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A Multiobjective Optimization Algorithm for Fluid Catalytic Cracking Process with Constraints and Dynamic Environments
The optimization problems in a fluid catalytic cracking process with dynamic constraints and conflicting objectives are challenging due to the complicated constraints and dynamic environments. The decision variables need to be reoptimized to obtain the best objectives when dynamic environments arise...
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| Published in: | Mathematics (Basel) 2024-07, Vol.12 (14), p.2285 |
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| Language: | English |
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| container_issue | 14 |
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| container_title | Mathematics (Basel) |
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| creator | Liu, Guanzhi Pang, Xinfu Wan, Jishen |
| description | The optimization problems in a fluid catalytic cracking process with dynamic constraints and conflicting objectives are challenging due to the complicated constraints and dynamic environments. The decision variables need to be reoptimized to obtain the best objectives when dynamic environments arise. To solve these problems, we established a mathematical model and proposed a dynamic constrained multiobjective optimization evolution algorithm for the fluid catalytic cracking process. In this algorithm, we design an offspring generation strategy based on minimax solutions, which can explore more feasible regions and converge quickly. Additionally, a dynamic response strategy based on population feasibility is proposed to improve the feasible and infeasible solutions by different perturbations, respectively. To verify the effectiveness of the algorithm, we test the algorithm on ten instances based on the hypervolume metric. Experimental results show that the proposed algorithm is highly competitive with several state-of-the-art competitors. |
| doi_str_mv | 10.3390/math12142285 |
| format | article |
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The decision variables need to be reoptimized to obtain the best objectives when dynamic environments arise. To solve these problems, we established a mathematical model and proposed a dynamic constrained multiobjective optimization evolution algorithm for the fluid catalytic cracking process. In this algorithm, we design an offspring generation strategy based on minimax solutions, which can explore more feasible regions and converge quickly. Additionally, a dynamic response strategy based on population feasibility is proposed to improve the feasible and infeasible solutions by different perturbations, respectively. To verify the effectiveness of the algorithm, we test the algorithm on ten instances based on the hypervolume metric. Experimental results show that the proposed algorithm is highly competitive with several state-of-the-art competitors.</description><identifier>ISSN: 2227-7390</identifier><identifier>EISSN: 2227-7390</identifier><identifier>DOI: 10.3390/math12142285</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Catalytic converters ; Catalytic cracking ; Constraints ; Design ; Design optimization ; dynamic constrained multiobjective optimization ; Dynamic response ; dynamic response strategy ; Energy consumption ; Energy industry ; Evolutionary algorithms ; Feasibility ; Fluid catalytic cracking ; Fossil fuels ; Gasoline ; Genetic algorithms ; Linear programming ; Mathematical models ; Multiple objective analysis ; offspring generation ; Optimization algorithms ; Profits ; Strategy ; Variables</subject><ispartof>Mathematics (Basel), 2024-07, Vol.12 (14), p.2285</ispartof><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. 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The decision variables need to be reoptimized to obtain the best objectives when dynamic environments arise. To solve these problems, we established a mathematical model and proposed a dynamic constrained multiobjective optimization evolution algorithm for the fluid catalytic cracking process. In this algorithm, we design an offspring generation strategy based on minimax solutions, which can explore more feasible regions and converge quickly. Additionally, a dynamic response strategy based on population feasibility is proposed to improve the feasible and infeasible solutions by different perturbations, respectively. To verify the effectiveness of the algorithm, we test the algorithm on ten instances based on the hypervolume metric. 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| subjects | Catalytic converters Catalytic cracking Constraints Design Design optimization dynamic constrained multiobjective optimization Dynamic response dynamic response strategy Energy consumption Energy industry Evolutionary algorithms Feasibility Fluid catalytic cracking Fossil fuels Gasoline Genetic algorithms Linear programming Mathematical models Multiple objective analysis offspring generation Optimization algorithms Profits Strategy Variables |
| title | A Multiobjective Optimization Algorithm for Fluid Catalytic Cracking Process with Constraints and Dynamic Environments |
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