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Related Experiment Videos

A simulated annealing algorithm for prioritized multiobjective optimization--implementation in an adaptive model

Eleni Aggelogiannaki1, Haralambos Sarimveis

  • 1Laboratory of Process Control and Informatics, School of Chemical Engineering, National Technical University, 10682 Athens, Greece.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|August 19, 2007
PubMed
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A novel stochastic algorithm efficiently solves hierarchical multiobjective optimization problems using simulated annealing. This method offers low computational cost for online applications, achieving near-optimal solutions comparable to advanced evolutionary algorithms.

Area of Science:

  • Optimization
  • Control Systems Engineering
  • Computational Intelligence

Background:

  • Hierarchical multiobjective optimization problems present significant computational challenges.
  • Existing methods often struggle with low computational cost, limiting their applicability in real-time scenarios.
  • Simulated annealing offers a robust framework for complex optimization tasks.

Purpose of the Study:

  • To introduce a new stochastic algorithm for solving hierarchical multiobjective optimization problems.
  • To develop a computationally efficient method suitable for online decision-making.
  • To demonstrate the algorithm's effectiveness on benchmark and large-scale control problems.

Main Methods:

  • A stochastic algorithm based on simulated annealing, tailored for hierarchical multiobjective problems.

Related Experiment Videos

  • Assigning distinct initial temperatures to objectives based on their hierarchical position.
  • Utilizing a radial-basis-function neural network for adaptive discrete-time system modeling.
  • Incorporating a persistent excitation constraint as a high-priority objective.

    • Main Results:

    • The algorithm achieves low computational cost, crucial for time-limited online applications.
    • Near-optimal solutions are found for nonconvex multiobjective optimization problems.
    • Performance is comparable to state-of-the-art algorithms like Nondominated Sorting Genetic Algorithm II.
    • Superior performance demonstrated in a multiobjective adaptive model predictive control (MPC) configuration for a pH reactor.

    Conclusions:

    • The proposed algorithm provides an efficient and effective solution for hierarchical multiobjective optimization.
    • Its low computational demand makes it ideal for online control and decision-making systems.
    • The method outperforms conventional MPC in complex adaptive control scenarios.