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A rhinopithecus swarm optimization algorithm for complex optimization problem.

Guoyuan Zhou1, Dong Wang1, Guoao Zhou2

  • 1College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.

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|July 7, 2024
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Summary
This summary is machine-generated.

A new Rhinopithecus Swarm Optimization (RSO) algorithm effectively solves high-dimensional problems. Inspired by rhinopithecus behavior, RSO shows superior performance compared to existing methods in benchmark tests and engineering applications.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Meta-heuristic Computing

Background:

  • High-dimensional optimization problems pose significant challenges in various scientific and engineering fields.
  • Existing meta-heuristic algorithms often struggle with scalability and efficiency in high-dimensional search spaces.
  • Novel approaches are needed to enhance the performance of optimization techniques for complex problems.

Purpose of the Study:

  • To introduce a novel meta-heuristic algorithm, Rhinopithecus Swarm Optimization (RSO), designed for high-dimensional optimization problems.
  • To investigate the efficacy of RSO by comparing its performance against established optimization algorithms.
  • To demonstrate the practical applicability of RSO in solving complex engineering design problems.

Main Methods:

  • The Rhinopithecus Swarm Optimization (RSO) algorithm is proposed, inspired by the social structure and behaviors of rhinopithecus swarms.
  • RSO categorizes individuals into mature, adolescent, and infancy groups, each employing distinct search strategies: vertical migration, concerted search, and mimicry.
  • Performance evaluation involved the CEC2017 test suite and three constrained engineering problems, with extensive statistical analysis using Wilcoxon signed-rank and Friedman tests.

Main Results:

  • RSO demonstrated outstanding performance on the CEC2017 test set across 30 and 100 dimensions, achieving first rank.
  • RSO outperformed eight well-known optimization algorithms, including DBO, BWO, SSA,AVOA, WOA, ARBBPSO, GTO, and HHO.
  • The algorithm consistently yielded the best results for the three classical engineering design problems evaluated.

Conclusions:

  • Rhinopithecus Swarm Optimization (RSO) is a highly effective algorithm for tackling high-dimensional optimization challenges.
  • The unique division of labor and search strategies within RSO contribute to its superior performance.
  • RSO presents a promising new tool for researchers and engineers dealing with complex optimization tasks.