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Multimodal function optimization using minimal representation size clustering and its application to planning

C Hocaoğlu1, A C Sanderson

  • 1Engineering Department, Rensselaer Polytechnic Institute, Troy, New York 12180-3590, USA. hocaoglu@eamri.rpi.edu

Evolutionary Computation
|April 1, 1997
PubMed
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This study introduces a novel genetic algorithm (GA) with minimal representation size cluster (MRSC) analysis for multimodal function optimization and path planning. The MRSC-GA effectively identifies multiple solutions and reveals unknown function structures, enhancing robotic path planning in dynamic environments.

Area of Science:

  • Computational Intelligence
  • Robotics
  • Optimization Algorithms

Background:

  • Multimodal function optimization problems present challenges due to multiple local minima.
  • Existing genetic algorithms (GAs) may struggle to efficiently explore and identify diverse solutions in complex search spaces.
  • Path planning for mobile robots and manipulators requires robust algorithms capable of handling dynamic environments and generating alternative routes.

Purpose of the Study:

  • To develop and implement a novel genetic algorithm (GA) incorporating Minimal Representation Size Cluster (MRSC) analysis for multimodal function optimization.
  • To apply the developed MRSC-GA to path-planning problems for mobile robots, piano-mover problems, and N-link manipulators.
  • To demonstrate the algorithm's capability in revealing unknown function structures and generating multipaths for enhanced path planning.

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Main Methods:

  • A multiple-population genetic algorithm (GA) framework was designed, utilizing Minimal Representation Size Cluster (MRSC) analysis for species formation and population sizing.
  • The MRSC criterion was employed to determine the number of populations, enabling the algorithm to discover the underlying structure of multimodal functions without prior knowledge.
  • A novel iterative multiresolution path representation was integrated as the basis for GA coding in path-planning applications.

Main Results:

  • The proposed MRSC-GA effectively solved multimodal function optimization problems, demonstrating a highly parallel approach for finding multiple local minima.
  • The algorithm successfully revealed the unknown structure of tested multimodal functions.
  • The MRSC-GA was effectively applied to path-planning tasks, generating multipaths for mobile robots and manipulators, offering alternative solutions crucial for dynamic environments.

Conclusions:

  • The MRSC-GA offers an effective and parallelizable method for multimodal function optimization and uncovering function structures.
  • The integration of MRSC-GA into path planning provides a robust solution for mobile robots and manipulators, especially in dynamic settings.
  • The generation of multipaths by MRSC-GA enhances adaptability and provides redundancy in robotic navigation.