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Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
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Related Experiment Video

Updated: Sep 1, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A new hybrid method based on Aquila optimizer and tangent search algorithm for global optimization.

Sinem Akyol1

  • 1Software Engineering Department, Engineering Faculty, Firat University, 23319 Elazig, Turkey.

Journal of Ambient Intelligence and Humanized Computing
|August 15, 2022
PubMed
Summary
This summary is machine-generated.

A new hybrid metaheuristic algorithm, Aquila Optimizer-Tangent Search Algorithm (AO-TSA), enhances optimization by balancing exploration and exploitation. This novel approach shows promising results for complex global solution search and engineering problems.

Keywords:
Aquila optimizerGlobal optimizationHybrid methodTangent search algorithm

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristic Computing

Background:

  • No single metaheuristic algorithm is optimal for all problems, necessitating the development of hybrid approaches.
  • Balancing exploration and exploitation is crucial for effective metaheuristic performance.
  • Existing algorithms may suffer from insufficient exploration or exploitation capabilities, leading to suboptimal solutions.

Purpose of the Study:

  • To propose a novel hybrid metaheuristic algorithm, the Aquila Optimizer-Tangent Search Algorithm (AO-TSA).
  • To enhance the exploitation capabilities of the Aquila Optimizer (AO) by integrating the intensification stage of the Tangent Search Algorithm (TSA).
  • To improve AO's ability to escape local minima using TSA's local minimum escape mechanism.

Main Methods:

  • Developed the Aquila Optimizer-Tangent Search Algorithm (AO-TSA) by hybridizing the Aquila Optimizer (AO) and Tangent Search Algorithm (TSA).
  • Integrated TSA's intensification and local minimum escape stages into the AO framework.
  • Evaluated AO-TSA performance on twenty-one benchmark functions (unimodal, multimodal, fixed-dimension multimodal, CEC 2019) and two real-world engineering design problems.

Main Results:

  • AO-TSA demonstrated competitive and promising performance compared to existing metaheuristic algorithms across various benchmark functions.
  • The hybrid approach effectively improved exploitation capabilities and avoided local minimum stagnation.
  • Sensitivity and statistical analyses confirmed the robustness and effectiveness of the proposed AO-TSA.

Conclusions:

  • The proposed AO-TSA is an effective hybrid metaheuristic algorithm for global solution search.
  • Hybridization significantly enhances optimization performance by leveraging the strengths of individual algorithms.
  • AO-TSA shows potential for addressing complex optimization challenges in engineering and other domains.