Page 60 - Fister jr., Iztok, and Andrej Brodnik (eds.). StuCoSReC. Proceedings of the 2016 3rd Student Computer Science Research Conference. Koper: University of Primorska Press, 2016
P. 60
le 1: Results obtained by the HSA-CS on the four design optimization problems.
Problem min max mean md std
Welded beam 1.724852309 1.724852309 1.724852309 1.724852309
Pressure vessel 0
Speed reducer 6059.714 6410.087 6095.32 6059.714 88.270
Spring design 2996.218 2996.218 2996.218 2996.218
0.01266523 0.0129087 0.01269661 0.01267089 0
5.402618 *10e−5
Table 2: Comparison with state-of-the-art.
Welded beam
Method min max mean std
Gandomi et al [7] 1.8786560 0.2677989
1.7312 2.3455793 1.741913
Akay et al.[1] 1.724853 0.031
Brajevic et al. [3] 1.724852 – 1.724852 0.0000017
Baykasoglu et al. [2] 1.724852309
1.724852 – 0
This study mean 0
1.724852 1.724852 6447.736
Method 6245.308144 std
Gandomi et al [6] 1.724852309 1.724852309 6192.116211 502.693
6064.33605261 205.00
Akay et al.[1] Pressure vessel 6095.32
Brajevic et al. [3] 204
Baykasoglu et al. [2] min max mean 11.28785324
3007.1997
This study 6059.714 6495.347 2997.058412 88.270
2994.471072
Method 6059.714736 – 2996.514874 std
Gandomi et al [6] 2996.218 4.9634
6059.714335 –
Akay et al.[1] mean –
Brajevic et al. [3] 6059.71427196 6090.52614259 0.01350052 0.00000598
Baykasoglu et al. [2]
6059.714 6410.087 0.012709 0.09
This study 0.012683 0
Speed reducer 0.0126770446
Method 0.01269661 std
Gandomi et al [7] min max 0.001420272
Akay et al.[1] 3000.9810 3009 0.012813
Brajevic et al. [3] 0.00000331
Baykasoglu et al. [2] 2997.058412 – 0.0127116883
5.402618*10e−5
This study 2994.471066 –
2996.372698 2996.669016
2996.218 2996.218
Spring design
min max
0.01266522 0.0168954
0.012665 –
0012665 –
0.0126653049 0.0000128058
0.01266523 0.0129087
[3] Ivona Brajevic and Milan Tuba. An upgraded artificial Applications, 22(6):1239–1255, 2013.
bee colony (abc) algorithm for constrained
optimization problems. Journal of Intelligent [8] Uroˇs Mlakar, Iztok Fister Jr., and Iztok Fister. Hybrid
Manufacturing, 24(4):729–740, 2013. self-adaptive cuckoo search for global optimization.
Swarm and Evolutionary Computation, 29:47 – 72,
[4] Radovan R Bulatovi´c, Goran Boˇskovi´c, Mile M 2016.
Savkovi´c, and Milomir M Gaˇsi´c. Improved cuckoo
search (ics) algorthm for constrained optimization [9] Shyue-Jian Wu and Pei-Tse Chow. Genetic algorithms
problems. Latin American Journal of Solids and for nonlinear mixed discrete-integer optimization
Structures, 11(8):1349–1362, 2014. problems via meta-genetic parameter optimization.
Engineering Optimization+ A35, 24(2):137–159, 1995.
[5] JZ Cha and RW Mayne. Optimization with discrete
variables via recursive quadratic programming: Part [10] Xin-She Yang and Suash Deb. Cuckoo search via l´evy
2—algorithm and results. Journal of Mechanisms, flights. In Nature & Biologically Inspired Computing,
Transmissions, and Automation in Design, 2009. NaBIC 2009. World Congress on, pages
111(1):130–136, 1989. 210–214. IEEE, 2009.
[6] Amir Hossein Gandomi, Xin-She Yang, and [11] Xin-She Yang and Suash Deb. Multiobjective cuckoo
Amir Hossein Alavi. Cuckoo search algorithm: a search for design optimization. Computers &
metaheuristic approach to solve structural Operations Research, 40(6):1616 – 1624, 2013.
optimization problems. Engineering with computers, Emergent Nature Inspired Algorithms for
29(1):17–35, 2013. Multi-Objective Optimization.
[7] Amir Hossein Gandomi, Xin-She Yang, Amir Hossein [12] Chun Zhang and Hsu-Pin Wang. Mixed-discrete
Alavi, and Siamak Talatahari. Bat algorithm for nonlinear optimization with simulated annealing.
constrained optimization tasks. Neural Computing and Engineering Optimization, 21(4):277–291, 1993.
