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A Multiobjective Optimization Algorithm for Fluid Catalytic Cracking Process with Constraints and Dynamic Environments

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  • Guanzhi Liu

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Xinfu Pang

    (Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China)

  • Jishen Wan

    (Department of Electrical Engineering, Shandong Taishan Institute of Technology, Tai’an 271002, China)

Abstract

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.

Suggested Citation

  • Guanzhi Liu & Xinfu Pang & Jishen Wan, 2024. "A Multiobjective Optimization Algorithm for Fluid Catalytic Cracking Process with Constraints and Dynamic Environments," Mathematics, MDPI, vol. 12(14), pages 1-17, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2285-:d:1440207
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