Quantum Inspired Chaotic Salp Swarm Optimization for Dynamic Optimization
<|reference_start|>Quantum Inspired Chaotic Salp Swarm Optimization for Dynamic Optimization: Many real-world problems are dynamic optimization problems that are unknown beforehand. In practice, unpredictable events such as the arrival of new jobs, due date changes, and reservation cancellations, changes in parameters or constraints make the search environment dynamic. Many algorithms are designed to deal with stationary optimization problems, but these algorithms do not face dynamic optimization problems or manage them correctly. Although some optimization algorithms are proposed to deal with the changes in dynamic environments differently, there are still areas of improvement in existing algorithms due to limitations or drawbacks, especially in terms of locating and following the previously identified optima. With this in mind, we studied a variant of SSA known as QSSO, which integrates the principles of quantum computing. An attempt is made to improve the overall performance of standard SSA to deal with the dynamic environment effectively by locating and tracking the global optima for DOPs. This work is an extension of the proposed new algorithm QSSO, known as the Quantum-inspired Chaotic Salp Swarm Optimization (QCSSO) Algorithm, which details the various approaches considered while solving DOPs. A chaotic operator is employed with quantum computing to respond to change and guarantee to increase individual searchability by improving population diversity and the speed at which the algorithm converges. We experimented by evaluating QCSSO on a well-known generalized dynamic benchmark problem (GDBG) provided for CEC 2009, followed by a comparative numerical study with well-regarded algorithms. As promised, the introduced QCSSO is discovered as the rival algorithm for DOPs.<|reference_end|>
@article{pathak2024quantum,
    title={Quantum Inspired Chaotic Salp Swarm Optimization for Dynamic
  Optimization},
    author={Sanjai Pathak, Ashish Mani, Mayank Sharma, Amlan Chatterjee},
    journal={arXiv preprint arXiv:2402.16863},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16863},
    primaryClass={cs.NE cs.AI}
}