The Scientific World Journal

Volume 2014 (2014), Article ID 375358, 13 pages

http://dx.doi.org/10.1155/2014/375358

Research Article

An Improved Cockroach Swarm Optimization

I. C. Obagbuwa and A. O. Adewumi

School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Westville, Durban 4000, South Africa

Received 6 December 2013; Accepted 18 March 2014; Published 14 May 2014

Academic Editors: P. Agarwal, S. Balochian, V. Bhatnagar, and Y. Zhang

Copyright © 2014 I. C. Obagbuwa and A. O. Adewumi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Hunger component is introduced to the existing cockroach swarm optimization (CSO) algorithm to improve its searching ability and population diversity. The original CSO was modelled with three components: chase-swarming, dispersion, and ruthless; additional hunger component which is modelled using partial differential equation (PDE) method is included in this paper. An improved cockroach swarm optimization (ICSO) is proposed in this paper. The performance of the proposed algorithm is tested on well known benchmarks and compared with the existing CSO, modified cockroach swarm optimization (MCSO), roach infestation optimization RIO, and hungry roach infestation optimization (HRIO). The comparison results show clearly that the proposed algorithm outperforms the existing algorithms.

1. Introduction

Swarm intelligence (SI) is a method of computing whereby simple decentralized agents get information by interacting locally with one another and their environment [1]. The local information received is not controlled centrally; local interaction of agents results in amazing and emergent global patterns which can be adopted for solving problems [1].

SI algorithms draw inspiration from insects and animals social behaviour and have been proven in literature to be efficient in solving global optimization problems. Examples of existing SI algorithms include particle swarm optimization (PSO), ant colony optimization (ACO), and bee colony optimization (BCO). PSO based on bird social behaviour, introduced by Kennedy and Eberhart [2], has been applied to several problems, including power and management processes [3, 4] and combinatorial optimization problem in [5]. ACO based on ant social behaviour, introduced by Dorigo [6], has been applied to problems such as vehicle routing problem [7] and network routing problem [8]. BCO based on bees social behaviour, introduced by Pham et al. [9], has been applied to real world problems by Karaboga and his research group [10–12].

One of the recent developments in SI is cockroach optimization [13–16]. Cockroach belongs to Insecta Blattodea, abodes in warm, dark, and moist shelters, and exhibits habits which include chasing, swarming, dispersing, being ruthless and omnivorous, and food searching. Cockroaches interact with peers and respond to their immediate environment and make decisions based on their interaction such as selecting shelter, searching for food sources and friends, dispersing when danger is noticed, and eating one another when food is scarce.

The original cockroach swarm optimization (CSO) algorithm, introduced by Zhaohui and Haiyan [14], was modified by ZhaoHui with the introduction of inertial weight [15]. CSO algorithms [14, 15] mimic chase swarming, dispersion, and ruthless social behaviour of cockroaches.

Global optimization problems are considered as very hard problems, ever increasing in complexity. It became necessary to design better optimization algorithms; this necessitated the design of a better cockroach algorithm. This paper extends MCSO with the introduction of another social behaviour called hunger behaviour. Hunger behaviour prevents local optimum and enhances diversity of population. An improved cockroach swarm optimization (ICSO) is presented in this paper.

The organization of this paper is as follows: Section 2 presents CSO, MCSO, and ICSO models with algorithmic steps; Section 3 shows the experiments carried out and results obtained; the paper is summarised in Section 4.

2. Cockroach Swarm Optimization

CSO algorithm is a population based global optimization algorithm which has been applied to problems in literature including [17–19]. CSO [14] models are given as follows.

(1) Chase-Swarming Behaviour.

where is the cockroach position, step is a fixed value, rand is a random number within , is the personal best position, and is the global best position. Consider

where perception distance visual is a constant, , . Consider

(2) Dispersion Behaviour.

where rand is a -dimensional random vector that can be set within a certain range.

(3) Ruthless Behaviour.

where is a random integer within and is the global best position.

2.1. Modified Cockroach Swarm Optimization

ZhaoHui presented a modified cockroach swarm optimization (MCSO) [15] with the introduction of inertial weight to chase swarming component of original CSO as shown below. Other models remain as in original CSO.

Chase-swarming behaviour is as follows:

where is an inertial weight which is a constant.

2.2. Improved Cockroach Swarm Optimization

In this paper, MCSO is extended with additional component called hunger behaviour.

