This study is based on the influence of power shift control smoothness cotton picker shifts and proposes an optimization method for with different strategies gearbox. Through structural design mechanism module, hydraulic distribution system travel module its mode are analyzed, a regional cooperative strategy proposed in conjunction theory. A model dynamics was built using AMESim simulation soft...

Particle swarm optimization (PSO) method is relatively a new population-based intelligence algorithm and exhibits good performance in optimization problems. However, during the optimization process, the particles become more and more similar, and gather into the neighborhood of the best particle in the swarm, which makes the swarm prematurely converged possibly around the local solution. PSO te...

Generality is one of the main advantages of heuristic algorithms, as such, multiple parameters are exposed to the user with the objective of allowing them to shape the algorithms to their specific needs. Parameter selection, therefore, becomes an intrinsic problem of every heuristic algorithm. Selecting good parameter values relies not only on knowledge related to the problem at hand, but to th...

Particle Swarm Optimization (PSO) has shown its good search ability in many optimization problems. But PSO easily gets trapped into local optima while dealing with complex problems due to lacks in diversity. In this work, we proposed an improved PSO, namely PSO-APMLB, in which adaptive polynomial mutation strategy is employed in local best of particles to introduce diversity in the swarm space....

Inspired by the estimation capability of Kalman filter, we have recently introduced a novel population-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering, which includes prediction, measurement, and estimation, the global minimum/maximum can be estimated. Measurement process, which is r...

Particle swarmoptimization (PSO) is a heuristic global optimizationmethod, proposed originally byKennedy and Eberhart in 1995. It is now one of themost commonly used optimization techniques.This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO)...

Particle swarm optimization (PSO) algorithm is a kind of random optimization algorithm based on swarm intelligence. Swarm intelligence of PSO is produced by cooperation and competition between particles, which is used for guiding optimization search. PSO has been studied widely in many applications due to its good global searching ability. Currently PSO has been widely used in function optimiza...

Knowledge Management (KM) is a relatively new phenomenon that appears in the field of Public Sector Organizations (PSO) bringing new paradigms of organizational management, challenges, risks and opportunities for its implementation, development and evaluation. KM can be seen as a systematic and deliberate effort to coordinate people, technology, organizational structures and its environment thr...

In this paper we have investigated the performance of PSO Particle Swarm Optimization based clustering on few real world data sets and one artificial data set. The performances are measured by two metric namely quantization error and inter-cluster distance. The K means clustering algorithm is first implemented for all data sets, the results of which form the basis of comparison of PSO based app...

PSO is a robust and stochastic optimization technique based on the movement and the intelligence of the swarms. PSO is inspired with social behavior of bird flocking and fish schooling. PSO algorithm is applied to solve the real world complex optimization problems. So many real world optimization problems are solved by using different combinations and variations of PSO algorithms. In this paper...