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A new metaheuristic algorithm, Ringed Seal Search (RSS), mimics seal pup behavior for global optimization. RSS balances exploration and exploitation, outperforming other algorithms in convergence rate.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Bio-inspired Computing

Background:

  • Metaheuristic algorithms are crucial for global optimization.
  • Balancing exploration and exploitation is key to algorithm efficiency.
  • Existing algorithms like Genetic Algorithm, Particle Swarm Optimization, and Cuckoo Search have limitations.

Purpose of the Study:

  • Introduce a novel metaheuristic algorithm, Ringed Seal Search (RSS).
  • Mimic the natural behavior of seal pups for a new optimization approach.
  • Enhance the balance between exploration and exploitation in global search.

Main Methods:

  • Developed the Ringed Seal Search (RSS) algorithm inspired by seal pup behavior.
  • Modeled seal pup movement using Brownian walk (normal state) and Levy walk (urgent state).
  • Validated RSS performance on fifteen benchmark test functions against baseline algorithms.

Main Results:

  • RSS demonstrated superior efficiency compared to Genetic Algorithm, Particle Swarm Optimization, and Cuckoo Search.
  • Achieved a higher convergence rate towards the global optimum.
  • Showcased an improved balance between exploration and exploitation.

Conclusions:

  • The Ringed Seal Search (RSS) algorithm effectively mimics seal pup behavior for global optimization.
  • RSS offers a promising new approach for addressing complex optimization problems.
  • The algorithm provides a better balance of search strategies, leading to improved performance.