduling (PS) problem is a challenging task that involves assigning manufacturing resources to jobs while ensuring all constraints are satisfied. key difficulty in PS determining the appropriate order of operations. In this study, we propose novel optimization algorithm called quantum-inspired African vultures with an elite mutation strategy (QEMAVOA) address issue. QEMAVOA enhanced version vulture incorporates three new improvement strategies. Firstly, enhance QEMAVOA’s diversification ability, population diversity enriched by introduction quantum double-chain encoding initialization phase QEMAVOA. Secondly, implementation rotating gate will balance and exploitation capabilities, leading better solution. Finally, purpose improving exploitability QEMAVOA, introduced. To evaluate performance apply it two benchmark problems: flexible job shop parallel machine scheduling. results compared those existing algorithms literature. test reveal surpasses comparison accuracy, stability, speed convergence.

Language: Английский

Citations

9
Sand cat arithmetic optimization algorithm for global optimization engineering design problems DOI Creative Commons
Shuilin Chen, Jianguo Zheng

Journal of Computational Design and Engineering, Journal Year: 2023, Volume and Issue: 10(6), P. 2122 - 2146

Published: Oct. 17, 2023

Abstract Sand cat swarm optimization (SCSO) is a recently introduced popular intelligence metaheuristic algorithm, which has two significant limitations – low convergence accuracy and the tendency to get stuck in local optima. To alleviate these issues, this paper proposes an improved SCSO based on arithmetic algorithm (AOA), refracted opposition-based learning crisscross strategy, called sand (SC-AOA), AOA balance exploration exploitation reduce possibility of falling into optimum, used strategy enhance accuracy. The effectiveness SC-AOA benchmarked 10 benchmark functions, CEC 2014, 2017, 2022, eight engineering problems. results show that competitive performance.

Language: Английский

Citations

9
AEOWOA: hybridizing whale optimization algorithm with artificial ecosystem-based optimization for optimal feature selection and global optimization DOI
Reham R. Mostafa, Abdelazim G. Hussien,

Marwa A. Gaheen

et al.

Evolving Systems, Journal Year: 2024, Volume and Issue: 15(5), P. 1753 - 1785

Published: May 15, 2024

Language: Английский

Citations

3