2.2.1. Hunger Behaviour

At interval of time, when cockroach is hungry, it migrates from its comfortable shelter and friends company to look for food [13, 20]. Hunger behaviour is modelled using partial differential equation (PDE) migration techniques [21]. Cockroach migrates from its shelter to any available food source within the search space. A threshold hunger is defined, when cockroach is hungry and threshold hunger is reached; it migrates to food source. Hunger behaviour prevents local optimum and enhances diversity of population.

PDE migration equation is described by Kerckhove [21]:

with .

Parameter is the controlling speed of the migration. is the population size, is time, and is location or position. is the population size at time in location with being the initial population distribution. Consider

The characteristic equations are

By integration, we have

Consider displacement = speed × time.

In , displaces .

satisfies migration equation at any initial population distribution [21].

Hunger behaviour is modelled as follows:

If

where denotes cockroach position, denotes cockroach migration from its present position, is a constant which controls migration speed at time , denotes food location, denotes hunger threshold, and hunger is a random number .

2.2.2. Improved Cockroach Swarm Optimization Models

(1) Chase-Swarming Behaviour.

where is an inertial weight which is a constant, step is a fixed value, rand is a random number within , is the personal best position, and is the global best position. Consider

where perception distance visual is a constant, , . Consider

(2) Hunger Behaviour. If ,

where denotes cockroach position, denotes cockroach migration from its present position, is a constant which controls migration speed at time , denotes food location, denotes hunger threshold, and hunger is a random number within .

(3) Dispersion Behaviour.

where rand is a -dimensional random vector that can be set within a certain range.

(4) Ruthless Behaviour.

where is a random integer within and is the global best position.

The algorithm for ICSO is illustrated in Algorithm 1 and its computational steps given as follows.

(1)Initialise cockroach swarm with uniform distributed random numbers and set all parameters with values.(2)Find and using (12) and (13).(3)Perform chase-swarming using (11).(4)Perform hunger behaviour using  (14)(5)Perform dispersion behaviour using  (15).(6)Perform ruthless behaviour using  (16).(7)Repeat the loop until stopping criterion is reached.

Series of experiments are conducted in Section 3 using established global optimization problems to test ICSO performance. The performance of ICSO is compared with that of existing algorithms RIO, HRIO, CSO, and MCSO.

INPUT: Fitness function:

set parameters and generate an initial population of cockroach

set  

for    do

      if    then

      end if

end for

for    do

      for    do

            for    do

                  if    then

                  end if

            end for

             if    then

             else

             end if

              if    then

            end if

       end for

       if    then

             Increment counters

       end if

      for    do

            if    then

            end if

       end for

       ;

end for

Check termination condition

Algorithm 1: An improved cockroach swarm optimization algorithm.

3. Simulation Studies

The speed, accuracy, robustness, stability, and searching capabilities of ICSO are evaluated in this section with 23 benchmark test functions. The test functions were adopted from [22–24]; any further information about the test functions can be found in these references. The test functions are of different characteristics such as unimodal , multimodal , separable , and nonseparable . Table 1 of this paper shows the test functions used, whose problem ranges from 2 to 30 in dimension as in [22–24].

Table 1: Benchmark test functions.

Number
Range

C
Functions
Description

1
−100, 100
30
US
Step

2
−100, 100
30
US
Sphere

3
−10, 10
30
US
Sumsquares

4
−100, 100
2
MS
Bohachevsky1

5
−100, 100
2
MN
Bohachevsky2

6
−100, 100
2
MN
Bohachevsky3

7
[0, 180]
20
UN
Sinusoidal20

8
−100, 100
30
UN
Quadric

9
−100, 100
2
UN
Easom

10
−10, 10
2
UN
Matyas

11
−5, 10
10
UN
Zakharov

12
−10, 10
24
UN
Powell

13
−10, 10
30
UN
Schwefel2.22

14
−30, 30
30
UN
Rosenbrock

15
−5.12, 5.12
30
MS
Rastrigin

16
−100, 100
2
MN
Schaffer1

17
−100, 100
30
MN
Schaffer2

18
−600, 600
30
MN
Griewangk

19
−32, 32
30
MN
Ackley

20
−5, 5
2
MN
Three hump camel back

21
−5, 5
2
MN
Six hump camel back

22
,
9
UN
Storn's Tchebychev
,

23
,
17

Storn's Tchebychev

for : , and  

for : , and .

dimension; C: characteristic; U: unimodal; S: seperable; N: non-separable.

