2013
DOI: 10.1016/j.asoc.2012.11.026
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Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems

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Cited by 798 publications
(288 citation statements)
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“…The detailed description of the adopted engineering problem is presented in Appendix 2. Table 7 presents the optimal solution for three-bar truss design problem obtained by particle swarm optimization with differential evolution (PSODE) [20], dynamic stochastic selection (DEDS) [42], mine blast algorithm (MBA) [29], water cycle algorithm (WCA) [6], MFO and SA-MFO. As it can be seen from this table, SA-MFO obtained better results compared with the standard MFO.…”
Section: Comparison Using Engineering Design Problems Experimentsmentioning
confidence: 99%
“…The detailed description of the adopted engineering problem is presented in Appendix 2. Table 7 presents the optimal solution for three-bar truss design problem obtained by particle swarm optimization with differential evolution (PSODE) [20], dynamic stochastic selection (DEDS) [42], mine blast algorithm (MBA) [29], water cycle algorithm (WCA) [6], MFO and SA-MFO. As it can be seen from this table, SA-MFO obtained better results compared with the standard MFO.…”
Section: Comparison Using Engineering Design Problems Experimentsmentioning
confidence: 99%
“…The design optimization problem involves three continuous variables and four nonlinear inequality constraints. This problem has already been solved by many researchers, including He and Wang [14],who proposed hybrid particle swarm optimization (HPSO), Zhang et al [13],who used differential evolution with dynamic stochastic selection algorithm (DEDS) , Wang and Li [12], who proposed differential evolution with level comparison algorithm(DELC), Sadollah et al [15], who employed a mine blast algorithm(MBA), Wen et al [5], who used effective hybrid cuckoo search(HCS -LSAL), and Yang [2], who applied a cuckoo search algorithm(CS).The best solutions obtained by the above mentioned approaches are shown in Table 2.Their statistical results are shown in table 3.…”
Section: Tension-compression Spring Designmentioning
confidence: 99%
“…Simulated Annealing [12] SA Gravitational Search Algorithm [13] GSA Charged System Search [14] CSS Black Hole Algorithm [15] BH Emperor Penguin Optimizer [16] EPO Artificial Chemical Reaction Optimization Algorithm [17] ACROA Ray Optimization Algorithm [18] RO Galaxy-Based Search Algorithm [19] GbSA Ant Colony Optimization [20] ACO Cuckoo Search [21] CS Bat-Inspired Algorithm [22] BA Firefly Algorithm [23] FA Spotted Hyena Optimizer [24] SHO Exchange Market Algorithm [25] EMA Social-Based Algorithm [26] SBA Harmony Search [27] HS Grey Wolf Optimizer [28] GWO Mine Blast Algorithm [29] MBA Every optimization algorithm needs to address the exploration and exploitation of a search space [30] and maintains a good balance between exploration and exploitation. The exploration phase investigates the different promising regions in a search space, whereas exploitation searches the close global optimal solutions around the promising regions [31,32].…”
Section: Algorithms Abbreviationmentioning
confidence: 99%