Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Фев. 29, 2024

Abstract The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. HO is conceived by drawing inspiration from inherent behaviors observed hippopotamuses, showcasing an innovative approach metaheuristic methodology. conceptually defined using trinary-phase model that incorporates their position updating rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained top rank 115 out 161 benchmark functions finding optimal value, encompassing unimodal high-dimensional multimodal functions, fixed-dimensional as well CEC 2019 test suite 2014 dimensions 10, 30, 50, 100 Zigzag Pattern suggests demonstrates noteworthy proficiency both exploitation exploration. Moreover, it effectively balances exploration exploitation, supporting search process. In light results addressing four distinct engineering design challenges, has achieved most efficient resolution while concurrently upholding adherence to designated constraints. performance evaluation algorithm encompasses various aspects, including comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, IWO recognized extensively researched metaheuristics, AOA recently developed algorithms, CMA-ES high-performance optimizers acknowledged for success IEEE competition. According statistical post hoc analysis, determined be significantly superior investigated algorithms. source codes publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho .

Язык: Английский

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A critical problem in benchmarking and analysis of evolutionary computation methods

Nature Machine Intelligence, Год журнала: 2022, Номер 4(12), С. 1238 - 1245

Опубликована: Дек. 12, 2022

Язык: Английский

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Fuzzy-Energy-Management-Based Intelligent Direct Torque Control for a Battery—Supercapacitor Electric Vehicle

Sustainability, Год журнала: 2022, Номер 14(14), С. 8407 - 8407

Опубликована: Июль 8, 2022

This paper presents a proposed fuzzy energy management strategy developed for battery−super capacitor electric vehicle. In addition to providing different driving modes, the delivers suitable type and amount of power Furthermore, takes into account possible failures in vehicle sources. The speed torque HEV traction machine are simultaneously controlled using genetic algorithm that provides simultaneous tuning via use newly cost functions give designer ability tradeoff prioritize between design variables be minimized. simulation results show intelligent control improved vehicle’s performance terms ripple minimization. real-time is conducted RT LAB simulator, obtained correspond those numerical MATLAB/Simulink.

Язык: Английский

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Combining Lipschitz and RBF surrogate models for high-dimensional computationally expensive problems

Information Sciences, Год журнала: 2022, Номер 619, С. 457 - 477

Опубликована: Ноя. 17, 2022

Язык: Английский

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Novel metaheuristic routing algorithm with optimized energy and enhanced coverage for WSNs

Ad Hoc Networks, Год журнала: 2023, Номер 144, С. 103133 - 103133

Опубликована: Март 5, 2023

Язык: Английский

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Soft computing methods in the solution of an inverse heat transfer problem with phase change: A comparative study

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108229 - 108229

Опубликована: Март 8, 2024

Язык: Английский

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A cross-benchmark examination of feature-based algorithm selector generalization in single-objective numerical optimization

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 87, С. 101534 - 101534

Опубликована: Апрель 11, 2024

The task of selecting the best optimization algorithm for a particular problem is known as selection (AS). This involves training model using landscape characteristics to predict performance, but key challenge remains: making AS models generalize effectively new, untrained benchmark suites. study assesses models' generalizability in single-objective numerical across diverse Using Exploratory Landscape Analysis (ELA) and transformer-based (TransOpt) features, research investigates their individual combined effectiveness four benchmarks: BBOB, AFFINE, RANDOM, ZIGZAG. perform differently based on suite similarities performance distributions single-best solvers. When suites align, these underperform against baseline predicting mean performance; yet, they outperform when differ trained ELA features are better than TransOpt BBOB AFFINE suites, while opposite true RANDOM suite. Ultimately, reveals challenges accurately capturing through (ELA or TransOpt), impacting applicability.

Язык: Английский

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Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review

Computation, Год журнала: 2023, Номер 11(12), С. 245 - 245

Опубликована: Дек. 4, 2023

The significance of robot manipulators in engineering applications and scientific research has increased substantially recent years. utilization to save labor increase production accuracy is becoming a common practice industry. Evolutionary computation (EC) techniques are optimization methods that have found their use diverse fields. This state-of-the-art review focuses on developments progress for industrial robotics, especially path planning problems need satisfy various constraints implied by both the geometry its surroundings. We discuss most-used EC method modifications suit this particular purpose, as well different simulation environments used development. Lastly, we outline possible gaps expected directions future area will entail.

Язык: Английский

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An Investigation of Structural Bias in Particle Swarm Optimization

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 129 - 144

Опубликована: Янв. 1, 2025

Язык: Английский

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Hippopotamus Optimization Algorithm: A Novel Nature-Inspired Optimization Algorithm

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Ноя. 3, 2023

Abstract The novelty of this article lies in introducing a novel nonparametric metaheuristic technique named the Hippopotamus Optimization (HO) algorithm. HO is conceived by drawing inspiration from inherent behaviors observed hippopotamuses, showcasing an innovative approach methodology. conceptually defined using trinary-phase model that incorporates their position updating rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained top rank 132 out 161 benchmark functions finding optimal value, encompassing unimodal high-dimensional multimodal functions, fixed-dimensional as well CEC 2019 test suite 2014 dimensions 10, 30, 50, 100 Zigzag Pattern suggests demonstrates noteworthy proficiency both local search exploitation, global exploration. Moreover, it effectively balances exploration supporting process. performance consistently surpassed 3 algorithms achieving except for 29 functions. However, although did not exhibit strong convergence these standard deviation them was lower than other investigated algorithms, illustrating its ability to manage effectively. In light results addressing four distinct engineering design challenges, has achieved most efficient resolution while concurrently upholding adherence designated constraints. Wilcoxon signed exhibits notable statistically significant advantage over optimization problems examined study.

Язык: Английский

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