Meta-heuristic techniques for path planning: recent trends and advancements

Sood, Monica; Panchal, Vinod Kumar

doi:10.1504/ijista.2020.10026838

Cited by 3 publications

(2 citation statements)

References 71 publications

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“…Employed bee strategy refers to sharing information with a certain flight path code array and randomly selected flight paths through the process of crossover and mutation in order to update the flight path code array represented by the nectar [18]. Every time the employed bee strategy is used for optimization, it calls the employed bee to perform a search.…”

Section: Ib-abc Algorithm 31 Single Way Point Employed Bee Strategy C...mentioning

confidence: 99%

Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

et al. 2022

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Path planning is a key research issue in the field of unmanned aerial vehicle (UAV) applications. In practical applications, multi-objective path planning is usually required for multi-UAVs, so this paper proposes the improved balanced artificial bee colony (IB-ABC) algorithm to optimize multi-objective path planning. The algorithm adopts the ABC algorithm that emphasizes the global search capability, which is based on iterative feedback information. It uses single-element points, multi-element points, and iteration constraints to optimize the strategies of employed bees, follower bees, and scout bees, respectively. In terms of time and priority, simulation experiments prove that the IB-ABC algorithm can balance local and global search capabilities, accelerate the speed of convergence, and realize multi-UAV path planning in complex mountain environments.

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Section: Ib-abc Algorithm 31 Single Way Point Employed Bee Strategy C...mentioning

confidence: 99%

Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

et al. 2022

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show abstract

“…The complexity of the chord determines the usage rate of the chord pitch: the higher the number of chords tones, the lower the usage rate, and the omitted tones will be processed. Second, the problem of vocal arrangement should be solved [ 4 ]. The weaving rules of the bass voice and the middle and high voices are not the same, so the bass voice and the middle and high voices should be divided into two parts for processing.…”

Section: Introductionmentioning

confidence: 99%

A Study of Piano-Assisted Automated Accompaniment System Based on Heuristic Dynamic Planning

Lin

Zhao

2022

Computational Intelligence and Neuroscience

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In this paper, a piano-assisted automated accompaniment system is designed and applied to a practical process using a heuristic dynamic planning approach. In this paper, we aim at the generation of piano vocal weaves in accompaniment from the perspective of assisting pop song writing, build an accompaniment piano generation tool through a set of systematic algorithm design and programming, and realize the generation of recognizable and numerous weaving styles within a controlled range under the same system. The mainstream music detection neural network approaches usually convert the problem into a similar way as image classification or sequence labelling and then use models such as convolutional neural networks or recurrent neural networks to solve the problem; however, the existing neural network approaches ignore the music relative loudness estimation subtask and ignore the inherent temporality of music data when solving the music detection task. However, the existing music generation neural network methods have not yet solved the problems of discrete integrability brought by piano roll representation music data and the still-limited control domain and variety of instruments generated in the controllable music generation task. To solve these two problems, this paper proposes a controlled music generation neural network model for multi-instrument polyphonic music. The effectiveness of the proposed model is verified by conducting several sets of experiments on the collected MIDICN data set, and the experimental results show that the model achieves better performance in the aspects of negative log-likelihood value, perplexity, musicality measure, domain similarity analysis, and manual evaluation.

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Hybrid metaheuristic approach for robot path planning in dynamic environment

Amar

Jasim

2021

Bulletin EEI

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Recently robots have gained great attention due to their ability to operate in dynamic and complex environments with moving obstacles. The path planning of a moving robot in a dynamic environment is to find the shortest and safe possible path from the starting point towards the desired target point. A dynamic environment is a robot's environment that consists of some static and moving obstacles. Therefore, this problem can be considered as an optimization problem and thus it is solved via optimization algorithms. In this paper, three approaches for determining the optimal pathway of a robot in a dynamic environment were proposed. These approaches are; the particle swarming optimization (PSO), ant colony optimization (ACO), and hybrid PSO and ACO. These used to carry out the path planning tasks effectively. A set of certain constraints must be met simultaneously to achieve the goals; the shortest path, the least time, and free from collisions. The results are calculated for the two algorithms separately and then that of the hybrid algorithm is calculated. The effectiveness and superiority of the hybrid algorithm were verified on both PSO and ACO algorithms.

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Meta-heuristic techniques for path planning: recent trends and advancements

Cited by 3 publications

References 71 publications

Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

A Study of Piano-Assisted Automated Accompaniment System Based on Heuristic Dynamic Planning

Hybrid metaheuristic approach for robot path planning in dynamic environment

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