Improved multi-strategy artificial rabbits optimization for solving global optimization problems

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

Опубликована: Авг. 7, 2024

Artificial rabbits optimization (ARO) is a metaheuristic algorithm based on the survival strategy of proposed in 2022. ARO has favorable performance, but it still some shortcomings, such as weak exploitation capacity, easy to fall into local optima, and serious decline population diversity at later stage. In order solve these problems, we propose an improved multi-strategy artificial optimization, called IMARO, algorithm. this paper, roulette fitness distance balanced hiding so that can find better locations hide more reasonably. Meanwhile, improve deficiency which optimum, non-monopoly search Gaussian Cauchy operators designed ability obtain global optimal solution. Finally, covariance restart when stagnant convergence accuracy speed ARO. The performance IMARO verified by comparing original with six basic algorithms seven algorithms. results CEC2014, CEC2017, CEC2022 show good exploration effectively get rid optimum. Moreover, produces real-world engineering further demonstrating its efficiency solving challenges.

Язык: Английский

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Sine cosine algorithm with communication and quality enhancement: Performance design for engineering problems

Journal of Computational Design and Engineering, Год журнала: 2023, Номер 10(4), С. 1868 - 1891

Опубликована: Июль 4, 2023

Abstract In recent years, the sine cosine algorithm (SCA) has become one of popular swarm intelligence algorithms due to its simple and convenient structure. However, standard SCA tends fall into local optimum when solving complex multimodal tasks, leading unsatisfactory results. Therefore, this study presents with communication quality enhancement, called CCEQSCA. The proposed includes two enhancement strategies: collaboration strategy (CC) (EQ). algorithm, CC strengthens connection populations by guiding search agents closer range optimal solutions. EQ improves candidate solutions enhance exploitation algorithm. Furthermore, can explore potential in other scopes, thus strengthening ability prevent trapping optimum. To verify capability CCEQSCA, 30 functions from IEEE CEC2017 are analyzed. is compared 5 advanced original 10 variants. outcomes indicate that it dominant over comparison global optimization tasks. work paper also utilized tackle three typical engineering design problems excellent capabilities. It been experimentally demonstrated CCEQSCA works as an effective tool real issues constraints space.

Язык: Английский

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An improved Coati Optimization Algorithm with multiple strategies for engineering design optimization problems

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

Опубликована: Сен. 3, 2024

Abstract Aiming at the problems of insufficient ability artificial COA in late optimization search period, loss population diversity, easy to fall into local extreme value, resulting slow convergence and lack exploration ability; In this paper, an improved algorithm based on chaotic sequence, nonlinear inertia weight, adaptive T-distribution variation strategy alert updating is proposed enhance performance (shorted as TNTWCOA). The introduces sequence mechanism initialize position. position distribution initial solution more uniform, high quality generated, richness increased, problem poor uneven Coati Optimization Algorithm solved. phase, inertial weight factor introduced coordinate global algorithm. exploitation increase diversity individual under low fitness value improve jump out optimal value. At same time, update algorithm, so that it can within optional range. When aware danger, edge will quickly move safe area obtain a better position, while middle randomly get closer other Coatis. IEEE CEC2017 with 29 classic test functions were used evaluate speed, accuracy indicators TNTWCOA Meanwhile, was verify 4 engineering design problems, such pressure vessel welding beam design. results are compared Improved (ICOA), (COA), Golden Jackal (GJO), Osprey (OOA), Sand Cat Swarm (SCSO), Subtraction-Average-Based Optimizer (SABO). experimental show significantly improves speed accuracy, has good robustness. Three‑bar truss problem, Gear Train Design Problem, Speed reducer shows strong advantage. superior practicability verified.

Язык: Английский

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Advances in Slime Mould Algorithm: A Comprehensive Survey

Biomimetics, Год журнала: 2024, Номер 9(1), С. 31 - 31

Опубликована: Янв. 4, 2024

The slime mould algorithm (SMA) is a new swarm intelligence inspired by the oscillatory behavior of moulds during foraging. Numerous researchers have widely applied SMA and its variants in various domains field proved value conducting literatures. In this paper, comprehensive review introduced, which based on 130 articles obtained from Google Scholar between 2022 2023. study, firstly, theory described. Secondly, improved are provided categorized according to approach used apply them. Finally, we also discuss main applications SMA, such as engineering optimization, energy machine learning, network, scheduling image segmentation. This presents some research suggestions for interested algorithm, additional multi-objective discrete SMAs extending neural networks extreme learning machining.

Язык: Английский

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Sparse reconstruction of ultrasonic guided wave signals of fluid-filled pipes by multistrategy hybrid DBO-OMP using dispersive Hanning-windowed chirplet model

Measurement, Год журнала: 2024, Номер 231, С. 114648 - 114648

Опубликована: Апрель 3, 2024

Язык: Английский

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