APPLYING HONEY-BEE MATING OPTIMIZATION AND PARTICLE SWARM OPTIMIZATION FOR CLUSTERING PROBLEMS

2009 ◽  
Vol 26 (5) ◽  
pp. 426-431 ◽  
Author(s):  
Chui-Yu Chiu ◽  
I-Ting Kuo
Keyword(s):  
Particle Swarm Optimization ◽  
Honey Bee ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Clustering Problems ◽  
Honey Bee Mating Optimization

scholarly journals Using Particle Swarm Optimization, Genetic Algorithm, Honey Bee mating Optimization and Shuffle Frog Leaping Algorithm for solving OPF Problem with their Comparison

2015 ◽  
Vol 15 (3) ◽  
pp. 445
Author(s):  
Sajjad Ahmadnia ◽  
Ehsan Tafehi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Honey Bee ◽  
Power Flow ◽  
Particle Swarm ◽  
Test System ◽  
Mathematical Methods ◽  
Swarm Optimization ◽  
Shuffle Frog Leaping Algorithm ◽  
Honey Bee Mating Optimization

Today using evolutionary programing for solving complex, nonlinear mathematical problems like optimum power flow is commonly in use. These types of problems are naturally nonlinear and the conventional mathematical methods aren’t powerful enough for achieving the desirable results. In this study an Optimum Power Flow problem solved by means of minimization of fuel costs for IEEE 30 buses test system by Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Honey Bee Mating Optimization (HBMO) and Shuffle Frog Leaping Algorithm (SFLA), these algorithms has been used in MATLAB medium with help of MATHPOWER to achieving more precise results and comparing these results with the other proposed results in other published papers.

scholarly journals A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Lin Wang ◽  
Xiyu Liu ◽  
Minghe Sun ◽  
Jianhua Qu ◽  
Yanmeng Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Function Optimization ◽  
Search Process ◽  
Search Performance ◽  
Swarm Optimization ◽  
Logistic Mapping ◽  
Disturbance Term ◽  
Clustering Problems

A new method using collective responses of starling birds is developed to enhance the global search performance of standard particle swarm optimization (PSO). The method is named chaotic starling particle swarm optimization (CSPSO). In CSPSO, the inertia weight is adjusted using a nonlinear decreasing approach and the acceleration coefficients are adjusted using a chaotic logistic mapping strategy to avoid prematurity of the search process. A dynamic disturbance term (DDT) is used in velocity updating to enhance convergence of the algorithm. A local search method inspired by the behavior of starling birds utilizing the information of the nearest neighbors is used to determine a new collective position and a new collective velocity for selected particles. Two particle selection methods, Euclidean distance and fitness function, are adopted to ensure the overall convergence of the search process. Experimental results on benchmark function optimization and classic clustering problems verified the effectiveness of this proposed CSPSO algorithm.

scholarly journals An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Lin Wang ◽  
Xiyu Liu ◽  
Minghe Sun ◽  
Jianhua Qu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Membrane System ◽  
P System ◽  
Swarm Optimization ◽  
Local Optima ◽  
Distributed Parallel Computing ◽  
Active Membranes ◽  
Accelerate Convergence ◽  
Clustering Problems

An extended clustering membrane system using a cell-like P system with active membranes based on particle swarm optimization (PSO), named PSO-CP, is designed, developed, implemented, and tested. The purpose of PSO-CP is to solve clustering problems. In PSO-CP, evolution rules based on the standard PSO mechanism are used to evolve the objects and communication rules are adopted to accelerate convergence and avoid prematurity. Subsystems of membranes are generated and dissolved by the membrane creation and dissolution rules, and a modified PSO mechanism is developed to help the objects escape from local optima. Under the control of the evolution-communication mechanism, the extended membrane system can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. This extended clustering membrane system is compared with five existing PSO clustering approaches using ten benchmark clustering problems, and the computational results demonstrate the effectiveness of PSO-CP.

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