this paper presents a relatively new management model for the optimal design and operation of irrigation water pumping systems. the model makes use of the newly introduced particle swarm optimization algorithm. a two step optimization model is developed and solved with the particle swarm optimization method. the model first carries out an exhaustive enumeration search for all feasible sets of p...

The large-scale integration of wind turbines (WTs) in renewable power generation induces oscillations, leading to frequency aberration due unbalance. Hence, this paper, a secondary control strategy called load (LFC) for systems with turbine participation is proposed. Specifically, backpropagation (BP)-trained neural network-based PI approach adopted optimize the conventional controller achieve ...

differential steering of in-wheel electric vehicle provides the functions of both active steering and power assisted steering with the coupling control of force and displacement transfer characteristic of system. a collaborative optimization model of the differential power-assisted steering system of in-wheel electric vehicle is built, with steering economy as the main system optimization goal,...

In this chapter, Deep Memory with Particle Swarm Optimization (DMPSO) algorithm is presented, which is based on Particle Swarm Optimization initialized by the particles of Deep Memory Greedy Search (DMGS). The Particle Swarm Optimization (PSO) is a population based optimization technique, where the population is called a swarm. In PSO, each particle represents a possible solution to the optimiz...

Particle swarm optimization (PSO) has been successfully applied to solve various optimization problems. Recently, a state-based algorithm called multi-state particle swarm optimization (MSPSO) has been proposed to solve discrete combinatorial optimization problems. The algorithm operates based on a simplified mechanism of transition between two states. However, the MSPSO algorithm has to deal w...

In order to further enhance the accuracy of Web information extraction, and overcome the shortcomings of the Hidden Markov Model (HMM) and its hybrid method in parameter optimization, a novel Web extraction algorithm based on a combined and improved particle swarm optimization, ant colony algorithm (IPSO-ACA) and HMM is presented. First, an HMM for information extraction is built. Second, an im...

As a new swarm intelligence algorithm after Ant Colony Algorithm (ACA), Immune Particle Swarm Optimization (IPSO) is currently an important branch of evolutionary algorithm. Its basic idea is influenced and inspired by research results of their modeling and simulation of behaviors of swarms of birds in earlier periods. And their model and simulation algorithm mainly took use of biologist Frallk...

1 Abstract algorithm to reach the local minima very fast, but never finds a global solution. In addition, BP training performance depends on the initial system parameter values. For different network topologies one must derive new mathematical expressions for each network layer. In order to enhance the immune algorithm (IA) performance and find the optimal solution when dealing with difficult p...

so far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is particle swarm optimization (pso). prior some efforts by applying fuzzy logic for improving defects of pso such as trapping in local optimums and early convergence has been done. moreover to overcome the problem of i...