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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Ampere-Maxwell's Law: Problem-Solving01:17

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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Types of Selection01:46

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Related Experiment Video

Updated: Jan 16, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Evolutionary salp swarm algorithm with multi-search strategies and advanced memory mechanism for solving global

Hoda Zamani1

  • 1Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. hoda_zamani@sco.iaun.ac.ir.

Scientific Reports
|September 30, 2025
PubMed
Summary

This study introduces an evolutionary Salp Swarm Algorithm (ESSA) to improve complex optimization tasks. ESSA enhances solution quality and convergence speed for global optimization and design challenges.

Keywords:
Evolutionary algorithmsEvolutionary multi-search strategyMetaheuristic algorithmsOptimizationSalp swarm optimization algorithm

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Engineering Applications

Background:

  • Real-world optimization problems are complex, with many variables and constraints, challenging traditional algorithms.
  • The Salp Swarm Algorithm (SSA) offers simplicity but lacks precision in guiding populations toward optimal solutions.
  • Existing methods struggle with complex design challenges and cleaner production systems.

Purpose of the Study:

  • To propose an enhanced Salp Swarm Algorithm (ESSA) for complex optimization problems.
  • To improve population diversity, adaptive search, and convergence stability.
  • To address limitations of the standard SSA in real-world applications.

Main Methods:

  • Developed ESSA with two novel evolutionary search strategies for diversity and adaptive search.
  • Implemented an enhanced SSA search strategy for steady convergence.
  • Incorporated an advanced memory mechanism and stochastic universal selection for archive regulation.

Main Results:

  • ESSA demonstrated superior performance over SSA and seven other leading algorithms on CEC 2017 and CEC 2020 benchmark functions.
  • Achieved high optimization effectiveness: 84.48% (30D), 96.55% (50D), and 89.66% (100D).
  • Outperformed competitors in solution quality and convergence speed across various dimensions.

Conclusions:

  • ESSA effectively tackles complex optimization problems, including cleaner production and design challenges.
  • The proposed algorithm shows significant improvements in optimization effectiveness and convergence.
  • ESSA offers a robust and efficient solution for demanding optimization tasks.