Opposition-based moth swarm algorithm; Expert Systems with Applications; Vol. 184
| Parent link: | Expert Systems with Applications Vol. 184.— 2021.— [115481, 15 p.] |
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| Erakunde egilea: | |
| Beste egile batzuk: | , , , , , , , |
| Gaia: | Title screen Nowadays, resource-optimizing techniques are required in many engineering areas to obtain the most appropriate solutions for complex problems. For this reason, there is a trend among researchers to improve existing swarm-based algorithms through different evolutionary techniques and to create new population-based methods that can accurately explore the feature space. The recently proposed Moth swarm algorithm (MSA) inspired by the orientation of moths towards moonlight is an associative learning mechanism with immediate memory that uses Lйvy mutation to cross-population diversity and spiral movement. The MSA is a population-based method used for tackling complex optimization problems. It presents an adequate capacity for exploration and exploitation trends; however, due to its nature of operators, this type of method is prone to get stuck in sub-optimal locations, which affects the speed of convergence and the computational effort to reach better solutions. To mitigate these shortcomings, this paper proposes an improved MSA that combines opposition-based learning (OBL) as a mechanism to enhance the exploration drifts of the basic version and increase the speed of convergence to obtain more accurate solutions. The proposed approach is called OBMSA. It has been tested for solving three classic engineering design problems (welded beam, tension/compression spring, and pressure vessel designs) with constraints, 19 benchmark functions comprising 7 unimodal, 6 multimodal, and 6 composite functions. Experimental results and comparisons provide evidence that the performance and accuracy of the proposed method are superior to the original MSA. We hope the community utilizes the proposed MSA-based approach for solving other complex problems. |
| Hizkuntza: | ingelesa |
| Argitaratua: |
2021
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| Gaiak: | |
| Sarrera elektronikoa: | https://doi.org/10.1016/j.eswa.2021.115481 |
| Formatua: | Baliabide elektronikoa Liburu kapitulua |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=666004 |
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| 200 | 1 | |a Opposition-based moth swarm algorithm |f D. A. Oliva Navarro, S. Esquivel-Torres, S. Hinojosa [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 78 tit.] | ||
| 330 | |a Nowadays, resource-optimizing techniques are required in many engineering areas to obtain the most appropriate solutions for complex problems. For this reason, there is a trend among researchers to improve existing swarm-based algorithms through different evolutionary techniques and to create new population-based methods that can accurately explore the feature space. The recently proposed Moth swarm algorithm (MSA) inspired by the orientation of moths towards moonlight is an associative learning mechanism with immediate memory that uses Lйvy mutation to cross-population diversity and spiral movement. The MSA is a population-based method used for tackling complex optimization problems. It presents an adequate capacity for exploration and exploitation trends; however, due to its nature of operators, this type of method is prone to get stuck in sub-optimal locations, which affects the speed of convergence and the computational effort to reach better solutions. To mitigate these shortcomings, this paper proposes an improved MSA that combines opposition-based learning (OBL) as a mechanism to enhance the exploration drifts of the basic version and increase the speed of convergence to obtain more accurate solutions. The proposed approach is called OBMSA. It has been tested for solving three classic engineering design problems (welded beam, tension/compression spring, and pressure vessel designs) with constraints, 19 benchmark functions comprising 7 unimodal, 6 multimodal, and 6 composite functions. Experimental results and comparisons provide evidence that the performance and accuracy of the proposed method are superior to the original MSA. We hope the community utilizes the proposed MSA-based approach for solving other complex problems. | ||
| 461 | |t Expert Systems with Applications | ||
| 463 | |t Vol. 184 |v [115481, 15 p.] |d 2021 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a moth swarm algorithm | |
| 610 | 1 | |a opposition-based learning | |
| 610 | 1 | |a optimization techniques | |
| 610 | 1 | |a metaheuristics | |
| 610 | 1 | |a алгоритмы | |
| 610 | 1 | |a оптимизация | |
| 610 | 1 | |a метаэвристика | |
| 701 | 1 | |a Oliva Navarro |b D. A. |c specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University |f 1983- |g Diego Alberto |3 (RuTPU)RU\TPU\pers\37366 | |
| 701 | 1 | |a Esquivel-Torres |b S. |g Sara | |
| 701 | 1 | |a Hinojosa |b S. |g Salvador | |
| 701 | 1 | |a Perez-Cisneros |b M. |g Marco | |
| 701 | 1 | |a Osuna-Enciso |b V. |g Valentin | |
| 701 | 1 | |a Ortega-Sanchez |b N. |g Noe | |
| 701 | 1 | |a Dhima |b G. |g Gaurav | |
| 701 | 1 | |a Heidari |b A. A. |g Ali Asghar | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |c 2017- |x TPU |7 ca |8 rus |9 28330 |
| 801 | 2 | |a RU |b 63413507 |c 20211129 |g RCR | |
| 856 | 4 | |u https://doi.org/10.1016/j.eswa.2021.115481 | |
| 942 | |c CF | ||