An improved salp optimization algorithm inspired by quantum computing

Abstract

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.

Salp Swarm Optimization: A critical review

“…This analysis aims at understanding whether the scientific community could abandon the use of SSO and ASSO, or if these algorithms provide some advantages with respect to simpler metaheuristics widely established in this research field. As a side note, SSO changed name several times in the literature: indeed, we can find it as Salp Swarm Algorithm (Sayed, Khoriba, & Haggag, 2018), Salp Swarm Optimizer (El-Fergany, 2018Shaheen & El-Sehiemy, 2020), Salp Swarm Optimization (Cheng & Wang, 2020), Salp Optimization Algorithm (Tian, Wei, Li, Lv, & Wang, 2020), and so forth. Incidentally, in the highlights of the original paper (Mirjalili et al, 2017), the authors state that the official name of the algorithm is Salp Swarm Optimizer, although the acronym SSA is used in the rest of the article.…”

QSHO: Quantum Spotted Hyena Optimizer for Global Optimization

“…Like OQFA, QFA used the QC theory to represent the positions of fireworks and sparks. Tian et al proposed an improved Salp Swarm Algorithm (QSSA) inspired by QC theory 31 and tested on benchmark function optimization. Zamani et al 32 developed a Quantum-based avian navigation optimizer algorithm (QANA) inspired by QC theory, and QANA incorporates quantum-based navigation, success-based population distribution (SPD) policy, qubit-mutation strategies, and a qubit-crossover operator.…”

“…The concepts and principles of quantum theory, as well as quantum computing, have already been successfully applied to enhance the performance of different metaheuristic algorithms such as Particle Swarm Optimizer (PSO) [25][26][27] , Immune Clonal Algorithm (ICA) 28 , Fireworks Algorithm (FWA) 29,30 , Salp Swarm Algorithm (SSA) 31 , Avian Navigation Optimizer (ANO) 32 , Marine Predators Algorithm (MPA) 33 , Multiverse Optimizer (MVO) 34 . Nonetheless, according to our knowledge, no research applied quantum theory to SHO approaches.…”

“…24SHO with space transformation search (STS-SHO)24 Quantum Salp Swarm Algorithm (QSSA)31 Chimp Optimization Algorithm (ChOA)…”

Salp Swarm Optimization: a Critical Review

“…As a side note, SSO changed name several times in the literature: indeed, we can find it as Salp Swarm Algorithm [30], Salp Swarm Optimizer [15,31], Salp Swarm Optimization [8], Salp Optimization Algorithm [33], and so forth. Interestingly, in the highlights of the original paper [24], the authors state that the official name of the algorithm is Salp Swarm Optimizer, although the acronym SSA is used in the rest of the paper.…”