EABC-AS: Elite-driven artificial bee colony algorithm with adaptive population scaling

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 94, С. 101893 - 101893

Опубликована: Март 4, 2025

Язык: Английский

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Thinking Innovation Strategy (TIS): A Novel Mechanism for Metaheuristic Algorithm Design and Evolutionary Update

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113071 - 113071

Опубликована: Март 1, 2025

Язык: Английский

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A Novel Improved Gradient‐Based Optimizer for Single‐Sensor Global MPPT of PV System

Journal of Mathematics, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

Gradient‐Based Optimizer (GBO) is a highly mathematics‐based metaheuristic algorithm that has garnered significant attention since its introduction. It offers several inherent advantages, such as low computational complexity, rapid convergence, and easy implementation. However, GBO some drawbacks, including lack of population diversity tendency to get trapped in local optima. To address these shortcomings, this research introduces an improved version (iGBO). In iGBO, introducing the Sobol sequence strategy ensures higher‐quality initial enhances convergence speed. Additionally, new modified Local Escaping Operator (LEO) proposed, which incorporates sine‐cosine operator DCS/Xbest/Current‐to‐2rand strategy. This LEO improves optimization efficiency boosts search capability, helping avoid The superiority iGBO thoroughly verified through comparisons with original well‐known newly developed algorithms on IEEE CEC’2022 benchmark suite. Furthermore, proposed approach applied extract photovoltaic system’s global maximum power point (MPP) under shading conditions. Three different patterns are considered assess reliability iGBO. performance compared leading algorithms, Particle Swarm Optimization (PSO), Reptile Search Algorithm (RSA), Black Widow (BWOA), Pelican OA (POA), Chimp (ChOA), Osprey (OOA), GBO. results reveal iGBO‐based MPPT consistently outperforms competitors identifying MPP various conditions followed by PSO, while RSA performs least effectively.

Язык: Английский

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Rüppell’s fox optimizer: A novel meta-heuristic approach for solving global optimization problems

Cluster Computing, Год журнала: 2025, Номер 28(5)

Опубликована: Апрель 28, 2025

Язык: Английский

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A Multi-Objective Sine Cosine Algorithm Based on a Competitive Mechanism and Its Application in Engineering Design Problems

Biomimetics, Год журнала: 2024, Номер 9(2), С. 115 - 115

Опубликована: Фев. 15, 2024

There are a lot of multi-objective optimization problems (MOPs) in the real world, and many evolutionary algorithms (MOEAs) have been presented to solve MOPs. However, obtaining non-dominated solutions that trade off convergence diversity remains major challenge for MOEA. To this problem, paper designs an efficient sine cosine algorithm based on competitive mechanism (CMOSCA). In CMOSCA, ranking relies sorting, crowding distance rank is utilized choose outstanding agents, which employed guide evolution SCA. Furthermore, stemming from shift-based density estimation approach adopted devise new position updating operator creating offspring agents. each competition, two agents randomly selected winner competition integrated into update scheme The performance our proposed CMOSCA was first verified three benchmark suites (i.e., DTLZ, WFG, ZDT) with characteristics compared several MOEAs. experimental results indicated can obtain Pareto-optimal front better diversity. Finally, applied deal engineering design taken literature, statistical demonstrated effective problems.

Язык: Английский

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Multi-Strategy Boosted Fick’s Law Algorithm for Engineering Optimization Problems and Parameter Estimation

Biomimetics, Год журнала: 2024, Номер 9(4), С. 205 - 205

Опубликована: Март 28, 2024

To address the shortcomings of recently proposed Fick’s Law Algorithm, which is prone to local convergence and poor efficiency, we propose a multi-strategy improved Algorithm (FLAS). The method combines multiple effective strategies, including differential mutation strategy, Gaussian interweaving-based comprehensive learning seagull update strategy. First, variation strategy added in search phase increase randomness expand degree space. Second, by introducing variation, diversity increased, exploration capability efficiency are further improved. Further, that simultaneously updates individual parameters introduced improve shorten running time. Finally, stability adding global mechanism balance distribution molecules on both sides during updates. test competitiveness algorithms, exploitation FLAS validated 23 benchmark functions, CEC2020 tests. compared with other algorithms seven engineering optimizations such as reducer, three-bar truss, gear transmission system, piston rod optimization, gas compressor, pressure vessel, stepped cone pulley. experimental results verify can effectively optimize conventional optimization problems. applicability algorithm highlighted analyzing parameter estimation for solar PV model.

Язык: Английский

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Multi-strategy Hybrid Coati Optimizer: A Case Study of Prediction of Average Daily Electricity Consumption in China

Journal of Bionic Engineering, Год журнала: 2024, Номер 21(5), С. 2540 - 2568

Опубликована: Май 31, 2024

Язык: Английский

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Nonlinear optimization of optical camera multiparameter via triple integrated Gradient-based optimizer algorithm

Optics & Laser Technology, Год журнала: 2024, Номер 179, С. 111294 - 111294

Опубликована: Июнь 15, 2024

Язык: Английский

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Research on Microgrid Optimal Scheduling Based on an Improved Honey Badger Algorithm

Electronics, Год журнала: 2024, Номер 13(22), С. 4491 - 4491

Опубликована: Ноя. 15, 2024

As global energy demands continue to grow and environmental protection pressures increase, microgrids have garnered widespread attention due their ability effectively integrate distributed sources, improve utilization efficiency, enhance grid stability. Due the complexity of internal structure, variety uncertainty load demand, optimal scheduling problem becomes extremely complicated. Traditional optimization methods often perform poorly in complex dynamic microgrid environments, it is assumed that low or more simplification needed, which leads poor convergence local optimality when dealing with nonlinear problems, making intelligent algorithms a crucial solution this problem. To address shortcomings traditional honey badger algorithm, such as slow speed tendency fall into optima paper proposes multi-strategy improved algorithm. During population initialization phase, combined opposition-based learning strategy introduced algorithm’s exploration exploitation capabilities. Additionally, introduction variable spiral factors linearly decreasing for parameters improves overall efficiency algorithm reduces risk optima. further diversity, hunger search employed, providing stronger adaptability capabilities varying environments. The then applied solve multi-objective grid-connected modes. simulation results indicate enhances economic benefits operations, improving system operational

Язык: Английский

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Path planning and engineering problems of 3D UAV based on adaptive coati optimization algorithm

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

Опубликована: Дек. 28, 2024

In response to the challenges faced by Coati Optimization Algorithm (COA), including imbalance between exploration and exploitation, slow convergence speed, susceptibility local optima, low accuracy, this paper introduces an enhanced variant termed Adaptive (ACOA). ACOA achieves a balanced exploration–exploitation trade-off through refined strategies developmental methodologies. It integrates chaos mapping enhance randomness global search capabilities incorporates dynamic antagonistic learning approach employing random protons mitigate premature convergence, thereby enhancing algorithmic robustness. Additionally, prevent entrapment in Levy Flight strategy maintain population diversity, improving accuracy. Furthermore, underperforming individuals are eliminated using cosine disturbance-based differential evolution overall quality of population. The efficacy is assessed across four dimensions: balance, characteristics, diverse variations. Ablation experiments further validate effectiveness individual modules. Experimental results on CEC-2017 CEC-2022 benchmarks, along with Wilcoxon rank-sum tests, demonstrate superior performance compared COA other state-of-the-art optimization algorithms. Finally, ACOA's applicability superiority reaffirmed experimentation five real-world engineering complex urban three-dimensional unmanned aerial vehicle (UAV) path planning problem.

Язык: Английский

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