Russell Eberhart

Learn More
A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are(More)
The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed.(More)
This paper presents particle swarm optimization based on learning from winner particle. (PSO-WS). Instead of considering gbest and pbest particle for position update, each particle considers its distance from immediate winner to update its position. Only winner particle follow general velocity and position update equation. If this strategy performs well for(More)
This paper discusses an emerging field of computation known as blended intelligence. A definition of blended intelligence is proposed. The definition is expanded upon by examples of what blended intelligence is, and what it is not. Examples of human/computational combinations that inspire blended intelligence are reviewed, followed by predictions of(More)
Abstract The paper proposes a cooperative evolutionary algorithm based on particle swarm optimization (PSO) and simulated annealing algorithm (SA). The method makes full use of the local convergent performance of PSO and the global convergent performance of SA, and can validly overcome the premature problem in PSO through cooperative search between PSO and(More)
Multi-objective optimization can be commonly found in many real world problems. In computational intelligence, Particle Swarm Optimization (PSO) algorithm has increasing popularity in solving optimization problems. An extended PSO algorithm called Vector Evaluated Particle Swarm Optimization (VEPSO) has been introduced to solve multi-objective optimization(More)
This paper introduces hybridization of particle swarm optimization (PSO) with genetic algorithm (GA) denoted as PSO+GA provides an efficient approach which is used to solve non linear chaotic datasets. The proposed algorithm employed in probabilistic neural network(PNN) which is a variant of radial basic function artificial neural network (RBFANN) for(More)
This paper discusses possible approaches to evolving intelligence in which blended intelligence and extended analog computing play roles. Deep learning and universal learning are briefly summarized. A definition of blended intelligence is proposed, followed by an introduction to extended analog computing. Implementing extended analog computing to achieve(More)
  • 1