Multi-objective optimization of CO boiler combustion chamber in the RFCC unit using NSGA II algorithm

Aminmahalati, Alireza; Fazlali, Alireza; Safikhani, Hamed

doi:10.1016/j.energy.2021.119859

Cited by 28 publications

(8 citation statements)

References 37 publications

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“…However, the third model was implemented using NSGAII technique [31] to solve the multi-objective optimization problem. Table II illustrates the values of each parameter used in GA implementation.…”

Section: Discussion and Resultsmentioning

confidence: 99%

AI-Driven Optimization Approach Based on Genetic Algorithm in Mass Customization Supplying and Manufacturing

Alfayoumi,

Eltazi,

Elgammal

2023

IJACSA

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Numerous artificial intelligence (AI) techniques are currently utilized to identify planning solutions for supply chains, which comprise suppliers, manufacturers, wholesalers, and customers. Continuous optimization of these chains is necessary to enhance their performance. Manufacturing is a critical stage within the supply chain that requires continuous optimization. Mass Customization Manufacturing is one such manufacturing type that involves high-volume production with a wide variety of materials. However, genetic algorithms have not been used to minimize both time and cost in the context of mass customization manufacturing. Therefore, we propose this study to present an artificial intelligence solution using genetic algorithm to build a model that minimizes the time and cost which associated with mass customized orders. Our problem formulation is based on a real-world case, and it adheres to expert descriptions. Our proposed optimization model incorporates two strategies to solve the optimization problem. The first strategy employs a single objective function focused on either time or cost, while the second strategy applies the multi-objective function NSGAII to optimize both time and cost simultaneously. The effectiveness of the proposed model was evaluated using a real case study, and the results demonstrated that leveraging genetic algorithms for mass customization optimization outperformed expert estimations in finding efficient solutions. On average, the evaluation revealed a 20.4% improvement for time optimization, a 29.8% improvement for cost optimization, and a 25.5% improvement for combined time and cost optimization compared to traditional expert optimization.

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Section: Discussion and Resultsmentioning

confidence: 99%

AI-Driven Optimization Approach Based on Genetic Algorithm in Mass Customization Supplying and Manufacturing

Alfayoumi,

Eltazi,

Elgammal

2023

IJACSA

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“…However, the genetic algorithms are modified to accommodate multiple objective functions for multi-objective optimization problems. There are different Multi-Objective Evolutionary algorithms (MOEA), including NSGA2 [ 41 , 42 , 43 , 44 ] and PESA-II [ 45 , 46 ]. In this work, we focused on NSGA-2 and MOEA/D algorithms for solving the constraint multi-objective optimization problem.…”

Section: Optimal Coverage Strategymentioning

confidence: 99%

Towards an Optimal Footprint Based Area Coverage Strategy for a False-Ceiling Inspection Robot

Pathmakumar

Sivanantham

Padmanabha

et al. 2021

Sensors

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False-ceiling inspection is a critical factor in pest-control management within a built infrastructure. Conventionally, the false-ceiling inspection is done manually, which is time-consuming and unsafe. A lightweight robot is considered a good solution for automated false-ceiling inspection. However, due to the constraints imposed by less load carrying capacity and brittleness of false ceilings, the inspection robots cannot rely upon heavy batteries, sensors, and computation payloads for enhancing task performance. Hence, the strategy for inspection has to ensure efficiency and best performance. This work presents an optimal functional footprint approach for the robot to maximize the efficiency of an inspection task. With a conventional footprint approach in path planning, complete coverage inspection may become inefficient. In this work, the camera installation parameters are considered as the footprint defining parameters for the false ceiling inspection. An evolutionary algorithm-based multi-objective optimization framework is utilized to derive the optimal robot footprint by minimizing the area missed and path-length taken for the inspection task. The effectiveness of the proposed approach is analyzed using numerical simulations. The results are validated on an in-house developed false-ceiling inspection robot—Raptor—by experiment trials on a false-ceiling test-bed.

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“…As regards, in this paper, we intend to design a novel rescue accessory with spreading and supporting functions, which can be quickly docked with general large-scale construction equipment. en, to better solve the optimal design problem for this accessory, an improved algorithm is proposed based on the nondominated sorting genetic algorithm (INSGA-II) [20] which has been widely used in the field of multiobjective optimization [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of a Novel Rescue Accessory with Spreading and Supporting Functions

Wang

Xia

Wang

et al. 2022

Mathematical Problems in Engineering

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A novel rescue accessory with spreading and supporting functions is proposed to promote the performance of usage continuity and improve the rescue efficiency of traditional rescue devices. To optimally design this rescue device, we apply related working principles to analyze the motion of the whole device and its spreading mechanism. In optimization, three important parameters of the spreading width, the jaw rotation angle, and the spreading force are simultaneously chosen as the objective functions. Meanwhile, specific mathematical models are established for estimating the performance of the novel rescue accessory. Then, an improved algorithm based on the nondominated sorting genetic algorithm (INSGA-II) is proposed to solve the above multiobjective optimization problem of the rescue accessory. The INSGA-II optimizes the population diversity, the crossover operator, and the mutation operation in calculation procedures. Furthermore, it can dynamically adjust the crowded distance and enhance the global as well as the local searching capabilities of INSGA-II. Results from computational tests show that INSGA-II has the best performance among other popular algorithms. More importantly, comparison results show that the compromise solution of the Pareto front for the above three objective functions obtained by INSGA-II is superior to those by other algorithms.

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Multi-objective optimization of CO boiler combustion chamber in the RFCC unit using NSGA II algorithm

Cited by 28 publications

References 37 publications

AI-Driven Optimization Approach Based on Genetic Algorithm in Mass Customization Supplying and Manufacturing

AI-Driven Optimization Approach Based on Genetic Algorithm in Mass Customization Supplying and Manufacturing

Towards an Optimal Footprint Based Area Coverage Strategy for a False-Ceiling Inspection Robot

Optimal Design of a Novel Rescue Accessory with Spreading and Supporting Functions

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