Fair Comparison of Population-based Heuristic Approaches

The Evils of Competitive Testing

Anikó Csébfalvi and György Csébfalvi

University of Pécs, Pécs, Hungary

Keywords: Heuristic Algorithm, Population-based Metaheuristic Algorithm, Statistical Test, Nonparametric Test,

Statistical Comparison, Sampling Theorem, Sample Design and Analysis Kolmogorov-Smirnov Test.

Abstract: 17 years ago, Hooker (1995) presented a pioneering work with the following title: "Testing Heuristics: We

Have It All Wrong". If we ask the question now: "Do we have it all wrong?" the answer will be undoubtedly

yes. The problem of the fair comparison remained essentially the same in the heuristic community. When

we use stochastic methods in the optimization (namely heuristics or metaheuristics with several tunable

parameters and starting seeds) then the usual presentation practice: "one problem - one result" is extremely

far from the fair comparison. From statistical point of view, the minimal requirement is a so-called "small-

sample" which is a set of results generated by independent runs and an appropriate "small-sample-test"

according to the theory of the experimental design and evaluation and the protocol used for example, in the

drug development processes. The viability and efficiency of the proposed statistically correct "bias-free"

nonparametric methodology is demonstrated using a well-known nonlinear structural optimization example

on the set of state-of-the-art heuristics. In the motivating example we used the presented solutions as a

small-sample generated by a "hyperheuristic" and we test its quality against ANGEL, where the

"supernatural" hybrid metaheuristic ANGEL combines ant colony optimization (AN), genetic algorithm

(GE) and a gradient-based local search (L) strategy. ANGEL is an "essence of the different but at the same

time similar heuristic approaches". The extremely simple and practically tuning-free ANGEL presents a

number of interesting aspects such as extremely good adaptability and the ability to cope with totally

different large real applications from the highly nonlinear structural optimization to the long-term

optimization of the geothermal energy utilization.

1 INTRODUCTION

The problem of fair comparison, as a fundamental

requirement of the evaluation of the real progress is

a general problem of the heuristic community

(Hooker, 1995).

When we use stochastic algorithms, the usual

presentation practice: "one problem - one result",

which is probable the first (most promising) element

of a larger ordered list, is extremely far from the fair

comparison. From statistical point of view, the

minimal requirement is a so-called small-sample

which is a set of results generated by 10-30

independent runs and an appropriate small-sample-

test according to the theory of the experimental

design and evaluation and the protocol used for

example, in the drug development processes. We

have to mention it, that even the usual mean or

standard deviation parameter may be misleading or

wrong when the distribution function is far from the

"normality". When the sample size is small, then the

nonparametric version of the Kolmogorov-Smirnov

test (NKST) or any other appropriate nonparametric

test may be the correct solution of the fair

comparison problem (Csébfalvi, 2012).

The measuring of computational efficiency is

generally a more complicated task. In this case, we

have to define an appropriate "timeless" measure,

which is invariant to progress of the optimization

methodology and computational technology and able

to characterize efficiency of a given approach as a

whole. From this point of view the solution time is

one of the worst from such measures, because (1) we

have to replace the real running times with

hypothetical but comparable times, and (2) we have

to eliminate the effect of "polished code - readable

code" like conflicts somehow to sure the fair and

bias-free comparison. In our opinion is simple: we

have to replace the solution time with a measure

306

Csébfalvi A. and Csébfalvi G..

Fair Comparison of Population-based Heuristic Approaches - The Evils of Competitive Testing.

DOI: 10.5220/0004168403060309

In Proceedings of the 4th International Joint Conference on Computational Intelligence (ECTA-2012), pages 306-309

ISBN: 978-989-8565-33-4

Copyright

c

2012 SCITEPRESS (Science and Technology Publications, Lda.)

which is invariant to the environmental factors and

problem types and able to characterize the

computational efforts of the problem solving process

as a whole. A good and generally usable measure

may be the sum of times each variable has obtained

a value (the total number of variable settings)

divided by the number of variables. Naturally, we

have to assume, that the total number of variable

settings contains the number of settings of the

parameter-setting or fine-tuning phase also. We

note, when the approach has several not necessarily

independent parameters, than the preliminary fine-

tuning may be more complicated than the real

problem solving process. This phase may mean, for

example, a complete "experimental design and

analysis" like time-consuming step in the problem

solving process.

