International Journal of Machine Learning and Cybernetics, Journal Year: 2025, Volume and Issue: unknown
Published: March 3, 2025
Language: Английский
PLoS ONE, Journal Year: 2024, Volume and Issue: 19(8), P. e0308474 - e0308474
Published: Aug. 19, 2024
This research article presents the Multi-Objective Hippopotamus Optimizer (MOHO), a unique approach that excels in tackling complex structural optimization problems. The (HO) is novel meta-heuristic methodology draws inspiration from natural behaviour of hippos. HO built upon trinary-phase model incorporates mathematical representations crucial aspects Hippo's behaviour, including their movements aquatic environments, defense mechanisms against predators, and avoidance strategies. conceptual framework forms basis for developing multi-objective (MO) variant MOHO, which was applied to optimize five well-known truss structures. Balancing safety precautions size constraints concerning stresses on individual sections constituent parts, these problems also involved competing objectives, such as reducing weight structure maximum nodal displacement. findings six popular methods were used compare results. Four industry-standard performance measures this comparison qualitative examination finest Pareto-front plots generated by each algorithm. average values obtained Friedman rank test analysis unequivocally showed MOHO outperformed other resolving significant quickly. In addition finding preserving more Pareto-optimal sets, recommended algorithm produced excellent convergence variance objective decision fields. demonstrated its potential navigating objectives through diversity analysis. Additionally, swarm effectively visualize MOHO's solution distribution across iterations, highlighting superior behaviour. Consequently, exhibits promise valuable method issues.
Language: Английский
Citations
36
Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(5)
Published: Jan. 15, 2025
Language: Английский
Citations
1
Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)
Published: Jan. 21, 2025
Language: Английский
Citations
1
Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(4)
Published: Feb. 5, 2025
Language: Английский
Citations
1
Computer Methods in Applied Mechanics and Engineering, Journal Year: 2025, Volume and Issue: 437, P. 117825 - 117825
Published: Feb. 9, 2025
Language: Английский
Citations
1
Forests, Journal Year: 2025, Volume and Issue: 16(2), P. 291 - 291
Published: Feb. 8, 2025
This study integrates the Backpropagation (BP) Neural Network with several optimization algorithms, namely Hippopotamus Optimization (HO), Parrot (PO), Osprey Algorithm (OOA), and Goose (GO), to develop four predictive models for adhesive strength of heat-treated wood: HO-BP, PO-BP, OOA-BP, GO-BP. These were used predict wood that was under multiple variables such as treatment temperature, time, feed rate, cutting speed, abrasive particle size. The efficacy BP neural network assessed utilizing coefficient determination (R2), error CEC test dataset. outcomes demonstrate that, relative other (HO) method shows better search convergence velocity. Furthermore, XGBoost statistically evaluate rank input variables, revealing speed (m/s) time (hours) had most significant impact on model predictions. Taken together, these demonstrated effective applicability in assessing various processing conditions practical experiments. MAE, RMSE, MAPE, R2 values HO-BP reached 0.0822, 0.1024, 1.1317, 0.9358, respectively, demonstrating superior accuracy compared models. findings support industrial process enhanced utilization.
Language: Английский
Citations
1
Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 58, P. 102240 - 102240
Published: Feb. 16, 2025
Language: Английский
Citations
1
The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(4)
Published: Feb. 17, 2025
Language: Английский
Citations
1
IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 122377 - 122400
Published: Jan. 1, 2024
Language: Английский
Citations
7
Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)
Published: Aug. 29, 2024
Supply chain efficiency is a major challenge in today's business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional strategies often struggle resources the complex dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring random search nature emphasizing that no algorithm best optimizer all applications, No Free Lunch (NFL) theorem encourages researchers design newer be able provide more effective solutions Motivated by NFL theorem, innovation novelty this paper designing new meta-heuristic called Bobcat Optimization Algorithm (BOA) imitates natural behavior bobcats wild. The basic inspiration BOA derived from hunting strategy during attack towards prey chase process between them. theory stated then mathematically modeled two phases (i) exploration based on simulation bobcat's position change while moving (ii) exploitation simulating catch prey. performance evaluated handle CEC 2017 test suite problem dimensions equal 10, 30, 50, 100, well address 2020. results show has high ability exploration, exploitation, balance them order achieve suitable solution obtained compared with twelve well-known algorithms. findings been successful handling 89.65, 79.31, 93.10, 89.65% functions dimension respectively. Also, 2020 suite, 100% suite. statistical analysis confirms significant superiority competition analyze dealing real world twenty-two constrained problems 2011 four engineering selected. 90.90% CEC2011 addition, SCM applications challenged ten case studies field sustainable lot size optimization. successfully provided superior competitor
Language: Английский
Citations
7