Path planning optimization for mechatronic systems with the use of genetic algorithm and ant colony

Tsagaris, Apostolos; Mansour, Gabriel

doi:10.1088/1757-899x/564/1/012051

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…Genetic algorithms, ant colony optimization algorithms, and particle swarm optimization algorithms are meta-heuristic algorithms that adapt to many problems and applications. As mentioned by Tsagaris et al [19], these algorithms are closely associated with mechatronic systems. Essentially, these three algorithms all use artificial intelligence applied to path optimization to solve TSP.…”

Section: Introductionmentioning

confidence: 94%

“…Osama et al [7] simultaneously applied an artificial neural network and genetic algorithms to their project. Tsagaris et al [8] compared the use of a genetic algorithm and the combination of a genetic algorithm and an ant colony algorithm. In the experiments, it turned out that the hybrid algorithms improved the evaluation of fitness function by 95% and decreased the processing time by 18%.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of GA, ACO algorithm, and PSO algorithm for path optimization on free-form surfaces using coordinate measuring machines

Chen

Shang

2021

Eng. Res. Express

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Coordinate measuring machines (CMMs) play an important role in modern manufacturing and inspection technologies. However, the inspection process of a CMM is recognized as time-consuming work. The low efficiency of coordinate measuring machines has given rise to new inspection strategies and methods, including path optimization. This study describes the optimization of an inspection path on free-form surfaces using three different algorithms: an ant colony optimization algorithm, a genetic algorithm, and a particle swarm optimization algorithm. The optimized sequence of sampling points is obtained in MATLAB R2020b software and tested on a Leitz Reference HP Bridge Type Coordinate Measuring Machine produced by HEXAGON. This study compares the performance of the three algorithms in theoretical and practical conditions. The results demonstrate that the use of the three algorithms can result in a collision-free path being found automatically and reduce the inspection time. However, owing to the different optimization methodologies, the optimized processes and optimized times of the three algorithms, as well as the optimized paths, are different. The results indicate that the ant colony algorithm has better performance for the path optimization of free-form surfaces.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Comparison of GA, ACO algorithm, and PSO algorithm for path optimization on free-form surfaces using coordinate measuring machines

Chen

Shang

2021

Eng. Res. Express

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“…In 2023, Apostolos Tsagaris et al, [10] have applied a hybrid algorithm that combines the GA and ant colony algorithm for path optimization. The ant colony algorithm was used to eliminate the parameterization uncertainty of the traditional genetic algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of the Ant Colony System Algorithm to Search for Distance and Shortest Routes on Travel Salesman Problems

Paryati

2023

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The travel salesman problem is a combinatorial optimization problem that is very well-known in graph theory. The travel salesman problem is categorized as a difficult problem when viewed from a computational point of view. Also includes the classic "NP-Complete" problem because it has been studied for decades. TSP can be viewed as a matter of finding the shortest route that must be taken by someone who departs from his hometown to visit each city exactly once and then returns to his hometown of departure. In the travel salesman problem, the colony can coordinate through a very simple interaction, through this interaction, the colony is known to be able to solve very difficult problems. So, the method used to solve this TSP problem, using the Ant System algorithm is modified to the Ant Colony System Algorithm, to improve its performance on larger TSP problems. The main principle used in the AS algorithm is still used in the Ant Colony System algorithm, namely the use of positive feedback through the use of pheromones. A pheromone placed along the route is intended, so that the ants are more interested in taking that route. So that the best solution later, has a high concentration of pheromones. In order not to get trapped in the local optimal, negative feedback is used in the form of pheromone evaporation. While the main differences between the Ant System and Ant Colony System algorithms are different state transition rules, different global pheromone renewal rules, and the addition of local pheromone renewal rules. With this modification, the optimization results on the TSP obtained will be better, and get the shortest route in the minimum possible time. Based on the results of the system trials that have been carried out, it shows that the ant algorithm, both Ant Colony System and Ant System can be applied to the Travel Salesmen Problem. The Ant Colony System algorithm still has a faster search time than the Ant System algorithm and the difference is quite large.

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“…Hence, this study focuses on the determination of an appropriate inspection path and an optimal part setup which are necessary for the generation of an automated inspection plan. Recently, Tsagaris and Mansour [30] developed a hybrid path-optimization algorithm based on ant colony optimization (ACO) and GA and reduced the inspection time by 50%, in comparison to the general GA procedure. Optimization algorithms derived from matrix relaxation technique and nearest neighbor approach were executed by Teodor et al [31] for car body parts.…”

Section: Literature Reviewmentioning

confidence: 99%

Path Planning and Setup Orientation for Automated Dimensional Inspection Using Coordinate Measuring Machines

Abdulhameed

Al-Ahmari

Mian

et al. 2020

Mathematical Problems in Engineering

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Inspection planning is considered an essential practice in the manufacturing industries because it ensures enhanced product quality and productivity. A reasonable inspection plan, which can reduce inspection costs and achieve high customer satisfaction, is therefore very important in the production industry. Considerations such as preparations for part inspection, measuring machines, and their setups as well as the measurement path are described in an inspection plan which is subsequently translated into part inspection machine language. Therefore, the measurement of any component using a coordinate measuring machine (CMM) is the final step preceded by several other procedures, such as the preparation of the part setup and the generation of the probe path. Effective measurement of components using CMM can only be done if the preceding steps are properly optimized to automate the whole inspection process. This paper has proposed a method based on artificial intelligence techniques, namely, artificial neural network (ANN) and genetic algorithm (GA), for fine-tuning output from the different steps to achieve an efficient inspection plan. A case study to check and validate the suggested approach for producing effective inspection plans for CMMs is presented. A decrease of nearly 50% was observed in the travel path of the probe, whereas the CMM measurement time was reduced by almost 25% during the actual component measurement. The proposed method yielded the optimum part setup and the most appropriate measuring sequence for the part considered.

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Path planning optimization for mechatronic systems with the use of genetic algorithm and ant colony

Cited by 8 publications

References 7 publications

Comparison of GA, ACO algorithm, and PSO algorithm for path optimization on free-form surfaces using coordinate measuring machines

Comparison of GA, ACO algorithm, and PSO algorithm for path optimization on free-form surfaces using coordinate measuring machines

Optimization of the Ant Colony System Algorithm to Search for Distance and Shortest Routes on Travel Salesman Problems

Path Planning and Setup Orientation for Automated Dimensional Inspection Using Coordinate Measuring Machines

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