Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms
in the literature. Although the original PSO has shown good optimization performance, it still …

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm
motivated by intelligent collective behavior of some animals such as flocks of birds or …

Data normalization is one of the pre-processing approaches where the data is either scaled
or transformed to make an equal contribution of each feature. The success of machine …

The grid connection of large-scale clean energy provides the possibility for the
establishment of a clean energy system. The urgent problem that needs to be solved is how …

The particle swarm optimization (PSO) is a population-based stochastic optimization
technique by the social behavior of bird flocking and fish schooling. The PSO has a high …

Cloud computing has emerged as the most favorable computing platform for researchers
and industry. The load balanced task scheduling has emerged as an important and …

Optimization in computationally expensive numerical problems with limited function
evaluations provides computational advantages over constraints based on runtime …

Chaotic time series prediction, a challenging research topic in dynamic system modeling,
has drawn great attention from researchers around the world. In recent years extensive …

Particle swarm optimization (PSO) has shown its advantages in various optimization
problems. Topology and updating strategies are among its key concepts and have …

In the past two decades, multi-objective optimization has attracted increasing interests in the
evolutionary computation community, and a variety of multi-objective optimization algorithms …