Sigmoid surface controller has been proved to be effective in motion control of autonomous underwater vehicles, but it is hard to adjust its control parameters. Improved particle swarm optimization of sigmoid surface controller was proposed in this paper, which solves the problems that particle swarm optimization may be trapped in local optimum and fails to converge to global optimum. Moreover,...

On the basis of the linearized Phillips-Herffron model of a single-machine power system, a novel method for designing unified power flow controller (UPFC) based output feedback controller is presented. The design problem of output feedback controller for UPFC is formulated as an optimization problem according to with the time domain-based objective function which is solved by iteration particle...

State assignment (SA) for finite state machines (FSMs) is one of the main optimization problems in the synthesis of sequential circuits. It determines the complexity of its combinational circuit and thus area, delay, testability and power dissipation of its implementation. Particle swarm optimization (PSO) is a nondeterministic heuristic that optimizes a problem by iteratively trying to improve...

Optimum scheduling of hydrothermal plants generation is of great importance to electric utilities. Many evolutionary techniques such as particle swarm optimization, differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. This...

This chapter presents particle swarm optimization (PSO) based algorithms. After an overview of PSO’s development and application history also two application examples are given in the following. PSO’s robustness and its simple applicability without the need for cumbersome derivative calculations make it an attractive optimization method. Such features also allow this algorithm to be adjusted fo...

The Long Short-Term Memory network of deep learning neural is widely used to predict stock price in financial field. In order optimize the accuracy prediction by LSTM network, this paper firstly uses principal component analysis method extract various influencing indexes stock. Then, use Circle mapping select initial value more evenly, sine and cosine factors improve particle swarm optimization...

A reliability analysis method based on least squares support vector machines with an improved particle swarm optimization algorithm (IPSO-LSSVM) is proposed to calculate the of concrete gravity dams when explicit nonlinear limit-state functions are difficult obtain accurately. First, main failure modes and their influencing factors determined. Second, Latin hypercube sampling used create sample...

This paper aims to improve the performance of original particle swarm optimization (PSO) so that the consequent method can be more robust and statistically sound for global optimization. A variation of PSO called the orthogonal permutation particle swarm optimization (OPPSO) is presented. An orthogonal permutation strategy, based on the orthogonal experimental design, is developed as a metaboli...

In order to overcome the defects of the slow convergence rate of the traditional Genetic Algorithm and basic Particle Swarm Optimization drops into local optimum easily, an improved Particle Swarm Optimization algorithm based on hybrid algorithm is proposed and applied to the signal detection for MIMO-OFDM system. The algorithm optimizes the basic Particle Swarm Optimization algorithm and some ...