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Enhancing engineering optimization using hybrid sine cosine algorithm with Roulette wheel selection and

Vu Hong Son Pham1, Nghiep Trinh Nguyen Dang2, Van Nam Nguyen1

  • 1Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University (VNU-HCM), Ho Chi Minh City, Vietnam.

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Summary
This summary is machine-generated.

The novel nSCA algorithm improves meta-heuristic optimization by balancing exploration and exploitation. It outperforms existing methods on benchmark and real-world problems, offering robust solutions for complex challenges.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms

Background:

  • Meta-heuristic algorithms offer adaptable and simple optimization solutions.
  • The Sine Cosine Algorithm (SCA) balances exploration and exploitation but has room for improvement.

Purpose of the Study:

  • To enhance the global optimization capabilities of the SCA.
  • To introduce an improved algorithm, nSCA, for complex optimization tasks.

Main Methods:

  • Developed nSCA by integrating Roulette Wheel Selection (RWS) with opposition-based learning.
  • Evaluated nSCA against Genetic Algorithm (GA), Particle Swarm Optimization, and others on CEC 2019 and 2021 benchmark functions.

Main Results:

  • nSCA demonstrated superior performance over existing algorithms on benchmark test functions.
  • The algorithm proved effective in solving numerous practical, real-world optimization problems.
  • nSCA consistently delivered high-quality solutions for both discrete and continuous optimization challenges.

Conclusions:

  • nSCA offers enhanced global optimization proficiency compared to foundational SCA and other leading methods.
  • The proposed nSCA is a robust candidate for addressing complex, real-world optimization problems.