Forest Fire Ash Optimizer (FFA): A Novel Physics-based Metaheuristic Algorithm for Implementing Exploration-Exploitation Flexible Regulation DOI
Baisen Lin, Yu Song, Jigang Wang

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 30, 2024

Abstract This study proposes a novel physics-inspired metaheuristic algorithm named Forest Fire Ash Optimizer (FFA). Inspired by the characteristics of ash movement in different forest fire burning stages, intelligently transformed these regular behaviors into variety unique algorithmic mechanisms, including four-population mechanism based on fitness and denseness partitioning, an inverse proportionality selection for elite influence, strategy grounded actual physical phenomena. These components complement each other to enable flexible regulation exploration exploitation, i.e., two phases are not merely sequential, but change dynamically depending search status FFA agents. To verify effectiveness proposed algorithm, is qualitatively analyzed using CEC-2022 test suite. Additionally, classical standard suite, CEC-2017 suite conducted compare performance with 9 advanced algorithms. The results demonstrate that excels performance, exhibiting high stability, flexibility, robustness. Finally, applied challenging real-world engineering optimization problems. indicate that, compared competing algorithms, provides superior more solutions, predicting its potential in-depth applications fields.

Language: Английский

Optimization based on the smart behavior of plants with its engineering applications: Ivy algorithm DOI
Mojtaba Ghasemi, Mohsen Zare, Pavel Trojovský

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 295, P. 111850 - 111850

Published: April 22, 2024

Language: Английский

Citations

42
Optimal truss design with MOHO: A multi-objective optimization perspective DOI Creative Commons
Nikunj Mashru, Ghanshyam G. Tejani, Pinank Patel

et al.

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

42
Flood algorithm (FLA): an efficient inspired meta-heuristic for engineering optimization DOI
Mojtaba Ghasemi, Keyvan Golalipour, Mohsen Zare

et al.

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 80(15), P. 22913 - 23017

Published: July 1, 2024

Language: Английский

Citations

40
Dollmaker Optimization Algorithm: A Novel Human-Inspired Optimizer for Solving Optimization Problems DOI Open Access

Saleh Al Omari,

Khalid Kaabneh,

I. Abu-Falahah

et al.

International journal of intelligent engineering and systems, Journal Year: 2024, Volume and Issue: 17(3), P. 816 - 828

Published: May 3, 2024

In this article, a new human-based metaheuristic algorithm named Dollmaker Optimization Algorithm (DOA) is introduced, which imitates the strategy and skill of dollmaker when making dolls.The basic inspiration DOA derived from two natural behaviors in doll process (i) general changes to dollmaking materials (ii) precise small on appearance characteristics theory proposed then modeled mathematically phases exploration based simulation large made doll-making exploitation performance optimization evaluated twenty-three standard benchmark functions unimodal, high-dimensional multimodal, fixed-dimensional multimodal types.The results show that has achieved suitable for problems with its ability exploration, exploitation, balance them during search process.Comparison twelve competing algorithms shows superior compared by providing better all getting rank first best optimizer.In addition, efficiency handling real-world applications four engineering design problems.Simulation acceptable real world values variables objective algorithms.

Language: Английский

Citations

20
An Improved Bio-Inspired Material Generation Algorithm for Engineering Optimization Problems Including PV Source Penetration in Distribution Systems DOI Creative Commons
Mona Gafar, Shahenda Sarhan, Ahmed R. Ginidi

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 603 - 603

Published: Jan. 9, 2025

The Material Generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore refine parameter space. By simulating bonding processes—such as ionic covalent bonds—MGO generates new solution candidates evaluates their stability, guiding toward convergence on optimal values. To improve its search efficiency, this paper introduces Enhanced (IMGO) algorithm, integrates a Quadratic Interpolated Learner Process (QILP). Unlike conventional random selection, QILP strategically selects three distinct compounds, resulting in increased diversity, more thorough exploration space, improved resistance local optima. adaptable non-linear adjustments QILP’s quadratic function allow traverse complex landscapes effectively. This IMGO, along with original MGO, developed support applications across phases, showcasing versatility enhanced optimization capabilities. Initially, both MGO algorithms are evaluated using several mathematical benchmarks from CEC 2017 test suite measure Following this, applied following well-known engineering problems: welded beam design, rolling element bearing pressure vessel design. simulation results then compared various established bio-inspired algorithms, including Artificial Ecosystem (AEO), Fitness–Distance-Balance AEO (FAEO), Chef-Based Algorithm (CBOA), Beluga Whale (BWOA), Arithmetic-Trigonometric (ATOA), Atomic Orbital Searching (AOSA). Moreover, IMGO tested real Egyptian power distribution system optimize placement PV capacitor units aim minimizing energy losses. Lastly, parameters estimation problem successfully solved via considering commercial RTC France cell. Comparative studies demonstrate that not only achieves significant loss reduction but also contributes environmental sustainability reducing emissions, overall effectiveness practical applications. outcomes 23 benchmark models average accuracy enhancement 65.22% consistency 69.57% method. Also, application achieved computational errors 27.8% while maintaining superior stability alternative methods.

