Optimization of the Intelligent Workshop Control Based on the Improved Group Leadership Optimization Algorithm

Xue, Ping; Jiang, Chuan; Wei, Wen‐Cheng J.; Lin, Jinn

doi:10.2507/ijsimm17(4)co16

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Machine-vision tools can find microscopic defects in products at resolutions well beyond human vision, using a machine-learning algorithm trained on remarkably small volumes of sample images. When integrated with a cloud-based data processing framework, defects are instantly flagged and a response is automatically coordinated (Xue et al, 2018).…”

Section: Qualitymentioning

confidence: 99%

Artificial Intelligence in Manufacturing Companies and Broader: An Overview

Buchmeister¹,

Palčić²,

Ojsteršek³

2019

DAAAM International Scientific Book

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The workplace as we know it and production systems as a whole will not be recognizable in a decade's time. In this chapter an overview of expected future changes in manufacturing systems is given. Artificial intelligence (AI) is the key driver in this change and it is critical to prepare us for a future dominated by AI. Robots and AI will prove far more disruptive than anything we have seen before. The most effective AI systems will be designed around the concept of intelligent augmentation. They will handle the math and basic analysis, relieving skilled operators of a tedious burden, absorb data, classify and prioritize information, conduct simulations and ultimately leave it to the human operator to decide the course of action. In the AI future, jobs requiring human creativity and management expertise will enjoy the greatest job security. This is our competitive advantage as humans. Those who understand the collaboration with other humans while working with AI tools to enhance their organization's effectiveness will create maximum value. An energyand resourcefriendly production is an important key performance indicator for industrial companies to work economically and remain competitive.

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Section: Qualitymentioning

confidence: 99%

Artificial Intelligence in Manufacturing Companies and Broader: An Overview

Buchmeister¹,

Palčić²,

Ojsteršek³

2019

DAAAM International Scientific Book

View full text Add to dashboard Cite

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“…Intelligent optimization algorithm [4][5][6][7][8] is a kind of optimization method inspired by biological system or physical phenomenon in nature. Many classical optimization algorithms are intelligent optimization algorithms, including genetic algorithm (GA), simulated annealing (SA) algorithm, particle swarm optimization (PSO) [9], ant colony optimization (ACO) [10], differential evolution (DE) [11], and many hybrid algorithms [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Optimization Algorithm for Blocking Flow-Shop Scheduling Based on Differential Evolution

Xu¹,

Xie²,

Yuan³

et al. 2019

Int. j. simul. model.

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Owing to its large scale, the blocking flow-shop scheduling problem (BFSP) cannot be solved effectively by traditional optimization methods. To solve the problem, this paper develops a novel intelligent optimization algorithm based on differential evolution (DE) for the BFSP with a single objective: minimizing the total flow time (TFT). On the one hand, a new heuristic method was introduced to balance the quality and diversity of the initial population. On the other hand, a new operator was adopted to update the acceleration, velocity and position of each particle. In this way, the population will not converge prematurely to local optimums, and the local and global search abilities are perfectly balanced. Simulation on standard test set proves that our algorithm outperformed most commonly used methods in solving the BFSP.

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“…The simulation and experiment of the tracking controller of the EFWA-ADRC algorithm are in the section 3. At last, conclusions are drawn [21,22].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Fireworks Algorithm-Auto Disturbance Rejection Control Algorithm for Robot Fish Path Tracking

Song¹,

Gao²,

Chen³

et al. 2019

INT J COMPUT COMMUN

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The robot fish is affected by many unknown internal and external interference factors when it performs path tracking in unknown waters. It was proposed that a path tracking method based on the EFWA-ADRC (enhanced fireworks algorithmauto disturbance rejection control) to obtain high-quality tracking effect. ADRC has strong adaptability and robustness. It is an effective method to solve the control problems of nonlinearity, uncertainty, strong interference, strong coupling and large time lag. For the optimization of parameters in ADRC, the enhanced fireworks algorithm (EFWA) is used for online adjustment. It is to improve the anti-interference of the robot fish in the path tracking process. The multi-joint bionic robot fish was taken as the research object in the paper. It was established a path tracking error model in the Serret-Frenet coordinate system combining the mathematical model of robotic fish. It was focused on the forward speed and steering speed control rate. It was constructed that the EFWA-ADRC based path tracking system. Finally, the simulation and experimental results show that the control method based on EFWAADRC and conventional ADRC makes the robotic fish track the given path at 2:8s and 3:3s respectively, and the tracking error is kept within plus or minus 0:09m and 0:1m respectively. The new control method tracking steady-state error was reduces by 10% compared with the conventional ADRC. It was proved that the proposed EFWA-ADRC controller has better control effect on the controlled system, which is subject to strong interference.

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Optimization of the Intelligent Workshop Control Based on the Improved Group Leadership Optimization Algorithm

Cited by 4 publications

References 26 publications

Artificial Intelligence in Manufacturing Companies and Broader: An Overview

Artificial Intelligence in Manufacturing Companies and Broader: An Overview

An Intelligent Optimization Algorithm for Blocking Flow-Shop Scheduling Based on Differential Evolution

Enhanced Fireworks Algorithm-Auto Disturbance Rejection Control Algorithm for Robot Fish Path Tracking

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