Variants in PSO

7/31/2019 Variants in PSO

1/17

ASSOCIATION RULE MINING USING MEMETIC

ALGORITHM

Team : Ashwini V

Divyamary PRangalakshmi B

Sumithra M

Under the Guidance of

Dr. S. KanmaniProfessor

Dept. of IT

1


2/17

Objective

To Propose an efficient methodology for mining

ARs using Memetic Algorithm

To compare the effectiveness of the algorithm

with Adaptive and Hybrid PSO methodologies

2


3/17

Extraction of interesting information or patterns from

data in large databases is known as data mining.

Data Mining

3


4/17

Association rule mining provides valuable information

in assessing significant correlations

It studies the frequency of items occurring together in

transactional databases

Relationship will be in the form of a rule: IF {X}, THEN {Y}

Parameters:

Support Level

Confidence Level

ASSOCIATION RULES

4


5/17

55

DATASET DESCRIPTION

Dataset Name No. of

Instances

No. of

Attributes

Attribute

characteristics

Lenses 24 3 Categorical

HabermansSurvival

306 3 Integer

Car Evaluation 1728 6 Categorical

Post Operative

Patient

90 8 Categorical,

Integer

Zoo 101 17 Categorical,

Integer


6/17

EXISTING SYSTEM

APRIORI ALGORITHM

Generate all frequent itemsets and gets all confident

association rules from those itemsets

FP GROWTH TREE

Stores information about frequent patterns in

the database

6


7/17

CONT..

DYNAMIC ITEMSET COUNTING

o It is alternate to apriori itemset generation

o Considers only itemsets whose subsets are all frequent

7


8/17

Most suitable in problems where multiple solutions are

required

GENETIC ALGORITHM is a global optimization method inspired

by biological mechanisms such as evolution and hereditary

Parallel implementation is easier

EVOLUTIONARY ALGORITHM

8


9/17

PARTICLE SWARM OPTIMIZATION

Particle swarm optimization is a population based

optimization Algorithm

Aims to simulate social behaviors in nature found in

insects, birds, fish, etc

Each data itemset are represented as particles

Driven by both personal and social influences

9


10/17

Pitfalls in Traditional PSO

The standard PSO algorithm can easily get trapped in

the local optima when solving complex multimodal

problems

Becomes computationally inefficient as it depends on

the function evaluators (FEs) required

10


11/17

PSO

Memetic

APSO

Hybrid

(PSO+DE)

(PSO+QC)

PROPOSED SYSTEM

11


12/17

12

Memetic Particle Swarm Optimization scheme incorporates

local search techniques in the standard Particle Swarm

Optimization algorithm, resulting in an efficient and effective

optimization method.

Two local searches

Solis and Wets local search strategy

Shuffled Frog Leaping Algorithm based search strategy

Memetic PSO


13/17

Adaptive PSO

Adaptive particle swarm optimization (APSO) perform global

search over the entire search space

Identifies one of the following four evolutionary states:

exploration, exploitation, convergence, and jumping outin

each generation

Enable the automatic control ofinertia weight, acceleration

coefficients, and other algorithmic parameters at run time

13


14/17

Hybrid

(PSO+DE) DEPSO is a strategy of Dual Evolution (DES) based on the

mechanism for sharing information.

Improves the drawbacks easy to drop into region

optimum and increases the performance with stableconvergence.

(PSO+QC)

The quantum theory of mechanics governs the

movement of swarm particles along with an interpolationbased recombination operator.

14


15/17

MONTH Rangalakshmi B

Sumithra M

Ashwini V

Divya Mary P

August -

September

Self Adaption of

control parameters

Memetic algorithm

(PSO + Local search)

October -

November

Hybrid model 1

(PSO + DE)

Hybrid model 2

(PSO + QC)

December Comparison of results and further Enhancement

WORK PLAN

15


16/17

BASE PAPERS

Jiuzhong Zhang , XuemingDing.: A Multi-Swarm Self-Adaptive

and Cooperative Particle Swarm Optimization, Engineering

Applications of Artificial Intelligence, Volume 24,pp. 958967,

2011.

Zhan, Z-H. and Zhang, J. and Li, Y. and Chung, H.S-H. (2009)

Adaptive particle swarm optimization. IEEE Transactions on

Systems Man, and Cybernetics Part B: Cybernetics, 39 (6).

pp. 1362-1381.

16


17/17

REFERENCES

Nickabadi,A. Ebadzadeh, M M. and Safabakhsh, R. A novel

particle swarm optimization algorithm with adaptive inertia

weight.Applied Soft Computing, 11,36583670, 2011.

Radha Thangaraj , Millie Pant , Ajith Abraham , Pascal Bouvry

.: Particle swarm optimization: Hybridization perspectives

and experimental illustrations, Applied Mathematics and

Computation 217 (2011) 52085226.

17

Variants in PSO

Documents

Transcript of Variants in PSO