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References | Techniques | Performance metrics | Pros | Cons |
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Liu et al. [34] | PSO-DE | Objective function values, median, mean, standard deviation | Converges quickly, it solves constrained problems | Higher cost in some problems |
Iwata and Fukuyama [35] | DEEPSO | Least square error | Accuracy | Underperforming in unsTable output conditions |
Yoshida and Fukuyama [36] | DEEPSO | Mean and standard deviation of objective functions | Faster convergence speed and better accuracy | Parallel distributed processing is not considered |
Buba and Lee [37] | Hybrid DE and PSO | Passengers’ and operators’ cost | Diversity of solution, better accuracy | It is not tested on larger and more realistic problem instances with heterogeneous buses |
Wang et al. [38] | DEPSO | Mean and standard deviations of the objective functions | Population diversity, higher scalability | Exploration and exploitation need improvement, implementation complexity |
Dabhi and Pandya [39] | EVDEPSO | Iterations and mean execution time and mean, max, min, and standard deviation of the objective function, average ranking index | It is superior in terms of the ranking index and average ranking index as compared to the other algorithms | Tests are confined to a limited category of problems |
Mirsadeghi and Khodayifar [40] | PSODE | Mean, the best, the worst, and standard deviation of the objective functions, run-time, success rate | High accuracy of solution | Improvement for the exploration and exploitation capabilities, implementation complexity |
Marcelino et al. [41] | C-DEEPSO | Mean, median, and standard deviation of objective functions | Better accuracy, high scalability | Improvement for the exploration and exploitation capabilities |
Tomar and Pant, 2011 [42] | MPDE | Mean, standard deviation of objective function | Better computation time | Getting trapped in local optima |
Yu et al. 2014 [43] | HPSO-DE | Mean, standard deviation and T-value of objective function | Maintains diversity of the population | Slow convergence and high computation time |
Lin et al. 2018 [44] | HPSODE | Mean, standard deviation of objective function | Good solution accuracy | Converging at local optima, |
Too et al. 2019 [45] | BPSODE | Mean, standard deviation of the objective function | Good solution accuracy | Extra computational cost, premature convergence |
Parouha and Das 2016 [46] | DE-PSO | Mean, standard deviation of objective function | Better solution accuracy | Rapid loss of diversity, converging prematurely. |
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