Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots

2021 ◽  
pp. 107372
Author(s):  
Divya Agarwal ◽  
Pushpendra S. Bharti
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Swarm Intelligence ◽  
Obstacle Avoidance ◽  
Intelligence Algorithm ◽  
Slime Moulds ◽  
Swarm Intelligence Algorithm ◽  
Avoidance Problem

scholarly journals Novel Swarm Intelligence Algorithm for Global Optimization and Multi-UAVs Cooperative Path Planning: Anas Platyrhynchos Optimizer

2020 ◽  
Vol 10 (14) ◽  
pp. 4821
Author(s):  
Yong Zhang ◽  
Pengfei Wang ◽  
Liuqing Yang ◽  
Yanbin Liu ◽  
Yuping Lu ◽  
...  
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Local Development ◽  
Anas Platyrhynchos ◽  
Intelligent Algorithm ◽  
Intelligence Algorithm ◽  
Engineering Problems ◽  
The Mathematical Model ◽  
Swarm Intelligence Algorithm

In this study, a novel type of swarm intelligence algorithm referred as the anas platyrhynchos optimizer is proposed by simulating the cluster action of the anas platyrhynchos. Starting from the core of swarm intelligence algorithm, on the premise of the use of few parameters and ease in implementation, the mathematical model and algorithm flow of the anas platyrhynchos optimizer are given, and the balance between global search and local development in the algorithm is ensured. The algorithm was applied to a benchmark function and a cooperative path planning solution for multi-UAVs as a means of testing the performance of the algorithm. The optimization results showed that the anas platyrhynchos optimizer is more superior in solving optimization problems compared with the mainstream intelligent algorithm. This study provides a new idea for solving more engineering problems.

scholarly journals Penetration Planning and Design Method of Unmanned Aerial Vehicle Inspired by Biological Swarm Intelligence Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fengtao Xiang ◽  
Keqin Chen ◽  
Jiongming Su ◽  
Hongfu Liu ◽  
Wanpeng Zhang
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Unmanned Aerial Vehicle ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Planning And Design ◽  
Intelligence Algorithm ◽  
Aerial Vehicle ◽  
Swarm Intelligence Algorithm ◽  
Improved Ant Colony Algorithm

Unmanned aerial vehicles (UAVs) are gradually used in logistics transportation. They are forbidden to fly in some airspace. To ensure the safety of UAVs, reasonable path planning and design is one of the key factors. Aiming at the problem of how to improve the success rate of unmanned aerial vehicle (UAV) maneuver penetration, a method of UAV penetration path planning and design is proposed. Ant colony algorithm has strong path planning ability in biological swarm intelligence algorithm. Based on the modeling of UAV planning and threat factors, improved ant colony algorithm is used for UAV penetration path planning and design. It is proposed that the path with the best pheromone content is used as the planning path. Some principles are given for using ant colony algorithm in UAV penetration path planning. By introducing heuristic information into the improved ant colony algorithm, the convergence is completed faster under the same number of iteratives. Compared with classical methods, the total steps reduced by 56% with 50 ant numbers and 200 iterations. 62% fewer steps to complete the first iteration. It is found that the optimal trajectory planned by the improved ant colony algorithm is smoother and the shortest path satisfying the constraints.

Research on Swarm Intelligence Algorithm with an Artificial Bee Colony Algorithm for Lot Streaming Problem in Job Shop

2014 ◽  
Vol 951 ◽  
pp. 239-244 ◽  
Author(s):  
Xiao Qiang Xu ◽  
De Ming Lei
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Lot Streaming ◽  
Lot Scheduling ◽  
Bee Colony ◽  
Intelligence Algorithm ◽  
Second Period ◽  
Swarm Intelligence Algorithm

The lot streaming (LS) problem in job shop with equal-size sub-lots and intermittent idling is considered. An effective swarm intelligence algorithm with an artificial bee colony (ABC) algorithm is proposed for the minimization of total penalties of tardiness and earliness. In the first period of ABC, the employed bee phase and the onlooker bee phase are both for lot/sub-lot scheduling. In the second period, the LS conditions are determined in the employed bee phase and the lot/sub-lot is scheduled in the onlooker phase. The worst solution of the swarm is replaced with the elite one every few cycles. Computational results show the promising advantage of ABC.

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