This paper presents an exposition of a new method of swarm intelligence–based algorithm for optimization. Modeling swallow swarm movement and their other behavior, this optimization method represents a new optimization method. There are three kinds of particles in this method: explorer particles, aimless particles, and leader particles. Each particle has a personal feature but all of them have a central colony of flying. Each particle exhibits an intelligent behavior and, perpetually, explores its surroundings with an adaptive radius. The situations of neighbor particles, local leader, and public leader are considered, and a move is made then. Swallow swarm optimization algorithm has proved high efficiency, such as fast move in flat areas (areas that there is no hope to find food and, derivation is equal to zero), not getting stuck in local extremum points, high convergence speed, and intelligent participation in the different groups of particles. SSO algorithm has been tested by 19 benchmark functions. It achieved good results in multimodal, rotated and shifted functions. Results of this method have been compared to standard PSO, FSO algorithm, and ten different kinds of PSO.

Swallow swarm optimization algorithm: a new method to optimization

Structural damage detection (SDD) is an important but still challenging task in the structural health monitoring (SHM) field. Many methodologies have been developed and broad application prospect are expected. However, there are still some difficulties when they are applied to the real structures. In this study, a novel global artificial fish swarm algorithm (GAFSA) is proposed for exploring a new solution to the SDD problem in the SHM field. Firstly, the basic theory of the GAFSA is introduced. The fish swarm behaviours inside water are simulated by the following four steps: random, preying, swarming and following behaviours, respectively. The artificial fish parameters are defined, the implementing procedure of GAFSA is expressed, and the computing performance of GAFSA is evaluated and compared with the basic artificial fish swarm algorithm by three test functions. Secondly, the SDD problem is modelled as a constrained optimization problem in mathematics, an objective function on optimization problem is...

A Global Artificial Fish Swarm Algorithm for Structural Damage Detection

A modified particle swarm optimization algorithm based on velocity updating mechanism

Artificial Bee Colony (ABC) algorithm is a novel bio-inspired swarm intelligence approach which is competitive with other population-based algorithms and has the advantage of using fewer control parameters. However, basic ABC is easy to be prematurely convergent and be trapped into local optimum. In the later iteration, algorithm has low convergent speed and population diversity seriously decreases. In this paper, Gaussian mutation and chaos disturbance are introduced into ABC to overcome the shortcomings above. Applications of improved ABC algorithm on four benchmark optimization functions show marked improvement in performance over the basic ABC.

An improved artificial bee colony algorithm based on gaussian mutation and chaos disturbance

Data-driven lithology prediction for tight sandstone reservoirs based on new ensemble learning of conventional logs: A demonstration of a Yanchang member, Ordos Basin

An improved algorithm (AFSA-IWO) was developed based on the artificial fish swarm algorithm (AFSA) and invasive weed optimization (IWO). It introduces IWO, and improves its mechanism of the competitive exclusion to meet practical application. Convergence analysis was performed with some typical benchmark test functions and comparison was made with AFSA. At the same time, it uses the AFSA-IWO to optimize the PID parameters. The results showed that the approach presented better ability in leaping over the local extremum and enhancing local exploration, and can void blind searching in the later evolution period. So it is a global optimization algorithm with good feasibility and high efficiency.

An Improved Artificial Fish Swarm Algorithm and its Application

To tune PID controller’s parameters, the algorithm based on the Data-Model of Controlled plant is presented. With input and output data, Data Model as well as the feature data of controlled plant is identified by the Convolution Equation, and then the fastest control signal referenced to ideal fastest response is calculated according to the constraint condition of control signal. Finally, the closed loop control system of the controlled plant is designed, and the PID parameters are identified by the Least-Squares-Method (LSM) with the data of the fastest control signal.

PID Controller Parameters Identification Based on Data Model

To accommodate deep sea walking wheel the complex characteristics of deep sea environment such as randomness, non-linear and variability, the algorithm of Multi-Model-Reference Adaptation Fuzzy Control is presented to run the walking wheel system steadily. This control method incorporates the multiple reference models, fuzzy control and the conventional PID control; it runs efficiently by the control compensation deduced by the error of different phase-plane zones with the guidance of reference models.

Multi-Model Adaptation Fuzzy Control for the Deep Sea Walking Hydraulic Control System

Classical mean shift tracking algorithm doesnt show good performance when the tracked objects move fast, change in size or pose. This paper proposes an improved mean shift method used for vehicle tracking. Firstly, a position prediction model based on second order auto-regression process is used to find the initial position of mean shift iteration, reduce times of iteration and enhance the tracking accuracy. Secondly, we employ a position search method based on the weight image to improve the tracking result when the result of basic mean shift tracking is not good. The proposed algorithm is tested in a real traffic video to track a vehicle changing in size and pose with more accurate result than basic mean shift tracking algorithm.

Yi Xin Yin

Papers

An Improved Artificial Fish Swarm Algorithm and its Application

PID Controller Parameters Identification Based on Data Model

Multi-Model Adaptation Fuzzy Control for the Deep Sea Walking Hydraulic Control System

An Improved Mean Shift Algorithm for Vehicle Tracking