Unified particle swarm optimization for solving constrained engineering optimization problems
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3,357 citations
Cites methods from "Unified particle swarm optimization..."
...The aforementioned mechanical design problemswere attempted by (µ+λ)-Evolutionary Strategy (ES) [19], Unified Particle Swarm Optimization (UPSO) [20], Co-evolutionary Particle Swarm Optimization (CPSO) [21], Coevolutionary Differential Evolution (CoDE) [17], Hybrid PSODE [13] and Artificial Bee Colony (ABC) [22]....
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...Problem (μ + λ)-ES [19] UPSO [20] CPSO [21] CoDE [17] PSO-DE [13] ABC [22] TLBO...
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...Problem (µ + λ)-ES [19] UPSO [20] CPSO [21] CoDE [17] PSO-DE [13] ABC [22] TLBO Welded Best 1.724852 1.92199 1.728 1.73346 1.72485 1.724852 1.724852 Beam Mean 1.777692 2.83721 1.74883 1.76815 1.72485 1.741913 1.72844676 Evaluations 30000 100000 200000 240000 33000 30000 10000 Pressure Best 6059.7016 6544.27 6061.077 6059.734 6059.714 6059.714 6059.714335 Vessel Mean 6379.938 9032.55 6147.1332 6085.23 6059.714 6245.308 6059.71434 Evaluations 30000 100000 200000 240000 42100 30000 10000 Tension Best 0.012689 0.01312 0.012674 0.01267 0.012665 0.012665 0.012665 Compression Mean 0.013165 0.02294 0.01273 0.012703 0.012665 0.012709 0.01266576 Spring Evaluations 30000 100000 200000 240000 24950 30000 10000 Gear Best 2996.348 NA NA NA 2996.348 2997.058 2996.34817 train Mean 2996.348 NA NA NA 2996.348 2997.058 2996.34817 Evaluations 30000 NA NA NA 54350 30000 10000 The data in bold indicate the best solution....
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...The aforementioned mechanical design problemswere attempted by (μ+λ)-Evolutionary Strategy (ES) [19], Unified Particle Swarm Optimization (UPSO) [20], Co-evolutionary Particle Swarm Optimization (CPSO) [21], Coevolutionary Differential Evolution (CoDE) [17], Hybrid PSODE [13] and Artificial Bee Colony (ABC) [22]....
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1,701 citations
1,501 citations
Cites background or methods or result from "Unified particle swarm optimization..."
...Algorithm Worst Mean Best Std. GA3 0.0128220 0.0127690 0.0127048 3.94e 5 GA4 0.0129730 0.0127420 0.0126810 5.90e 5 CPSO 0.0129240 0.0127300 0.0126747 5.20e 4 HPSO 0.0127190 0.0127072 0.0126652 1.58e 5 G-QPSO 0.017759 0.013524 0.012665 0.001268 QPSO 0.018127 0.013854 0.012669 0.001341 PSO 0.071802 0.019555 0.012857 0.011662 DSS-MDE 0.012738262 0.012669366 0.012665233 1.25e 5 PSO-DE 0.012665304 0.012665244 0.012665233 1.2e 8 SC 0.016717272 0.012922669 0.012669249 5.9e 4 UPSO N.A. 0.02294 0.01312 7.2e 3 (l + k)-ES N.A. 0.013165 0.012689 3.9e 4 ABC N.A. 0.012709 0.012665 0.012813 TLBO N.A. 0.01266576 0.012665 N.A. MBA 0.012900 0.012713 0.012665 6.3e 5 CSA 0.0126701816 0.0126659984 0.0126652328 1.357079e 6 Fig....
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...Table 11 compares the statistical results obtained by CSA and those found by UPSO [30], ABC [32] and MBA [23]....
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...In terms of the mean index, CSA outperforms UPSO and MBA and is outperformed by ABC....
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...In terms of the best index, CSA outperforms GA3 [24], GA4 [25], CPSO [26], QPSO [28], PSO [28], SC [20], UPSO [30] and (l + k)-ES [29]....
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...Algorithm Worst Mean Best Std. GA3 6308.4970 6293.8432 6288.7445 7.4133 GA4 6469.3220 6177.2533 6059.9463 130.9297 CPSO 6363.8041 6147.1332 6061.0777 86.45 HPSO 6288.6770 6099.9323 6059.7143 86.20 G-QPSO 7544.4925 6440.3786 6059.7208 448.4711 QPSO 8017.2816 6440.3786 6059.7209 479.2671 PSO 14076.3240 8756.6803 6693.7212 1492.5670 CDE 6371.0455 6085.2303 6059.7340 43.0130 UPSO 9387.77 8016.37 6154.70 745.869 PSO-DE N.A. 6059.714 6059.714 N.A. ABC N.A. 6245.308144 6059.714736 205 (l + k)-ES N.A. 6379.938037 6059.701610 210 TLBO N.A. 6059.71434 6059.714335 N.A. CSA 7332.84162110 6342.49910551 6059.71436343 384.94541634 by HPSO [27], G-QPSO [28], DSS-MDE [22], PSO-DE [21], ABC [32], TLBO [33] and MBA [23]....
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Cites background from "Unified particle swarm optimization..."
...2013a), PSO (Parsopoulos and Vrahatis 2005), Genetic Adaptive Search (GeneAS) (Deb and Goyal 1996) and Simulated annealing (Zhang and Wang 1993)....
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...10 Schematic representation of gear CS (Gandomi et al. 2013a), PSO (Parsopoulos and Vrahatis 2005), Genetic Adaptive Search (GeneAS) (Deb and Goyal 1996) and Simulated annealing (Zhang and Wang 1993)....
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References
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