An improved salp optimization algorithm inspired by quantum computing

Salp Swarm Algorithm (SSA) is a novel optimization algorithm which is widely used in engineering problems. An improved SSA inspired by quantum computing is proposed in this paper. The principles of quantum computing, such as qubits and quantum states, are introduced into the original SSA in order to overcome the defect of trapping into local optimum easily. Instead of updating the salp position directly, the quantum angle related to the quantum state is updated to increase the diversity of states. Two multidimensional benchmark functions are used to verify the proposed improved SSA, the result shows that the introduction of quantum computing can successfully prevent the SSA from falling into the local optimum and increase the accuracy.