Red-billed blue magpie optimizer: a novel metaheuristic algorithm for 2D/3D UAV path planning and engineering design problems

Artificial Intelligence Review, Год журнала: 2024, Номер 57(6)

Опубликована: Май 3, 2024

Abstract Numerical optimization, Unmanned Aerial Vehicle (UAV) path planning, and engineering design problems are fundamental to the development of artificial intelligence. Traditional methods show limitations in dealing with these complex nonlinear models. To address challenges, swarm intelligence algorithm is introduced as a metaheuristic method effectively implemented. However, existing technology exhibits drawbacks such slow convergence speed, low precision, poor robustness. In this paper, we propose novel approach called Red-billed Blue Magpie Optimizer (RBMO), inspired by cooperative efficient predation behaviors red-billed blue magpies. The mathematical model RBMO was established simulating searching, chasing, attacking prey, food storage magpie. demonstrate RBMO’s performance, first conduct qualitative analyses through behavior experiments. Next, numerical optimization capabilities substantiated using CEC2014 (Dim = 10, 30, 50, 100) CEC2017 suites, consistently achieving best Friedman mean rank. UAV planning applications (two-dimensional three − dimensional), obtains preferable solutions, demonstrating its effectiveness solving NP-hard problems. Additionally, five problems, yields minimum cost, showcasing advantage practical problem-solving. We compare our experimental results categories widely recognized algorithms: (1) advanced variants, (2) recently proposed algorithms, (3) high-performance optimizers, including CEC winners.

Язык: Английский

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Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications

Biomimetics, Год журнала: 2024, Номер 9(5), С. 291 - 291

Опубликована: Май 13, 2024

The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, unsatisfactory speed when facing complex problems. In response, this paper proposes the multi-strategy improved algorithm (MDBO). core improvements include using Latin hypercube sampling better initialization introduction of novel differential variation strategy, termed "Mean Differential Variation", enhance algorithm's ability evade optima. Moreover, strategy combining lens imaging reverse learning dimension-by-dimension was proposed applied current optimal solution. Through comprehensive performance testing on standard benchmark functions CEC2017 CEC2020, MDBO demonstrates superior in terms accuracy, stability, compared with other classical metaheuristic algorithms. Additionally, efficacy addressing real-world engineering problems validated through three representative application scenarios namely extension/compression spring design problems, reducer welded beam

Язык: Английский

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A novel MPPT technology based on dung beetle optimization algorithm for PV systems under complex partial shade conditions

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

Опубликована: Март 18, 2024

Abstract Solar power is a renewable energy source, and its efficient development utilization are important for achieving global carbon neutrality. However, partial shading conditions cause the output of PV systems to exhibit nonlinear multipeak characteristics, resulting in loss power. In this paper, we propose novel Maximum Power Point Tracking (MPPT) technique based on Dung Beetle Optimization Algorithm (DBO) maximize under various weather conditions. We performed performance comparison analysis DBO with existing renowned MPPT techniques such as Squirrel Search Algorithm, Cuckoo search Optimization, Horse Herd Particle Swarm Adaptive Factorized Gray Wolf Hybrid Nelder-mead. The experimental validation carried out HIL + RCP physical platform, which fully demonstrates advantages terms tracking speed accuracy. results show that proposed achieves 99.99% maximum point (GMPP) efficiency, well improvement 80% convergence rate stabilization rate, 8% average A faster, more robust GMPP significant contribution controller.

Язык: Английский

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A Halton Enhanced Solution-based Human Evolutionary Algorithm for Complex Optimization and Advanced Feature Selection Problems

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113062 - 113062

Опубликована: Янв. 1, 2025

Язык: Английский

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Improved Dung Beetle Optimizer Algorithm With Multi-Strategy for Global Optimization and UAV 3D Path Planning

IEEE Access, Год журнала: 2024, Номер 12, С. 69240 - 69257

Опубликована: Янв. 1, 2024

In high-dimensional scenarios, path planning is a challenging and computationally complex optimization task that requires finding optimal paths within domains. Metaheuristic (MH) algorithms offer practical approach to addressing this issue. The Dung Beetle Optimizer (DBO), categorized as MH algorithm, takes inspiration from the biological behaviors exhibited by dung beetles. However, DBO exhibits limitations, including inadequate global search capabilities tendency converge on local optima. To address these challenges, paper proposes multi-strategy Improved Optimization algorithm (IDBO) for UAV 3D planning. Initially, cubic chaos mapping applied population initialization, enhancing diversity. Subsequently, novel exploration strategy replaces DBO's original rolling phase, improving information exchange minimizing parameter dependence. Third, an adaptive t-distribution introduced adjust beetle positions, balancing exploitation. Finally, enhanced update proposed, utilizing varied behavioral logic at different stages improve solution quality efficiency. Additionally, performance comparisons with six advanced CEC2017 test suite, validation of IDBO's effectiveness via Wilcoxon rank-sum Friedman mean rank test. Meanwhile, in experiment, IDBO achieves best cost index, which 1.34% higher than DBO, also significantly better most such WOA, GSA, HHO, COA, standard deviation reduced 99.93% compared proves robustness

