Optimal shape design of an autonomous underwater vehicle based on multi-objective particle swarm optimization

2019 ◽  
Vol 19 (4) ◽  
pp. 733-742
Author(s):  
Qirong Tang ◽  
Yinghao Li ◽  
Zhenqiang Deng ◽  
Di Chen ◽  
Ruiqin Guo ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Autonomous Underwater Vehicle ◽  
Particle Swarm ◽  
Underwater Vehicle ◽  
Optimal Shape ◽  
Shape Design ◽  
Optimal Shape Design ◽  
Swarm Optimization ◽  
Multi Objective

scholarly journals Constrained path planning of autonomous underwater vehicle using selectively-hybridized particle swarm optimization algorithms

2019 ◽  
Vol 52 (21) ◽  
pp. 315-322 ◽  
Author(s):  
Hui Sheng Lim ◽  
Shuangshuang Fan ◽  
Christopher K.H. Chin ◽  
Shuhong Chai ◽  
Neil Bose ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Underwater Vehicle ◽  
Swarm Optimization

The Development of MOPSO-Based Dynamic Routing Algorithm for the Inspection of Autonomous Underwater Vehicle

2017 ◽  
Author(s):  
Yu-Hsien Lin ◽  
Lin-Chin Huang ◽  
Shao-Yu Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Autonomous Underwater Vehicle ◽  
Routing Algorithm ◽  
Particle Swarm ◽  
Underwater Vehicle ◽  
Dynamic Routing ◽  
Ocean Currents ◽  
Modular Structure ◽  
Swarm Optimization ◽  
Fixed Weight

In this study, the authors developed the dynamic routing algorithm combining an image detection technique to support the optimal route plan of Autonomous Underwater Vehicle (AUV) inspecting an offshore wind farm affected by ocean currents. A modular structure is applied to program design by the graphical language, LabVIEW (Laboratory Virtual Instrument Engineering Workbench). The modular structure is composed of 6-DOF (Six Degrees-of-Freedom) motion module, a self-tuning fuzzy control module, a stereo-vision detection module, and a dynamic routing module. In terms of path planning for inspection, several Pareto frontiers are solved iteratively according to two objectives, namely, cruise time and energy consumption. Performances obtained from MOPSO (Multi-Objective Particle Swarm Optimization) -based dynamic routing algorithm would be in comparison with those from SOPSO (Single-Objective Particle Swarm Optimization) -based dynamic routing algorithm. In addition, selections of fixed weight and dynamic weight of MOPSO-based dynamic routing algorithms would be discussed in the environment with or without ocean currents. Eventually, the image inspection mode is not only beneficial for optimizing feasible routes but it can also identify features of obstacles for positioning.

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

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