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In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Adaptive Constraint Penalty-Based Multiobjective Operation Optimization of an Industrial Dynamic System With Complex

Ping Zhou, Shuai Zhang, Tianyou Chai

    IEEE Transactions on Cybernetics
    |January 10, 2024
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    This study introduces an advanced evolutionary algorithm for optimizing wastewater treatment processes (WWTPs). The method enhances diversity and convergence for complex, time-varying systems with multiple constraints.

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    Area of Science:

    • Environmental Engineering
    • Computational Intelligence
    • Process Control

    Background:

    • Wastewater treatment processes (WWTPs) exhibit nonstationary, time-varying dynamics.
    • Optimizing WWTPs involves complex multiconstraint challenges.
    • Existing algorithms may struggle with dynamic and constrained environments.

    Purpose of the Study:

    • To develop a novel algorithm for optimizing WWTP operation under complex conditions.
    • To improve the handling efficiency and success rate of constraints in evolutionary algorithms.
    • To provide accurate model guarantees for high-performance multiobjective operation optimization.

    Main Methods:

    • Proposed a multiobjective evolutionary algorithm with synthetical distance (SD)-based cross-generation crossover.
    • Introduced spatial SD to evaluate solution similarity (distance and angle).
    • Implemented an adaptive penalty algorithm for constraint handling.
    • Utilized a recursive bilinear subspace identification method for time-varying dynamics.

    Main Results:

    • The algorithm demonstrated enhanced individual diversity and accelerated convergence.
    • Adaptive penalty improved constraint handling efficiency and success rates.
    • Recursive identification provided accurate models for optimization.
    • Effectiveness verified through test functions and WWTP control experiments.

    Conclusions:

    • The proposed method is effective, superior, and practical for WWTP operation optimization.
    • The algorithm successfully addresses nonstationary dynamics and multiconstraint challenges.
    • This approach offers a robust solution for complex environmental engineering problems.