The 2013 Annual Meeting of The Institute of Electrical Engineers of Japan, Nagoya University, Nagoya, Japan, March 20-22, 2013

Multiple Food Sources for Composing Harvest

Season Artificial Bee Colony Algorithm on

Economic Dispatch Problem

A.N. Afandi* and Hajime Miyauchi (Kumamoto University)

1. INTRODUCTION

Recent years evolutionary methods are frequent used

to solve optimization problems. A novel method of this

group is Artificial Bee Colony (ABC) algorithm imitated

foraging behavior of honeybees in nature [1]. Presently,

the important challenges of the ABC are devoted to

convergence speed, searching mechanism and real

applications [2], [5]. As part of works to improve the ABC

becomes Harvest Season Artificial Bee Colony (HSABC),

this paper introduces multiple food sources for

composing the HSABC applied to economic dispatch

problem.

2. MULTIPLE FOOD SOURCES

In these studies, a food source expresses a flower

and multiple food sources (MFS) are used to mimic flowers

of harvest season. The MFS is preceded by foraging for

the first food source. The first food source is searched

by using expression (1) adopted from the classical ABC.

One others of the MFS are generated by equation (2)

around the first food source to perform the HSABC.

(1)

(2)

where i is the ith solution of the food source, k

{1,2,3,…,SN}, SN is the number of solutions,

j{1,2,3,…,D}, D is the variable number of objective

function problem, vij is food position, Øij is a random

number within [-1,1], xij is a current food, xkj is random

neighbor of xij, xfj is random harvest neighbor of xkj,

Hiho is harvest season food position, ho{2,3,…,FT},

f{1,2,3,…,SN}, and FT is the total number of flowers

for harvest season.

Contribution of the MFS is shown in economic dispatch

(ED) computation. The ED problem considers an emission

dispatch during minimizing cost as single objective

function of optimization and it is composed to be

Combined Economic Emission Dispatch (CEED) [3], [4].

3. SIMULATION RESULTS

In these works, the MFS is demonstrated in the CEED

considered 283.4 MW of total load of IEEE-30 bus system.

Determining solution is controlled by colony size=100,

food source=50, limit food source=50 and foraging

cycles=100. By considering 0.5 of compromised factor,

the CEED is constrained by equilibrium between demands

and power outputs, maximum and minimum powers of

generating units, and 5% of voltage limit.

A set population is shown in Figure 1 for initiating

50 values of candidate solution in every foraging for

the food. This figure illustrates the random foods for

the MFS at certain position. By indexing in Figure 2 for

all period of cycles, positions of the MFS are performed

on Figure 3, Figure 4 and figure 5. Figure 6 shows the

combined involvement of each food source during

computation captured within fifth iterations.

Fig. 1. Initial population of the candidate solution

Fig. 2. Random index of multiple food sources

-

50

100

150

200

250

0 10 20 30 40 50

Foo

d c

and

idat

es (

MW

)

Population

G1 G2 G3 G4 G5 G6

-

10

20

30

40

50

60

0 20 40 60 80 100

Ran

do

m in

dex

Iterations

j k f1 f2

The 2013 Annual Meeting of The Institute of Electrical Engineers of Japan, Nagoya University, Nagoya, Japan, March 20-22, 2013

Fig. 3. Position of food source 1

Fig. 4. Position of food source 2

Fig. 5. Position of food source 3

Fig. 6. Combined position of multiple food sources

Every position of food source is associated with

population of candidate solution which is explored as

the solution at certain cycle. Combined position of the

MFS is applied to solve the CEED for determining solution

in every cycle. Final solutions of simulation using

combined MFS are given in Table 1 for powers, costs and

emissions. According to this table, generating units

produce 289.7 MW for supplying 283.4 MW of load demand

with 6.3 MW of power loss. The joining power stations

needs 1449.055 $/hr of total payment for fuel cost and

emission cost. By considering 0.5 of compromised factor,

minimum cost value of the CEED is remained in 9

iterations.

Table 1. Numerical Results of computation

Units Power (MW)

Emission (kg/hr)

Fuel cost ($/hr)

Emission Cost ($/hr)

G1 126.274 84.991 312.343 152.269

G2 49.679 69.705 130.127 124.883

G3 28.338 46.903 78.527 84.032

G4 31.696 53.976 111.389 96.103

G5 26.579 45.081 97.398 80.766

G6 27.134 45.104 99.809 80.807

Total 289.700 345.758 829.594 619.461

4. CONCLUSIONS

The simulation results demonstrated successful

application of multiple food sources on economic

dispatch through the CEED problem using IEEE-30 bus as

a test system. By using three food sources, the

convergence speed is quick to select the solution. From

these works, the effectiveness application on the real

sample system is important for future investigations.

References

(1). Dervis Karaboga, “An Idea Based on Honey Bee Swarm for Numerical Optimization,” in Technical Report-TR06, Erciyes University, Turkey, Oct. 2005.

(2). Efren Mezura Montes, Mauricio Damian Araoz, Omar Centina Dominges, “Smart Flight and Dynamic Tolerances in the Artificial Bee Colony for Constrained Optimization,” in

Proc. IEEE Congress on Evolutionary Computation, CEC, pp. 1-8, July 2010.

(3). K. Sathish Kumar, V.Tamilselvan, N.Murali, R.Rajaram,

N.Shanmuga Sundaram and T.Jayabarathi, “Economic load dispatch with emission constraints using various PSO algorithm,” WSEAS Transaction on Power System, vol. 9,

pp. 598-607, Sept. 2008 (4). R.Gopalakrishnan, A.Krishnan, “A novel combined

economic and emission dispatch problem solving technique

using non-dominated ranked genetic algorithm,” European Journal of Scientific Research, vol. 64, pp. 141-151, Nov. 2011.

(5). X.T. Li, X.W. Zhao, J.N. Wang, M.H. Yin, “Improved Artificial Bee Colony for Design of a Reconfigurable Antenna Array with Discrete Phase Shifters,” in Progress

in Electromagnetics Research C, vol. 25, pp.193-208, 2012.