All algorithms were implemented in MATLAB 7.14 (R2012a) and run on a computer with 2.30 GHz processor with 4.00 GB of RAM. Experimental setting of [13–15] is used for the experiments of this paper; experiment runs 20 times with maximum iteration 1000, perception distance , the largest step was , and inertia weight was ; we defined hunger threshold and hunger as a randomly generated number in each iteration for ICSO. Cockroach parameters [13] are used for RIO and HRIO; and , hunger threshold , and hunger as randomly generated number . Cockroach population size is used in this paper for all the algorithms. Further details about RIO, HRIO, CSO, and MSCO can be found in [13–15].

ICSO along with similar algorithms, that is, CSO, MSCO, RIO, and HRIO, was implemented with several simulation experiments conducted and reported. Success rate, average and best fitness, standard deviation (STD), and execution time in seconds are used as performance measure for comparative purpose (see Tables 2, 3, and 4 of this paper).

Table 2: Simulation results of RIO, HRIO, CSO, MCSO, and ICSO.

SN
Fn.
Dim.
Opt.

RIO
HRIO
CSO
MCSO
ICSO

1
Boha1
2
0
Ave.

0.0000

STD

0.0000

Best

0.0000
0.0000

Success
20/20
20/20
5/20
20/20
20/20

Time
1.137525
0.886356
23.913237
0.075212
0.097187

2
Boha2
2
0
Ave.

0.0000

STD

0.0000

Best

0.0000
0.0000

Success
20/20
20/20
4/20
20/20
20/20

Time
0.998178
0.946887
26.492095
0.072021
0.074106

3
Boha3
2
0
Ave.

0.0000

STD

0.0000

Best

0.0000
0.0000

Success
20/20
20/20
3/20
20/20
20/20

Time
1.089920
0.885252
25.028054
0.080908
0.068189

4
3camel
2
0
Ave.

STD

Best

Success
19/20
20/20
12/20
20/20
20/20

Time
4.231533
0.794983
18.281683
0.104132
0.078845

5
6camel
2

Ave.

STD

Best

Success

Time
0.406355
0.330198
5.723039
0.0945856
0.086637

6
Easom
2

Ave.

STD

Best

Success
20/20
20/20
20/20
20/20
20/20

Time
0.124022
0.107303
0.106738
0.077179
0.092393

7
Matyax
2
0
Ave.

7.5712

STD

Best

Success
20/20
20/20
11/20
20/20
20/20

Time
0.973322
0.711734
13.559576
0.88536
0.076693

8
Schaffer1
2

Ave.

STD

Best

Success
20/20
20/20
20/20
20/20
20/20

Time
0.109048
0.086433
0.119076
0.072400
0.081599

9
Schaffer2
2
0
Ave.

STD

Best

Success

Time
62.567654
31.415836
29.194283
0.084127
0.082320

Dim. denotes dimension. Opt. denotes optimum value. Boha1 denotes Bohachevsky1. Boha2 denotes Bohachevsky2. Boha3 denotes Bohachevsky3. 3camel denotes three hump camel back. 6camel denotes six hump camel back.

Table 3: Simulation results of RIO, HRIO, CSO, MCSO, and ICSO.

SN
Fn.
Dim.
Opt.

RIO
HRIO
CSO
MCSO
ICSO

10
Sphere
30
0
Ave.

STD

Best

Success
20/20
20/20
19/20
20/20
20/20

Time
0.617544
0.557871
25.378161
0.82512
0.199373

11
Rastrigin
30
0
Ave.

0.0000

STD

0.0000

Best

0.0000
0.0000

Success
20/20
20/20
5/20
20/20
20/20

Time
0.956329
0.826770
71.811170
0.175563
0.369987

12
Rosenbrock
30
0
Ave.

STD

0.0000
0.0000

Best

Success
0/20
0/20
0/20
0/20
0/20

Time
126.618734
127.469638
81.361663
76.084929
78.572185

13
Ackley
30
0
Ave.

STD

Best

Success
0/20
0/20
0/20
20/20
20/20

Time
122.216187
117.635854
82.227210
0.235012
0.192339

14
Quadric
30
0
Ave.

STD

Best

Success
20/20
20/20
20/20
20/20
20/20

Time
0.718785
0.512242
31.075809
0.247456
0.227244

15
Schwefel2.22
30
0
Ave.

STD

Best

Success
0/20
0/20
0/20
20/20
20/20

Time
128.445013
127.084387
79.924516
0.217104
0.219296

16
Griewangk
30
0
Ave.

0.0000

STD

0.0000

Best

0.0000
0.0000

Success
0/20
0/20
5/20
20/20
20/20

Time
126.872461
126.210153
70.852376
0.211351
0.210934

17
Sumsquare
30
0
Ave.

STD

Best

Success
0/20
0/20
0/20
20/20
20/20

Time
122.748646
125.154349
78.809270
0.273780
0.236129

18
Sinusoidal
30

Ave.

