Results show that Artificial Cooperative Search algorithm outperforms other algorithms in terms of robustness and effectiveness.
📌 3 Key Takeaways
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Objective & Hypothesis:Parameter estimation of chaotic systems is a challenging and critical topic in nonlinear science.
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Key Findings & Data:In this study, Artificial Cooperative Search algorithm is put into practice for successful parameter estimation of chaotic systems and compared the parameter estimation performance of Artificial Cooperative Search with Bat, Artificial Bee Colony, Quantum behaved Particle Swarm Optimization algorithms.
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Conclusion & Impact:Results show that Artificial Cooperative Search algorithm outperforms other algorithms in terms of robustness and effectiveness.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Artificial cooperative search · Chaotic systems · Metaheuristic algorithms
Abstract
Parameter estimation of chaotic systems is a challenging and critical topic in nonlinear science. Problem at hand is multi-dimensional and highly nonlinear thereof conventional optimization methods generally fail to extract the unknown parameters of chaotic system. In this study, Artificial Cooperative Search algorithm is put into practice for successful parameter estimation of chaotic systems and compared the parameter estimation performance of Artificial Cooperative Search with Bat, Artificial Bee Colony, Quantum behaved Particle Swarm Optimization algorithms. Parameter identification performance of each algorithm is outlined and benchmarked with several numerical simulations including Lörenz system, Duffing equation and Josephson junction. Results show that Artificial Cooperative Search algorithm outperforms other algorithms in terms of robustness and effectiveness.
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