Wavelet neural network (WNN) is a combination of wavelet analysis and neural network and has the strong fault tolerance, the strong anti-jamming and the strong adaptive ability. However, WNN is likely to trap local minimum and premature convergence. According to these shortcomings, particle swarm optimization (PSO) algorithm is applied to wavelet neural network (WNN) and has good effect. This p...

This chapter presents two ways of improvement for TRIBES, a parameterfree Particle Swarm Optimization (PSO) algorithm. PSO requires the tuning of a set of parameters, and the performance of the algorithm is strongly linked to the values given to the parameter set. However, finding the optimal set of parameters is a very hard and time consuming problem. So, Clerc worked out TRIBES, a totally ada...

Particle swarm optimization (PSO) has emerged as an acclaimed approach for solving complex optimization problems. The nature metaphors of flocking birds or schooling fish that originally motivated PSO have made the algorithm easy to describe but have also occluded the view of valuable strategies based on other foundations. From a complementary perspective, scatter search (SS) and path relinking...

Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is c...

Concrete is the second most consumed material after water and the most widely used construction material in the world. The compressive strength of concrete is one of its most important mechanical properties, which highly depends on its mix design. The present study uses the intelligent methods with instance-based learning ability to predict the compressive strength of concrete. To achieve this ...

Although evident progress and considerable achievements have been attained in developing a new particle swarm optimization (PSO) algorithm, successfully balancing the exploration and exploitation capabilities of PSO to determine high-quality solutions for complex optimization problems remains a fundamental challenge. In this study, we propose a new PSO variant, namely, adaptive division of labo...

In the electric power systems, there is a wide range of problems involving optimization processes. Among them, Economic Dispatch (ED) is one of the most important problems in the operation and management. Recently, modern meta-heuristic algorithms have been considered as effective tools for nonlinear optimization problems with applications to power systems scheduling. PSO is one of modern heuri...