STD

1.0203

Best

Success
20/20
20/20
20/20
20/20
20/20

Time
0.204559
0.240200
0.234205
0.200361
0.217635

Dim. denotes dimension. Opt. denotes optimum value.

Table 4: Simulation results of RIO, HRIO, CSO, MCSO, and ICSO.

SN
Function
Dim.
Opt.

RIO
HRIO
CSO
MCSO
ICSO

19
Zakharov
30
0
Ave.

STD

Best

Success
0/20
0/20
0/20
20/20
20/20

Time
115.192226
114.691827
79.926232
0.205280
0.259202

20
Step
30
0
Ave.
0.0000
0.0000

0.0000
0.0000

STD
0.0000
0.0000

0.0000
0.0000

Best
0.0000
0.0000
0.0000
0.0000
0.0000

Success
20/20
20/20
16/20
20/20
20/20

Time
0.686403
0.633264
39.136696
0.239525
0.225102

21
Powell
24
0
Ave.

STD

Best

Success
2/20
12/20
0/20
20/20
20/20

Time
122.796991
92.876086
74.794730
1.527170
0.853751

22
ST
9
0
Ave.
0.0000
0.0000
0.0000
0.0000
0.0000

STD
0.0000
0.0000
0.0000
0.0000
0.0000

Best
0.0000
0.0000
0.0000
0.0000
0.0000

Success
20/20
20/20
20/20
20/20
20/20

Time
0.435911
0.426320
0.437944
0.431122
0.436741

23
ST
17
0
Ave.
0.0000
0.0000
0.0000
0.0000
0.0000

STD
0.0000
0.0000
0.0000
0.0000
0.0000

Best
0.0000
0.0000
0.0000
0.0000
0.0000

Success
20/20
20/20
20/20
20/20
20/20

Time
1.066161
1.052169
1.159830
1.089657
1.147114

Dim. denotes dimension. Opt. denotes optimum value.

ICSO locates minimum values for the tested benchmark problems such as Bohachevsky, Rastrigin, Easom, Schaffer, Step, and Storn’s Tchebychev problems as shown in Tables 2, 3, and 4. The comparison of the average performance of ICSO with that of RIO, HRIO, CSO, and MCSO is shown in Table 5; the comparison result clearly shows that ICSO outperforms other algorithms. Similarly, the best performance of ICSO with that of RIO, HRIO, CSO, and MCSO is shown in Table 6; ICSO has better performance than others.

Table 5: Comparison of average performance of RIO, HRIO, CSO, MCSO, and ICSO.

SN
Function
RIO
HRIO
CSO
MCSO
ICSO
Optimum

1
Bohachevsky1

0.0000
0

2
Bohachevsky2

0.0000
0

3
Bohachevsky3

0.0000
0

4
3 Hump camel back

0

5
6 Hump camel back

6
Easom

7
Matyax

0

8
Schaffer1

9
Schaffer2

0

10
Sphere

0

11
Rastrigin

0.0000
0

12
Rosenbrock

0

13
Ackley

0

14
Quadric

0

15
Schwefel2.22

0

16
Griewangk

0.0000
0

17
Sumsquare

0

18
Sinusoidal

19
Zakharov

0

20
Step
0.0000
0.0000

0.0000
0.0000
0

21
Powell

0

22
ST9
0.0000
0.0000
0.0000
0.0000
0.0000
0

23
ST17
0.0000
0.0000
0.0000
0.0000
0.0000
0

Number of good optimums

4
4
2
7
23

ST9 denotes Storn's Tchebychev 9. ST17 denotes Storn's Tchebychev 17.

Table 6: Comparison of best performance of RIO, HRIO, CSO, MCSO, and ICSO.