2 STATISTICAL COMPARISON

In this paper we present a theoretically correct

comparison methodology, which can be used to

compare two or more stochastic algorithms, or to

evaluate the efficiency of a potential improvement

for a given algorithm. The statistical analysis is a

very important element of the evaluation of the real

progress. In the heuristic community, the "fashion

change" not necessarily means real improvement,

and the "improved", "enhanced", "hybridized", etc.

versions not necessarily give better results than the

original algorithms. The development sometimes is

driven by a specific problem set, on which the

original algorithm is unable to produce the

"expected better results" comparing with the

competitors.

NKST for two samples obtained by running two

competitive heuristics independently several times

(10-30) the following:

xFxFH

2HEURISTIC 1HEURISTIC

:

0

,

(1)

that is, the two samples are from populations with

the same distribution function.

3 EXAMPLE

We illustrate the essence of the methodological

problems connected to the fair comparison by a

popular structural optimization problem. In this

nonlinear ten-bar truss (T10) weight minimization

problem the design-variables are element cross-

section areas and implicit functions define the

response-variables, namely, the nodal displacements

and the element stresses for the given load case (see

Figure 1).

In the last decades, according to the challenging

but sometimes frustrating nature of this problem and

the progress of the optimization methodology and

computational technology, it was investigated by

several authors to demonstrate that their algorithm

seems to be the best to date (it is robust, effective,

and efficient).

The optimal solution of the problem is unknown

but it is well-known that it has several more or less

similar local optima according to the "hills and

dales" like nature of the design space. In Table 1 we

present the most important results obtained by using

totally different methodological approaches.

The detailed investigation of the results can be

found in Csébfalvi (2012). In this paper we only

want to point out that the results are practically

invariant to the year of publication which means that

we have to evaluate the real progress very carefully.

In other words, we can reach the area of alternative

optima using different solution searching strategies

and without knowledge about the real computational

efficiency we cannot discriminate among the

presented approaches.

360 in

360 in

100 kips 100 kips

1

246

351 2

34

56

7

8

9

10

y

x

360 in

Figure 1: The benchmark-example.

In this motivating example we assume that the

presented twenty solutions is a small-sample which

is generated by a "hyperheuristic" and we test its

quality against ANGEL developed by Csébfalvi

(2007, 2011) for engineering optimization. The

"supernatural" hybrid metaheuristic ANGEL

combines ant colony optimization (AN), genetic

algorithm (GE) and a gradient-based local search (L)

strategy. We have to note, that according to the

current terminology "hyperheuristic" means a

metaheuristic set with a problem-specific selection

mechanism.

The extremely simple and practically tuning-free

ANGEL presents a number of interesting aspects

FairComparisonofPopulation-basedHeuristicApproaches-TheEvilsofCompetitiveTesting

307

such as extremely good adaptability and the ability

to cope with totally different large scale real

applications from the highly nonlinear structural

optimization to the long-term optimization of the

geothermal energy utilization (Csébfalvi and

Schreiner, 2011).

Table 1: The most important results for T10.

Year Authors Weight (lb)

