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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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ICOA: An Improved Coati Optimization Algorithm with Multi-Strategy Enhancement for Global Optimization and

Xiangyu Cheng1,2, Min Zhou1,2, Liping Zhang2,3

  • 1Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.

Biomimetics (Basel, Switzerland)
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

The Improved Coati Optimization Algorithm (ICOA) enhances metaheuristic search by integrating novel strategies, achieving superior performance on benchmark functions and engineering problems. This advanced algorithm overcomes limitations of existing methods, offering improved exploration and exploitation for complex optimization tasks.

Keywords:
Lévy flightbenchmark functionscoati optimization algorithmdifferential evolutionengineering designglobal optimizationmetaheuristic

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Nature-Inspired Computing

Background:

  • Existing metaheuristic algorithms often face premature convergence and struggle with balancing exploration and exploitation.
  • The Coati Optimization Algorithm (COA), while novel, shows limited search diversity and can stagnate in local optima, especially in high-dimensional problems.
  • There is a need for improved optimization techniques that address these limitations for complex, multimodal landscapes.

Purpose of the Study:

  • To propose an Improved Coati Optimization Algorithm (ICOA) that enhances search diversity and overcomes local optima stagnation.
  • To integrate four novel strategies: Dynamic Adaptive Step-Size, Population-Adaptive Dynamic Perturbation, Iterative-Cyclic Differential Perturbation, and Cosine-Adaptive Gaussian Perturbation.
  • To rigorously evaluate ICOA's performance on benchmark functions and constrained engineering design problems.

Main Methods:

  • Integration of four complementary strategies into the Coati Optimization Algorithm: Lévy flights with Student's t-distribution, differential evolution with fitness-proportional scaling, sinusoidal scheduling with population-differential guidance, and time-decaying Gaussian perturbations.
  • Extensive testing on CEC2017, CEC2020, and CEC2022 benchmark functions across various dimensions (10D to 100D).
  • Statistical validation using Friedman and Wilcoxon rank-sum tests, scalability analysis, ablation studies, and sensitivity analysis.

Main Results:

  • ICOA achieved overall rank 1 on all three benchmark suites (CEC2017, CEC2020, CEC2022), demonstrating superior performance.
  • Consistent first-place rankings were observed in scalability analyses across dimensions from 10D to 100D.
  • ICOA secured the best results on all six constrained engineering design problems, with statistically significant improvements (p<0.05).

Conclusions:

  • The proposed ICOA effectively addresses the limitations of the original COA and other state-of-the-art algorithms.
  • The integrated enhancement strategies significantly improve exploration, exploitation, and search diversity, leading to robust optimization capabilities.
  • ICOA represents a significant advancement in metaheuristic optimization, offering a powerful tool for complex computational problems.