StuCoSReC Proceedings of the 2016 3rd Student Computer Science Research Conference 60
Ljubljana, Slovenia, 12 October
Problem min max mean md std
Welded beam 1.724852309 1.724852309 1.724852309 1.724852309
Pressure vessel 0
Speed reducer 6059.714 6410.087 6095.32 6059.714 88.270
Spring design 2996.218 2996.218 2996.218 2996.218
0.01266523 0.0129087 0.01269661 0.01267089 0
5.402618 *10e−5
Table 2: Comparison with state-of-the-art.
Welded beam
Method min max mean std
Gandomi et al [7] 1.8786560 0.2677989
1.7312 2.3455793 1.741913
Akay et al.[1] 1.724853 0.031
Brajevic et al. [3] 1.724852 – 1.724852 0.0000017
Baykasoglu et al. [2] 1.724852309
1.724852 – 0
This study mean 0
1.724852 1.724852 6447.736
Method 6245.308144 std
Gandomi et al [6] 1.724852309 1.724852309 6192.116211 502.693
6064.33605261 205.00
Akay et al.[1] Pressure vessel 6095.32
Brajevic et al. [3] 204
Baykasoglu et al. [2] min max mean 11.28785324
3007.1997
This study 6059.714 6495.347 2997.058412 88.270
2994.471072
Method 6059.714736 – 2996.514874 std
Gandomi et al [6] 2996.218 4.9634
6059.714335 –
Akay et al.[1] mean –
Brajevic et al. [3] 6059.71427196 6090.52614259 0.01350052 0.00000598
Baykasoglu et al. [2]
6059.714 6410.087 0.012709 0.09
This study 0.012683 0
Speed reducer 0.0126770446
Method 0.01269661 std
Gandomi et al [7] min max 0.001420272
Akay et al.[1] 3000.9810 3009 0.012813
Brajevic et al. [3] 0.00000331
Baykasoglu et al. [2] 2997.058412 – 0.0127116883
5.402618*10e−5
This study 2994.471066 –
2996.372698 2996.669016
2996.218 2996.218
Spring design
min max
0.01266522 0.0168954
0.012665 –
0012665 –
0.0126653049 0.0000128058
0.01266523 0.0129087
[3] Ivona Brajevic and Milan Tuba. An upgraded artificial Applications, 22(6):1239–1255, 2013.
bee colony (abc) algorithm for constrained
optimization problems. Journal of Intelligent [8] Uroˇs Mlakar, Iztok Fister Jr., and Iztok Fister. Hybrid
Manufacturing, 24(4):729–740, 2013. self-adaptive cuckoo search for global optimization.
Swarm and Evolutionary Computation, 29:47 – 72,
[4] Radovan R Bulatovi´c, Goran Boˇskovi´c, Mile M 2016.
Savkovi´c, and Milomir M Gaˇsi´c. Improved cuckoo
search (ics) algorthm for constrained optimization [9] Shyue-Jian Wu and Pei-Tse Chow. Genetic algorithms
problems. Latin American Journal of Solids and for nonlinear mixed discrete-integer optimization
Structures, 11(8):1349–1362, 2014. problems via meta-genetic parameter optimization.
Engineering Optimization+ A35, 24(2):137–159, 1995.
[5] JZ Cha and RW Mayne. Optimization with discrete
variables via recursive quadratic programming: Part [10] Xin-She Yang and Suash Deb. Cuckoo search via l´evy
2—algorithm and results. Journal of Mechanisms, flights. In Nature & Biologically Inspired Computing,
Transmissions, and Automation in Design, 2009. NaBIC 2009. World Congress on, pages
111(1):130–136, 1989. 210–214. IEEE, 2009.
[6] Amir Hossein Gandomi, Xin-She Yang, and [11] Xin-She Yang and Suash Deb. Multiobjective cuckoo
Amir Hossein Alavi. Cuckoo search algorithm: a search for design optimization. Computers &
metaheuristic approach to solve structural Operations Research, 40(6):1616 – 1624, 2013.
optimization problems. Engineering with computers, Emergent Nature Inspired Algorithms for
29(1):17–35, 2013. Multi-Objective Optimization.
[7] Amir Hossein Gandomi, Xin-She Yang, Amir Hossein [12] Chun Zhang and Hsu-Pin Wang. Mixed-discrete
Alavi, and Siamak Talatahari. Bat algorithm for nonlinear optimization with simulated annealing.
constrained optimization tasks. Neural Computing and Engineering Optimization, 21(4):277–291, 1993.
StuCoSReC Proceedings of the 2016 3rd Student Computer Science Research Conference 60
Ljubljana, Slovenia, 12 October
