Rank-driven salp swarm algorithm with orthogonal opposition-based learning for global optimization

Abstract

Salp swarm algorithm (SSA) is a relatively new and straightforward swarm-based meta-heuristic optimization algorithm, which is inspired by the flocking behavior of salps when foraging and navigating in oceans. Although SSA is very competitive, it suffers from some limitations including unbalanced exploration and exploitation operation, slow convergence. Therefore, this study presents an improved version of SSA, called OOSSA, to enhance the comprehensive performance of the basic method. In preference, a new opposition-based learning strategy based on optical lens imaging principle is proposed, and combined with the orthogonal experimental design, an orthogonal lens opposition-based learning technique is designed to help the population jump out of a local optimum. Next, the scheme of adaptively adjusting the number of leaders is embraced to boost the global exploration capability and improve the convergence speed. Also, a dynamic learning strategy is applied to the canonical methodology to improve the exploitation capability. To confirm the efficacy of the proposed OOSSA, this paper uses 26 standard mathematical optimization functions with various features to test the method. Alongside, the performance of the proposed methodology is validated by Wilcoxon signed-rank and Friedman statistical tests. Additionally, three well-known engineering optimization problems and unknown parameters extraction issue of photovoltaic model are applied to check the ability of the OOSA algorithm to obtain solutions to intractable real-world problems. The experimental results reveal that the developed OOSSA is significantly superior to the standard SSA, currently popular SSA-based algorithms, and other state-of-the-artmetaheuristic algorithms for solving numerical optimization, real-world engineering optimization, and photovoltaic model parameter extraction problems. Finally, an OOSSA-based path planning approach is developed for creating the shortest obstacle-free route for autonomous mobile robots. Our introduced method is compared with several successful swarm-based metaheuristic techniques in five maps, and the comparative results indicate that the suggested approach can generate the shortest collision-free trajectory as compared to other peers.

Modeling of Internet of Things Enabled Sustainable Environment Air Pollution Monitoring System

“…During this case, LOBL was employed for updating the candidate solutions in the exploration step, enlarging the search range, and supporting this technique for escaping in the local optimal [20]. Different models on LOBL were demonstrated mathematically as follows.…”

“…At this point, assume that the scale factor 𝑛 = ℎ/ℎ * , the reverse solution 𝐺𝑋 * was computed by transmitting as [20]:…”

“…If a novel inverse solution was created, there is no assurance that it can be continuously superior to the present candidate solution as during the gorilla place. Thus, it can be needed to estimate the fitness value of inverse as well as candidate solutions, afterwards, the fitter one is chosen for continuing participation during the succeeding exploitation stage is explained as follows [20]: In Eq. ( 23),𝑦 represents the number of rounds, 𝑦 𝑖 indicates the experimental value, 𝑦 ̂𝑖 shows the predicted values correspondingly.…”

Advanced orthogonal opposition‐based learning‐driven dynamic salp swarm algorithm: Framework and case studies

“…In addition, swarm-based methods can solve large-scale problems by consuming fewer computing resources. Based on these advantages, swarm intelligence-based algorithm is widely used to solve multiple optimisation problems that exist in various fields [39]. However, swarm-based optimisation algorithms have different characteristics, i.e.…”

Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization

“…In, Tu et al (2021) proposed a quantum-behaved SSA approach and studied the application of the advocated approach in wireless sensor networks. In, Wang et al (2021) developed an improved SSA with opposition based learning mechanism and ranking-based learning strategy for global optimization problems and PV parameter extraction task.…”