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A new Multi Sine-Cosine algorithm for unconstrained optimization problems.

Muhammad Zubair Rehman1, Abdullah Khan2, Rozaida Ghazali1

  • 1Soft Computing & Data Mining Centre (SMC), Faculty of Computer Science & Information Technology (FSKTM), Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia.

Plos One
|August 6, 2021
PubMed
Summary
This summary is machine-generated.

The new Multi Sine-Cosine algorithm (MSCA) enhances metaheuristic search by using multiple clusters to avoid local minima. MSCA demonstrates superior convergence compared to the original Sine-Cosine algorithm (SCA).

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • The Sine-Cosine Algorithm (SCA) is a population-based metaheuristic.
  • SCA uses sine and cosine functions for optimization but can be trapped in local minima/maxima due to its parameters.
  • There is a need for improved metaheuristics that can avoid local optima and achieve better convergence.

Purpose of the Study:

  • To propose a novel Multi Sine-Cosine Algorithm (MSCA) to address the local minima problem in SCA.
  • To enhance the diversification and intensification of the search process in metaheuristic optimization.
  • To improve the convergence rate and global search capability of metaheuristic algorithms.

Main Methods:

  • Developed the Multi Sine-Cosine Algorithm (MSCA) incorporating multiple swarm clusters.
  • Implemented a mechanism within MSCA to identify and switch to better-performing search clusters.
  • Evaluated MSCA performance on a suite of unimodal, multimodal, and composite benchmark functions.

Main Results:

  • MSCA effectively diversifies and intensifies the search space using multiple swarm clusters.
  • The cluster-switching mechanism in MSCA aids in avoiding local minima/maxima.
  • Experimental results show MSCA achieves statistically superior convergence compared to the original SCA and other state-of-the-art algorithms.

Conclusions:

  • MSCA is a robust metaheuristic algorithm capable of overcoming the local minima/maxima limitations of SCA.
  • The proposed multi-cluster approach enhances search efficiency and global convergence.
  • MSCA represents a significant advancement in metaheuristic optimization techniques.