| 000 | 03941nlm1a2200481 4500 | ||
|---|---|---|---|
| 001 | 667794 | ||
| 005 | 20231030042126.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\39005 | ||
| 035 | _aRU\TPU\network\36151 | ||
| 090 | _a667794 | ||
| 100 | _a20220421a2021 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aCH | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aMTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm _fM. H. Nadimi-Shahraki , Sh. Taghian, S. Mirjalili [et al.] |
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| 203 |
_aText _celectronic |
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| 300 | _aTitle screen | ||
| 320 | _a[References: 94 tit.] | ||
| 330 | _aThe moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search ability, maintain the balance between exploration and exploitation, and prevent the original MFO's premature convergence during the optimization process. Furthermore, the MTV-MFO algorithm uses the knowledge of inferior moths preserved in two archives to prevent premature convergence and avoid local optima. The performance of the MTV-MFO algorithm was evaluated using 29 benchmark problems taken from the CEC 2018 competition on real parameter optimization. The gained results were compared with eight metaheuristic algorithms. The comparison of results shows that the MTV-MFO algorithm is able to provide competitive and superior results to the compared algorithms in terms of accuracy and convergence rate. Moreover, a statistical analysis of the MTV-MFO algorithm and other compared algorithms was conducted, and the effectiveness of our proposed algorithm was also demonstrated experimentall. | ||
| 461 | _tSymmetry | ||
| 463 |
_tVol. 13, iss. 12 _v[2388, 30 p.] _d2021 |
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| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _aoptimization | |
| 610 | 1 | _ametaheuristic algorithms | |
| 610 | 1 | _amoth-flame optimization | |
| 610 | 1 | _aglobal numerical optimization | |
| 610 | 1 | _aоптимизация | |
| 610 | 1 | _aметаэвристические алгоритмы | |
| 610 | 1 | _aвекторные алгоритмы | |
| 610 | 1 | _aперемещения | |
| 701 | 1 |
_aNadimi-Shahraki _bM. H. _gMohammad |
|
| 701 | 1 |
_aTaghian _bSh. _gShokooh |
|
| 701 | 1 |
_aMirjalili _bS. _gSeyedali |
|
| 701 | 1 |
_aEwees _bA. A. _gAhmed |
|
| 701 | 1 |
_aAbualigah _bL. _gLaith |
|
| 701 | 1 |
_aMokhamed Elsaed (Mohamed Abd Elaziz) _bA. M. _cSpecialist in the field of informatics and computer technology _cProfessor of Tomsk Polytechnic University _f1987- _gAkhmed Mokhamed _2stltpush _3(RuTPU)RU\TPU\pers\46943 |
|
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИнженерная школа информационных технологий и робототехники _bОтделение информационных технологий _h7951 _2stltpush _3(RuTPU)RU\TPU\col\23515 |
| 801 | 2 |
_aRU _b63413507 _c20220421 _gRCR |
|
| 856 | 4 | _uhttps://doi.org/10.3390/sym13122388 | |
| 942 | _cCF | ||