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Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation
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Coupled simulated annealing.

Samuel Xavier-de-Souza1, Johan A K Suykens, Joos Vandewalle

  • 1Department of Computer Engineering and Automation, Universidade Federal do Rio Grande do Norte, 59078-900 Natal-RN, Brazil. samuel@dca.ufrn.br

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|August 5, 2009
PubMed
Summary

We introduce Coupled Simulated Annealing (CSA), a novel method enhancing global optimization. By coupling parallel processes, CSA improves efficiency and guides searches toward optimal solutions.

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Area of Science:

  • Computational Mathematics
  • Optimization Algorithms
  • Artificial Intelligence

Background:

  • Global optimization of continuous variables is a challenging problem.
  • Simulated Annealing (SA) is a common heuristic for optimization.
  • Existing SA methods can be sensitive to initialization and may converge prematurely.

Purpose of the Study:

  • To introduce a new class of methods, Coupled Simulated Annealing (CSA), for global optimization.
  • To enhance the efficiency and robustness of simulated annealing algorithms.
  • To facilitate cooperative behavior and information exchange between parallel optimization processes.

Main Methods:

  • Developed a class of parallel Simulated Annealing (SA) processes coupled via their acceptance probabilities.
  • Introduced a coupling term in the acceptance probability function dependent on the energies of current states.
  • Implemented and compared three CSA instance methods against uncoupled multistart SA.

Main Results:

  • CSA methods demonstrated cooperative behavior through information exchange, aiding decisions on accepting uphill moves.
  • Online steering of the optimization process towards the global optimum was achieved using acceptance temperature to control variance.
  • Extensive experiments showed considerable improvements over uncoupled SA and a distributed SA version, reducing sensitivity to initialization parameters.

Conclusions:

  • Coupled Simulated Annealing (CSA) offers significant improvements in global optimization efficiency and performance.
  • The cooperative nature and online control mechanisms of CSA effectively guide the search towards quasi-optimal solutions.
  • CSA represents a promising advancement in parallel metaheuristic optimization techniques.