Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, improved version of TLBO algorithm, called the Weighted Teaching-Learning-Based Optimization (WTLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. Pe...

Iterative learning control (ILC) is a strategy for repetitive tasks wherein information from previous runs leveraged to improve future performance. Optimization-based ILC (OB-ILC) powerful design framework constrained where measurements the process are integrated into an optimization algorithm provide robustness against noise and modelling error. This paper proposes robust controller linear pro...

Federated learning is a hot area of concern in the field privacy protection. There are local model parameters that difficult to integrate, poor timeliness, and training security issues. This paper proposes blockchain-based differential optimization federated incremental algorithm, First, we apply weighted random forest optimize reduce impact adding on accuracy model. Using different ensemble al...

Abstract Fixtures are an important element of the manufacturing system, as they ensure productive and accurate machining differently shaped workpieces. Regarding fixture design or layout elements, a high static dynamic stiffness fixtures is therefore required to defined position orientation workpieces under process loads, e.g. cutting forces. Nowadays, with increase in computing performance dev...

Abstract Each subject in the integrated energy system has different interests and demands, it is necessary to optimize dispatching with help of multi-subject game theory. In order solve above problems, this paper proposes a reinforcement learning-based multi-object operation optimization method for systems. Firstly, model including suppliers, park service providers users constructed; secondly, ...

Extreme Learning Machine (ELM) is popular in batch learning, sequential and progressive due to its speed, easy integration, generalization ability. While, Traditional ELM cannot train massive data rapidly efficiently memory residence, high time space complexity. In ELM, the hidden layer typically necessitates a huge number of nodes. Furthermore, there no certainty that arrangement weights biase...

Abstract Here, we propose a strategy for the global optimization of process flowsheets, fundamental problem in systems engineering, based on algebraic surrogates that are built from rigorous simulations via Bayesian symbolic regression. The applied method provides closed‐form expression can be optimized to optimality using state‐of‐the‐art solvers, where BARON or ANTIGONE were solvers choice. W...