1969 Venkayya-Khot-Reddy 5084.90

1971 Gellatly-Berke 5112.00

1974 Schimit-Farshi 5089.00

1976 Rizzi 5076.66

1976 Schimit-Miura 5076.85

1976 Dobbs-Nelson 5080.00

1976 Schimit-Miura 5107.30

1979 Haug-Arora 5061.60

1979 Khan-Wilmert-Thornton 5066.98

1985 Haftka 5060.80

1991 Adeli-Kamal 5052.00

1992 Galante 4987.00

1994 Memari-Fuladgar 4981.10

1997 Ghasemi-Hinton-Wood 5095.65

1999 Lemonge 5060.92

2004 Lee-Geem 5057.88

2004 Lemonge-Barbosa 5069.09

2007 Li-Huang-Liu-Wu 5060.92

2009 Kaveh-Talatahari 5056.56

2009 Koohestani-Azad 5060.90

ANGEL has only three "tunable" parameters

{P,G,I}, where P is the size of the population, G is

the number of generations, I is the maximal number

of local search iterations. Naturally, the maximal

number of local search iterations means only a

possibility, the procedure terminates when it reaches

a size limit or a local minimum. The gradient-based

L, try to make a better (lighter) feasible or a less

unfeasible design from the current design obtained

by AN or GE. The result of L will be the "locally

best mutation".

The ANGEL sample was generated by 20

independent runs according to the number of results

given by the state-of-the-art methods to date. In the

investigation, the relative percent constraint

tolerance was 0.001 %. We have to note, that we

applied the original highly nonlinear "potential

energy minimization model" without simplifications.

In procedure L exact analytical derivatives were

used.

In Table 2 we show an ordered ANGEL sample

of 20 generated by the following settings:

{P,G,I} = {100, 10, 10} (2)

Table 2: A random ordered sample of 20 for T10.

index Weight (lb) index Weight (lb)

1 5063.27 11 5070.08

2 5064.80 12 5072.46

3 5065.72 13 5072.79

4 5066.11 13 5073.08

5 5067.14 15 5073.72

6 5067.70 16 5073.86

7 5068.33 17 5074.14

8 5068.52 18 5075.08

9 5068.69 19 5076.08

10 5069.71 20 5076.26

NKST (we reject the null-hypothesis) and the

results of Table 2 and Figure 3 reveal that ANGEL

is robust and able to produce good quality solutions

within reasonable time without problem-specific

preliminary investigation (fine-tuning). According to

our computational experiences the range is one of

the best measures of the robustness:

5076.26 - 5069.71 = 6.55 (3)

W = [ 5063.269 , 5076.264 ]

1 100

0

7314.978

5063.2695063.269

Figure 2: ANGEL searching history.

4 CONCLUSIONS

In this paper we presented a statistically correct

methodology for to compare the efficiency of

population-based heuristic approaches developed to

generate good quality solutions within reasonable

time for different optimization problems.

When we use stochastic methods to solve

optimization problems, then the usual presentation

practice: "one problem - one result" is extremely far

from the fair comparison. From statistical point of

view, the minimal requirement is the presentation of

IJCCI2012-InternationalJointConferenceonComputationalIntelligence

308

a small-sample generated by independent runs. The

fair competitive testing needs an appropriate

nonparametric test according to the theory of the

experimental design and evaluation.

The viability and efficiency of the proposed

statistically correct methodology is demonstrated

using the well-known nonlinear ten-bar truss

optimization example on a set of approaches

developed in the last decades. In this motivating

example, we assumed that the presented solutions

form a small-sample generated by a "hyperheuristic"

and we tested its quality against a "supernatural"

hybrid metaheuristic ANGEL which combines ant

colony optimization (AN), genetic algorithm (GE)

and a gradient-based local search (L) strategy.

REFERENCES

Adeli, H., Kamal, O., (1991). Efficient optimization of

plane trusses, Advances in Engineering Software; 13

(3), 116–122.

Barr, R.S., Goldenm, B. L., Kelly, J. P., Resende, M. G.

C., Stewart, W. R., (1995). Design and Reporting on

Computational Experiments with Heuristic Methods.

Journal of Heuristics, 1, 9-32.

Csébfalvi, A., (2007). Angel method for discrete

optimization problems. Periodica Polytechnica: Civil

Engineering, 51/2 37–46, doi: 10.3311/pp.ci.2007-

2.06, http://www.pp.bme.hu/ci.

Csébfalvi, A., (2009). A hybrid meta-heuristic method for

continuous engineering optimization. Periodica

Polytechnica: Civil Engineering, 53 (2), 93–100, doi:

10.3311/pp.ci.2009-2.05.