Язык: Английский

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Robot path planning based on improved dung beetle optimizer algorithm

Journal of the Brazilian Society of Mechanical Sciences and Engineering, Год журнала: 2024, Номер 46(4)

Опубликована: Март 19, 2024

Язык: Английский

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Dung Beetle Optimization Algorithm Based on Improved Multi-Strategy Fusion

Electronics, Год журнала: 2025, Номер 14(1), С. 197 - 197

Опубликована: Янв. 5, 2025

The Dung Beetle Optimization Algorithm (DBO) is characterized by its great convergence accuracy and quick speed. However, like other swarm intelligent optimization algorithms, it also has the disadvantages of having an unbalanced ability to explore world use local resources, as well being prone settling into optimal search in latter stages optimization. In order address these issues, this research suggests a multi-strategy fusion dung beetle method (MSFDBO). To enhance quality first solution, refractive reverse learning technique expands algorithm space stage. algorithm’s increased adding adaptive curve control population size prevent from reaching optimum. improve balance exploitation global exploration, respectively, triangle wandering strategy subtractive averaging optimizer were later added Rolling Breeding Beetle. Individual beetles will congregate at current position, which near value, during last stage MSFDBO; however, value could not be value. Thus, variationally perturb solution (so that leaps out final MSFDBO) algorithmic performance (generally specifically, effect optimizing search), Gaussian–Cauchy hybrid variational perturbation factor introduced. Using CEC2017 benchmark function, MSFDBO’s verified comparing seven different intelligence algorithms. MSFDBO ranks terms average performance. can lower labor production expenses associated with welding beam reducer design after testing two engineering application challenges. When comes lowering manufacturing costs overall weight, outperforms methods.

Язык: Английский

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A multi-strategy enhanced Dung Beetle Optimization for real-world engineering problems and UAV path planning

Alexandria Engineering Journal, Год журнала: 2025, Номер 118, С. 406 - 434

Опубликована: Янв. 24, 2025

Язык: Английский

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Enhanced Dung Beetle Optimization Algorithm for Practical Engineering Optimization

Mathematics, Год журнала: 2024, Номер 12(7), С. 1084 - 1084

Опубликована: Апрель 3, 2024

An enhanced dung beetle optimization algorithm (EDBO) is proposed for nonlinear problems with multiple constraints in manufacturing. Firstly, the rolling phase improved by removing worst value interference and coupling current solution optimal to each other, while retaining advantages of original formulation. Subsequently, address problem that dancing focuses only on information solution, which leads overly stochastic inefficient exploration space, globally introduced steer beetle, a factor added solution. Finally, foraging introduces Jacobi curve further enhance algorithm’s ability jump out local optimum avoid phenomenon premature convergence. The performance EDBO tested using CEC2017 function set, significance verified Wilcoxon rank-sum test Friedman test. experimental results show has strong optimization-seeking accuracy stability. By solving four engineering varying degrees, proven have good adaptability robustness.

Язык: Английский

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DEMFFA: a multi-strategy modified Fennec Fox algorithm with mixed improved differential evolutionary variation strategies

Journal Of Big Data, Год журнала: 2024, Номер 11(1)

Опубликована: Май 8, 2024

Abstract The Fennec Fox algorithm (FFA) is a new meta-heuristic that primarily inspired by the fox's ability to dig and escape from wild predators. Compared with other classical algorithms, FFA shows strong competitiveness. “No free lunch” theorem an has different effects in face of problems, such as: when solving high-dimensional or more complex applications, there are challenges as easily falling into local optimal slow convergence speed. To solve this problem FFA, paper, improved Fenna fox DEMFFA proposed adding sin chaotic mapping, formula factor adjustment, Cauchy operator mutation, differential evolution mutation strategies. Firstly, mapping strategy added initialization stage make population distribution uniform, thus speeding up Secondly, order expedite speed algorithm, adjustments made factors whose position updated first stage, resulting faster convergence. Finally, prevent getting too early expand search space population, after second stages original update. In verify performance DEMFFA, qualitative analysis carried out on test sets, tested newly algorithms three sets. And we also CEC2020. addition, applied 10 practical engineering design problems 24-bar truss topology optimization problem, results show potential problems.

Язык: Английский

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