SN
Function
RIO
HRIO
CSO
MCSO
ICSO
Optimum

1
Bohachevsky1

0.0000
0.0000
0

2
Bohachevsky2

0.0000
0.0000
0

3
Bohachevsky3

0.0000
0.0000
0

4
3 hump camel back

0

5
6 hump camel back

6
Easom

7
Matyax

0

8
Schaffer1

9
Schaffer2

0

10
Sphere

0

12
Rosenbrock

0

14
Quadric

0

15
Schwefel2.22

0

16
Griewangk

0.0000
0.0000
0

17
Sumsquare

0

18
Sinusoidal

19
Zakharov

0

20
Step
0.0000
0.0000
0.0000
0.0000
0.0000
0

21
Powell

0

22
ST9
0.0000
0.0000
0.0000
0.0000
0.0000
0

23
ST17
0.0000
0.0000
0.0000
0.0000
0.0000
0

Number of good optimums

4
4
5
11
22

ST9 denotes Storn's Tchebychev 9. ST17 denotes Storn's Tchebychev 17.

ICSO algorithm has consistent performance in each iteration. This is proved by very low standard deviation of the average optimal recoded during experiments. The ICSO average optimal STD is compared with the STD of RIO, HRIO, CSO, and MCSO in Table 7. ICSO has better minimum STD than others.

Table 7: Comparison of standard deviation of mean global optimum values of RIO, HRIO, CSO, MCSO, and ICSO.

SN
Function
RIO
HRIO
CSO
MCSO
ICSO

1
Bohachevsky1

0.0000

2
Bohachevsky2

0.0000

3
Bohachevsky3

0.0000

4
3 hump camel back

5
6 hump camel back

6
Easom

7
Matyax

8
Schaffer1

9
Schaffer12

5.3095

10
Sphere

11
Rastrigin

0.0000

12
Rosenbrock

0.0000
0.0000

13
Ackley

5.8258

14
Quadric

15
Schwefel2.22

16
Griewangk

0.0000

17
Sumsquare

18
Sinusoidal

1.0203

19
Zakharov

20
Step
0.0000
0.0000

0.0000
0.0000

21
Powell

22
ST9
0.0000
0.0000
0.0000
0.0000
0.0000

23
ST17
0.0000
0.0000
0.0000
0.0000
0.0000

Number of good STD

2
2
2
4
23

ST9 denotes Storn's Tchebychev 9. ST17 denotes Storn's Tchebychev 17.

ICSO locates good solutions in each experiment; this is proved by the success rate of the algorithm. Table 8 shows the comparison of the success rate of the proposed algorithm with the existing algorithms RIO, HRIO, CSO, and MCSO. ICSO has success rate in all test functions except Rosenbrock.

Table 8: Comparison of success performance of RIO, HRIO, CSO, MCSO, and ICSO.

SN
Function
RIO
HRIO
CSO
MCSO
ICSO

1
Bohachevsky1
1
1
2.5
1
1

2
Bohachevsky2
1
1
0.2
1
1

3
Bohachevsky3
1
1
0.15
1
1

4
3 hump camel back
0.95
1
0.6
1
1

5
6 hump camel back
1
1
0.95
1
1

6
Easom
1
1
1
1
1

7
Matyax
1
1
0.55
1
1

8
Schaffer1
1
1
1
1
1

9
Schaffer2
0.1
0.65
0
1
1

10
Sphere
1
1
0.95
1
1

11
Rastrigin
1
1
0.25
1
1

12
Rosenbrock
0
0
0
0
0

13
Ackley
0
0
0
1
1

14
Quadric
1
1
1
1
1

15
Schwefel2.22
0
0
0
1
1

16
Griewangk
0
0
0.25
1
1

17
Sumsquare
0
0
0
1
1

18
Sinusoidal
1
1
1
1
1

19
Zakharov
0
0
0
1
1

20
Step
1
1
0.8
1
1

21
Powell
0.1
0.6
0
1
1

22
ST9
1
1
1
1
1

23
ST17
1
1
1
1
1

Number of 100% success rates

14
15
6
22
22

ST9 denotes Storn's Tchebychev 9. ST17 denotes Storn's Tchebychev 17.

ICSO utilizes minimum time in executing the selected test function. Table 9 shows the comparison of the execution time of ICSO and that of RIO, HRIO, CSO, and MCSO; ICSO is shown to have utilized minimum time.

Table 9: Comparison of exec1ution time of RIO, HRIO, CSO, MCSO, and ICSO.