Csébfalvi, A., (2011). Multiple constrained sizing-shaping

truss-optimization using ANGEL method. Periodica

Polytechnica: Civil Engineering, 55 (1), 81–86, doi:

10.3311/pp.ci.2011-1.10, http://www.pp.bme.hu/ci.

Csébfalvi, A., Schreiner, J., (2011) A Net Present Value

Oriented Hybrid Method to Optimize the Revenue of

Geothermal Systems with Respect to Operation and

Expansion, In: Tsompanakis, Y., Topping, B.H.V.

(Eds.) Proceedings of the Second International

Conference on Soft Computing Technology in Civil,

Structural and Environmental Engineering. Chania,

Greece, Civil-Comp Press, Stirling, UK.

Dobbs, M. W., Nelson, R. B., (1976). Application of

optimality criteria to automated structural design.

AIAA Journal, 14 (10), 1436–43.

El-Sayed, M. E., Jang, T. S., (1994). Structural

optimization using unconstrained non-linear goal

programming algorithm. Computers and Structures,

52 (4), 723–727.

Galante, M., (1992). Structures optimization by a simple

genetic algorithm. In Proceedings of Numerical

Methods in Engineering and Applied Sciences,

CIMNE: Barcelona, Spain, 862–870.

Haftka, R. T., Kamat, M. P., (1985). Elements of

Structural Optimization, Martinus Nighoff: Dordrecht.

Haug E. J., Arora, J. S., (1979) Applied Optimal Design,

Wiley: New York.

Hooker, J. N., (1995). Testing Heuristics: We Have It All

Wrong. Journal of Heuristics, 1, 33-42.

Kaveh, A., Talatahari, S., (2009) Particle swarm

optimizer, ant colony strategy and harmony search

scheme hybridized for optimization of truss structures.

Computers and Structures, 87 (5-6), 267-283.

Khan, M. R., Willmert, K. D., Thornton, W. A., (1979).

An optimality criterion method for large-scale

structures. AIAA Journal 17 (7), 753—61.

Koohestani, K., Kazemzadeh Azad, S., (2009). An

Adaptive Real-Coded Genetic Algorithm for Size and

Shape Optimization of Truss Structures. In Topping,

B.H.V., Tsompanakis Y. (Eds.), Proceedings of the

First International Conference on Soft Computing

Technology in Civil, Structural and Environmental

Engineering, Civil-Comp Press, Stirlingshire, UK.

Lee, K. S., Geem, Z. W., (2004). A new structural

optimization method based on the harmony search

algorithm,

Computers and Structures, 82 (9-10), 781-

798.

Lemonge, A. C. C., (1999). Application of Genetic

algorithms in Structural Optimization Problems. Ph.D.

Thesis, Program of Civil Engineering–COPPE,

Federal University of Rio de Janeiro, Brazil.

Lemonge, A. C. C., Barbosa, H. J. C., (2004). An adaptive

penalty scheme for genetic algorithms in structural

optimization. International Journal of Numerical

Methods in Engineering, 59, 703–736.

Li, L. J., Huang, Z. B., Liu, F., Wu, Q. H., (2007). A

heuristic particle swarm optimizer for optimization of

pin connected structures. Computers and Structures,

85(7-8), 340-349.

Rizzi, P., (1976). Optimization of multiconstrained

structures based on optimality criteria. In Proceedings

of 17th Structures, Structural Dynamics, and

Materials Conference, King of Prussia, PA.

Schmit, L. A. Jr, Farshi, B., (1974). Some approximation

concepts for structural synthesis. AIAA Journal, 12

(5), 692–9.

Schmit, L. A., Miura, H., (1976). Approximation concepts

for efficient structural synthesis. NASA CR-2552,

Washington, DC: NASA.

Venkayya, V. B., (1971). Design of optimum structures.

Computers and Structures, 1 (1–2), 265–309.

FairComparisonofPopulation-basedHeuristicApproaches-TheEvilsofCompetitiveTesting

309