Language: Английский

Citations

6
Parameters identification of photovoltaic models using Lambert W-function and Newton-Raphson method collaborated with AI-based optimization techniques: A comparative study DOI Creative Commons
Mohamed Abdel‐Basset, Reda Mohamed, Ibrahim M. Hezam

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124777 - 124777

Published: July 14, 2024

Accurately estimating the unknown parameters of photovoltaic (PV) models based on measured voltage-current data is a challenging optimization problem due to its high nonlinearity and multimodality. An accurate solution this essential for efficiently simulating, controlling, evaluating PV systems. There are three different models, including single-diode model, double-diode triple-diode with five, seven, nine parameters, respectively, proposed represent electrical characteristics systems varying levels complexity accuracy. In literature, several deterministic metaheuristic algorithms have been used accurately solve hard problem. However, problem, methods could not achieve solutions. On other side, algorithms, also known as gradient-free methods, somewhat good solutions but they still need further improvements strengthen their performance against stuck-in local optima slow convergence speed problems. Over last two years, recent better improve avoid tackle continuous majority those has investigated. Therefore, in paper, nineteen recently published such Mantis search algorithm (MSA), spider wasp optimizer (SWO), light spectrum (LSO), growth (GO), walrus (WAOA), hippopotamus (HOA), black-winged kite (BKA), quadratic interpolation (QIO), sinh cosh (SCHA), exponential distribution (EDO), optical microscope (OMA), secretary bird (SBOA), Parrot Optimizer (PO), Newton-Raphson-based (NRBO), crested porcupine (CPO), differentiated creative (DCS), propagation (PSA), one-to-one (OOBO), triangulation topology aggregation (TTAO), studied clarify effectiveness models. addition, collaborate functions, namely Lambert W-Function Newton-Raphson Method, aid solving I-V curve equations more accurately, thereby improving Those assessed using four well-known solar cells modules compared each metrics, best fitness, average worst standard deviation (SD), Friedman mean rank, speed; multiple-comparison test compare difference between ranks. Results comparison show that SWO efficient effective SDM, DDM, TDM over modules, Method equations. study reports perform poorly when applied

Language: Английский

Citations

10
Hyperbolic Sine Optimizer: a new metaheuristic algorithm for high performance computing to address computationally intensive tasks DOI

Shivankur Thapliyal,

Narender Kumar

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(5), P. 6703 - 6772

Published: March 7, 2024

Language: Английский

Citations

6
Multi scenario chaotic transient search optimization algorithm for global optimization technique DOI Creative Commons

Ibrahim Mohamed Diaaeldin,

Hany M. Hasanien, Mohammed H. Qais

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 4, 2025

Recently, chaotic maps (CMs) have been employed in many optimization algorithms as a motivator to find better solution non-convex engineering problems since they can avoid local optima and the near-optimal rapidly. In this article, metaheuristic, physics-based algorithm called transient search (CTSO) is developed solve 23 benchmark functions, including uni- multi-modal functions. Nine CMs integrated into TSO improve its capabilities by applying various scenarios for improving random numbers. Further, proposed CTSO was compared with original using Wilcoxon p-value test, non-parametric sign t-test, convergence curves, elapsed time. Furthermore, has solving real-life design problems, coil spring, welded beam, pressure vessel design, where performed than some recent finding best design.

Language: Английский

Citations

0
Comprehensive performance metric for bio-inspired optimizers: a generation-wise population convergence approach toward global optimality DOI

Shivankur Thapliyal,

Narender Kumar

Iran Journal of Computer Science, Journal Year: 2025, Volume and Issue: unknown

Published: March 14, 2025

Language: Английский

Citations

0
Enhanced snow ablation optimizer using advanced quadratic and newton interpolation with taylor neighbourhood search and second-order differential perturbation strategies for high-dimensional feature selection DOI

Shivankur Thapliyal,

Narender Kumar

Evolutionary Intelligence, Journal Year: 2025, Volume and Issue: 18(2)

Published: March 27, 2025

Language: Английский

Citations

0