Epidemics of influenza are major public health concerns. Since prediction always relies on the weekly clinical or laboratory surveillance data, typically Influenza-like illness (ILI) rate series, accurate multi-step-ahead predictions using ILI series is great importance, especially, to potential coming outbreaks. This study proposes Comprehensive Learning Particle Swarm Optimization based Machi...

A new particle filter algorithm based on new Clone particle swarm optimization(NCPSO-PF) is presented in this paper in order to solve the problem of low precision and complicated calculation of particle filter based on particle swarm optimization algorithm(PSO-PF). The algorithm enables the particles to fit the environment better and then reach the goal of global optimization through orthogonal...

Particle swarm optimization techniques are typically made up of a population of simple agents interacting locally with one another and with their environment, with the goal of locating the optima within the operational environment. In this paper, a robust and intelligent particle swarm optimization framework based on multi-agent system is presented, where learning capabilities are incorporated ...

This paper proposes a novel population-based evolution algorithm named grouping-shuffling particle swarm optimization (GSPSO) by hybridizing particle swarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) for continuous optimization problems. In the proposed algorithm, each particle automatically and periodically executes grouping and shuffling operations in its flight learning evo...

in this paper, tender problems in an automobile company for procuring needed items from potential suppliers have been resolved by the learning algorithm q. in this case the purchaser with respect to proposals received from potential providers, including price and delivery time is proposed; order the needed parts to suppliers assigns. the buyer’s objective is minimizing the procurement costs thr...

Particle swarm optimization (PSO) has received increasing attention in solving multi-objective economic dispatch (ED) problems in power systems because of parallel computation, faster convergence, and easier implementation. This paper presents a detailed overview of multi-objective particle swarm optimization (MOPSO) and provides a comprehensive survey on its applications in power system econom...

In this paper, we propose an efficient approach to design optimization of analog circuits that is based on the reinforcement learning method. In this work, Multi-Objective Learning Automata (MOLA) is used to design a two-stage CMOS operational amplifier (op-amp) in 0.25μm technology. The aim is optimizing power consumption and area so as to achieve minimum Total Optimality Index (TOI), as a new...

Training neural networks is a complex task that is important for supervised learning. A few metaheuristic optimization techniques have been applied to increase the effectiveness of the training process. The Cuckoo Search (CS) algorithm is a recently developed meta-heuristic optimization algorithm which is suitable for solving optimization problems. In this paper, Cuckoo search is implemented in...