Optimum Mobile Robot Path Planning Using Improved Artificial Bee Colony Algorithm and Evolutionary Programming

Kumar, Sunil; Sikander, Afzal

doi:10.1007/s13369-021-06326-8

Cited by 35 publications

(10 citation statements)

References 49 publications

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“…Teaching Learning-based Optimization (TLBO) [12], Imperialist Competitive Algorithm (ICA) [13], and Poor and Rich Optimization (PRO) [14] are three prominent members of the human-based algorithms simulating essential features of the socio-cultural interaction between human individuals living in a specific community. Swarm-based algorithms or Swarm Intelligence (SI) methods constitute a large portion of metaheuristic algorithms in the literature and finds their application within a broad range of engineering field, from the design of solar PV modules [15] to mobile robot path planning [16]. SI-based methods are generally inspired by the biological approaches taking place in nature and imitate the social collective behaviors of animals.…”

Section: Introductionmentioning

confidence: 99%

Quasi-dynamic opposite learning enhanced Runge-Kutta optimizer for solving complex optimization problems

Turgut,

Turgut

2024

Evol. Intel.

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The Runge-Kutta Optimization (RUNGE) algorithm is a recently proposed metaphor-free metaheuristic optimizer borrowing practical mathematical foundations of the famous Runge-Kutta differential equation solver. Despite its relatively new emergence, this algorithm has several applications in various branches of scientific fields. However, there is still much room for improvement as it suffers from premature convergence resulting from inefficient search space exploration. To overcome this algorithmic drawback, this research study proposes a brand-new quasidynamic opposition-based learning (QDOPP) mechanism to be implemented in a standard Runge-Kutta optimizer to eliminate the local minimum points over the search space. Enhancing the asymmetric search hyperspace by taking advantage of various positions of the current solution within the domain is the critical novelty to enrich general diversity in the population, significantly improving the algorithm's overall exploration capability. To validate the effectivity of the proposed RUNGE-QDOPP method, thirty-four multidimensional optimization benchmark problems comprised of unimodal and multimodal test functions with various dimensionalities have been solved, and the corresponding results are compared against the predictions obtained from the other opposition-based learning variants as well as some state-of-art literature optimizers. Furthermore, six constrained engineering design problems with different functional characteristics have been solved, and the respective results are benchmarked against those obtained for the well-known optimizers. Comparison of the solution outcomes with literature optimizers for constrained and unconstrained test problems reveals that the proposed QDOPP has significant advantages over its counterparts regarding solution accuracy and efficiency.

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Section: Introductionmentioning

confidence: 99%

Quasi-dynamic opposite learning enhanced Runge-Kutta optimizer for solving complex optimization problems

Turgut,

Turgut

2024

Evol. Intel.

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“…[8]. Sunil Kumar fuses the ABC and EP algorithms to solve path planning problems in unknown or partially known environments [9]. Y B, Li adds the search process and optimization process of ABC algorithm to the optimized particle swarm algorithm, namely ABC-PSO algorithm, and applies it to robot path planning.…”

Section: Introductionmentioning

confidence: 99%

Improved artificial bee colony algorithm to solve mobile robot path planning

li,

liu,

Xiao

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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Swarm intelligence algorithms have a large number of applications in solving robot path planning problems. The traditional artificial bee colony (ABC) algorithm has strong global ability, large randomness, however there are problems such as the more nodes, the higher the path complexity. In this study, node movement artificial bee colony algorithm (NMABC) is proposed overcome the disadvantages of the traditional ABC in solving robot path planning problems. The NMABC algorithm contains two novel mechanisms which are the improved initial path generation way and the neighborhood search method of the iterative path of the employed bee. The experimental results show that the NMABC has good global search capability and fast iterative convergence, and it can find the optimal path quickly.

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“…Global planning is to plan the path by search algorithms after constructing the map model based on the known global environment. Common global planning algorithms include genetic algorithm [7], bee colony algorithm [8], rapidly-exploring random tree(RRT). Classified by algorithm type, there are interpolation based planning methods such as Bezier curve [9] and spline curve [10], sampling based planning methods like RRT algorithm and numerical optimization methods like function optimization method [18].…”

Section: Introductionmentioning

confidence: 99%

Improved UCAV Attack Trajectory Planning Algorithm Based on GWO and Dubins Curve

Huang¹,

Shen²,

Zhang³

et al. 2023

Lecture Notes in Electrical Engineering

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AMR is widely used in factories to replace manual labor to reduce costs and improve efficiency. However, it is often difficult for logistics robots to plan the optimal trajectory and unreasonable trajectory planning can lead to low transport efficiency and high energy consumption. In this paper, we propose a method to directly calculate the optimal trajectory for short distance on the basis of the Dubins set, which completes the calculation of the Dubins path. Additionally, as an improvement of Dubins path, we smooth the Dubins path based on clothoid, which makes the curvature varies linearly. AMR can adjust the steering wheels while following this trajectory. The experiments show that the Dubins path can be calculated quickly and well smoothed.

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Optimum Mobile Robot Path Planning Using Improved Artificial Bee Colony Algorithm and Evolutionary Programming

Cited by 35 publications

References 49 publications

Quasi-dynamic opposite learning enhanced Runge-Kutta optimizer for solving complex optimization problems

Quasi-dynamic opposite learning enhanced Runge-Kutta optimizer for solving complex optimization problems

Improved artificial bee colony algorithm to solve mobile robot path planning

Improved UCAV Attack Trajectory Planning Algorithm Based on GWO and Dubins Curve

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