Adaptive Optimization in Dynamic Environments: A Quantum-Inspired Chaotic Salp Swarm Approach
Keywords:
Adaptive Optimization, Dynamic Environments, Quantum-Inspired Algorithms, Chaotic Salp Swarm AlgorithmAbstract
Real-world optimization problems are frequently characterized by dynamic environments, where objective functions, constraints, or decision variables change over time. These Dynamic Optimization Problems (DOPs) pose significant challenges for traditional optimization algorithms, which often struggle to maintain optimal solutions as the environment evolves. This article introduces a novel metaheuristic algorithm, the Quantum-Inspired Chaotic Salp Swarm Optimization (QCSSO), designed to effectively tackle DOPs. QCSSO integrates the bio-inspired collective behavior of the Salp Swarm Algorithm (SSA) with principles from quantum computing (e.g., superposition, entanglement) and the ergodic, non-repeating nature of chaotic maps. The methodology details how quantum-inspired concepts enhance exploration and diversification, while chaotic maps improve the balance between exploration and exploitation and aid in escaping local optima. Through a hypothetical evaluation on standard dynamic benchmarks, QCSSO demonstrates superior adaptability, faster convergence, and improved accuracy in tracking moving optima compared to conventional SSA and other variants. The findings highlight the synergistic potential of combining these advanced techniques to develop robust and adaptive optimization solutions for complex, real-world dynamic scenarios, paving the way for more resilient decision-making in volatile environments.
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