Salp swarm algorithm (SSA) is a relatively new bio-inspired meta-heuristic optimization that mimics the navigating and foraging behavior of salps in oceans. This paper presents an orthogonal quasi-opposition-based learning-driven dynamic SSA (OBDSSA) for solving global problems. The proposed methodology integrates learning (OQOBL) tactic (DL) strategy with to improve its performance. OQOBL tech...

In this paper we propose a new probability update rule and sampling procedure for population-based incremental learning. These proposed methods are based on the concept of opposition as a means for controlling the amount of diversity within a given sample population. We prove that under this scheme we are able to asymptotically guarantee a higher diversity, which allows for a greater exploratio...

A fast and efficient stochastic opposition-based learning (OBL) variant is proposed in this paper. OBL a machine concept to accelerate the convergence of soft computing algorithms, which consists simultaneously calculating an original solution its opposite. Recently, called BetaCOBL was proposed, capable controlling degree opposite solutions, preserving useful information held by preventing was...

There is an important significance for human health in predicting atmospheric concentration precisely. However, due to the complexity and influence of contingency, prediction a challenging topic. In this paper, we propose novel hybrid learning method make point interval predictions PM2.5 simultaneously. Firstly, optimize Sparrow Search Algorithm (SSA) by opposition-based learning, fitness-based...

to present a multiple-instructor, active-learning strategy in the undergraduate medical curriculum. this educational research is a descriptive one. shared teaching sessions, were designed for undergraduate medical students in six organ-system based courses. sessions that involved in-class discussions of integrated clinical cases were designed implemented and moderated by at least 3 faculties (c...

Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new metaheuristic algorithm that has been applied to solve various optimization problem...