SN
Function
RIO
HRIO
CSO
MCSO
ICSO

1
Bohachevsky1
1.137525
0.886356
23.913237
0.075212
0.097187

2
Bohachevsky2
0.998178
0.946887
26.492095
0.072021
0.074106

3
Bohachevsky3
1.089920
0.885252
25.028054
0.080908
0.068189

4
3 hump camel back
4.231533
0.794983
18.281683
0.104132
0.078845

5
6 hump camel back
0.406355
0.330198
5.723039
0.0945856
0.086637

6
Easom
0.124022
0.107303
0.106738
0.077179
0.092393

7
Matyax
0.973322
0.711734
13.559576
0.88536
0.076693

8
Schaffer1
0.109048
0.086433
0.119076
0.072400
0.081599

9
Schaffer2
62.567654
31.415836
29.194283
0.084127
0.082320

10
Sphere
0.617544
0.557871
25.378161
0.82512
0.199373

11
Rastrigin
0.956329
0.826770
71.811170
0.175563
0.369987

12
Rosenbrock
126.618734
127.469638
81.361663
76.084929
78.572185

13
Ackley
122.216187
117.635854
82.227210
0.235012
0.192339

14
Quadric
0.718785
0.512242
31.075809
0.247456
0.227244

15
Schwefel2.22
128.445013
127.084387
79.924516
0.217104
0.219296

16
Griewangk
126.872461
126.210153
70.852376
0.211351
0.210934

17
Sumsquare
122.748646
125.154349
78.809270
0.273780
0.236129

18
Sinusoidal
0.204559
0.240200
0.234205
0.200361
0.217635

19
Zakharov
115.192226
114.691827
79.926232
0.205280
0.259202

20
Step
0.686403
0.633264
39.136696
0.239525
0.225102

21
Powell
122.796991
92.876086
74.794730
1.527170
0.853751

22
ST9
0.435911
0.426320
0.437944
0.431122
0.436741

23
ST17
1.066161
1.052169
1.159830
1.089657
1.147114

Number of minimum execution times

—
2
—
9
12

ST9 denotes Storn's Tchebychev 9. ST17 denotes Storn's Tchebychev 17.

To determine the significant difference between the performance of the proposed algorithm and the existing algorithms, test statistic of Jonckheere-Terpstra (J-T) test was conducted using the statistical package for the social science (SPSS). The Null hypothesis test for J-T test is that there is no difference among several independent groups. As the usual practice in most literature, value threshold value for hypothesis test was set to 0.05. If value is less than 0.05, the Null is rejected which means there is significant difference between the groups. Otherwise the Null hypothesis is accepted. Table 10 shows the result of J-T test; value (Asymp. Sig.) was computed to be 0.001. The value is less than the threshold value 0.05; therefore, there is significant difference in performance of ICSO and that of RIO, HRIO, CSO, and MCSO for benchmarks evaluated.

Table 10: Jonckheere-Terpstra test statistic.

Fitness

Number of levels in algorithm
5

114

Observed J-T statistic
1952.000

Mean J-T statistic
2599.500

STD of J-T statistic
199.355

Standard data of J-T statistic

Asymp. Sig. (2-tailed)
0.001

Grouping variable: algorithm.

Effect size of the significant difference is the measure of the magnitude of the observed effect. The effect size , () of the significant difference of J-T test, was calculated as

where is the standard data of J-T statistic as shown in Table 10, is the total number of samples, and . Consider

where denotes observed J-T statistic, denotes the mean J-T statistic, and denoted the standard deviation of J-T statistic. Consider

The distance between the observed data and the mean in units of standard deviation is absolute value of ( is negative when observed data is below the mean and positive when above). The effect size is of medium size, using Cohen’s guideline on effect size [25, 26]. The statistics of effect size shows that there is significant difference of medium magnitude between proposed algorithm and existing algorithms.

4. Conclusion

Cockroach swarm optimization algorithm is extended in this paper with a new component called hunger component. Hunger component enhances the algorithm diversity and searching capability. An improved cockroach swarm optimization algorithm is proposed. The efficiency of the proposed algorithm is shown through empirical studies where its performance was compared with that of existing algorithms, that is, CSO, MSCO, RIO, and HRIO. Results show its outstanding performance compared to the existing algorithms. Application of the algorithm to real life problems can be considered in further studies.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgment

The authors thank College of Agriculture, Engineering and Science, University of KwaZulu-Natal, for supporting this paper through bursary award.

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