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EAs generally perform unconstrained searches, and they require some additional mechanism to handle constraints.
The MIPT algorithm is suitable for large optimization problems and can handle constraints and infeasibilities.
Our developed algorithm can effectively handle constraints encountered in chemical engineering optimization problems.
One of the main advantages of MPC is that it can handle constraints on the inputs and outputs.
The model predictive control designs presented here handle constraints on the controller power and allow ISE, minimum settling time, or other optimal designs.
However, relatively few of these multi-objective algorithms handle constraints, and even fewer, use surrogates to approximate the objective or constraint functions when these functions are computationally expensive.
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In this paper, model predictive control (MPC) strategy is introduced to control the collector pressure system due to its ability to handle constraint and good control performance.
Moreover the concept of cytokines is applied for handling constraints.
Handling constraints and optimizing objectives are two equally important goals.
To this purpose we propose a passivity-based control strategy to separate the handling constraints.
All algorithms incorporate a generalized method to handle discrete variables and Deb's parameterless penalty method for handling constraints.
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