The Pursuit of Evolutionary Particle Swarm Optimization

Abstract

IntroductionParticle swarm optimization inspired with the social behavior in flocks of birds and schools of fish is an adaptive, stochastic and population-based optimization technique which was created by Kennedy and Eberhart in 1995 (9; 12). As one of the representatives of swarm intelligence (20), it has the distinctive characteristics: information exchange, intrinsic memory, and directional search in contrast to genetic algorithms (GAs) (14) and genetic programming (GP) (16). Due to ease of understanding and implementation, good expression and expandability, higher searching ability and solution accuracy, the technique has been successfully applied to different fields of science, technology, engineering, and applications for dealing with various large-scale, high-grade nonlinear, and multimodal optimization problems (22; 23).Although the mechanism of a plain particle swarm optimizer (the PSO) (13) is simple to implement with only a few parameters, in general, it can provide better computational results in contrast to other methods such as machine learning, neural network learning, genetic algorithms, tabu search, and simulated annealing (1). Nevertheless, like other optimization methods, an essential issue is how to make the PSO efficiently in dealing with different kinds of optimization problems. And it is well-known that the systematic selection of the parameter values in the PSO is one of fundamental manners to the end, and the most important especially for establishing a policy which determines the PSO with high search performance.However, in fact how to properly determine the values of parameters in the PSO is a quite attractive but hard subject especially for a detailed analysis of higher order (7). The cause is because the search behavior of the PSO has very high indeterminacy. Usually, these parameter values related to internal stochastic factors need to be adjusted for keeping search efficiency (5).As new development and expansion of the technique of meta-optimization 1 , the above issue already can be settled by the method of evolutionary particle swarm optimization (EPSO) (27), which provides a good framework to systematically estimate appropriate values of 1 Meta-optimization, in general, is defined as the process of using an optimization algorithm to automatically search the best optimizer from all computable optimizers. 4www.intechopen.com

Prediction for Gas Emission Quantity of the Working Face Based on LSSVM Optimized by Improved Particle Swarm Optimization

“…From two aspects of diversity and improving the convergence speed, in order to solve the premature problem of particle swarm , the researchers designed a lot of improved algorithm, such as linear change inertia weight method, the compression factor, crossover PSO model, immune PSO model, adaptive PSO algorithm, stretching PSO algorithm, PSO algorithm with operation. [6] But the above algorithms have the below problems:…”