Abstract
The mechanical resistance of a locomotive is crucial for power consumption. It is desirable to maintain this resistance at a minimum value for energy efficiency under optimal operation conditions. The optimal conditions can be found by particle swarm optimization with constraints. The particle swarm optimization method is a highly preferred type of heuristic algorithm because of its advantages, such as fewer parameters, faster speed, and a simpler flow diagram. However, fast convergence can be misleading in finding the optimum solution in some cases. Pareto analysis is used in this proposed study to prevent missing the target. When the literature is searched, it is seen that there are various studies using this method. However, in all of these studies, the results of the particle swarm method have been interpreted as whether or not they complied with Pareto’s 80/20 rule. The validity of the Pareto analysis is taken as an assumption, and with the help of this assumption, the coefficients of a locomotive’s mathematical equation were changed, and finally the results were found by applying the particle herd optimization method. Finally, a novel hybrid method has been created by including the Pareto optimality condition to particle swarm optimization. The results are compared with this innovative hybrid method of Pareto and particle swarm and the results found using only the particle swarm method.
Highlights
Optimization is used in many fields of life
The mathematical model of the system is introduced, this modal is solved with different optimization methods
In previous studies, different from this study, the results of constrained Particle Swarm Optimization (PSO) have been evaluated as to whether or not constrained PSO is a good method for the study by looking at whether it complies with the 80/20 rule
Summary
Optimization is used in many fields of life. The mathematical model of the system is introduced, this modal is solved with different optimization methods. Optimization methods are classified as classical methods and heuristic optimization methods. Classical methods depend on the analytical and derivation of the problem. Ere are two types of classical methods. Ey are Gradient and nongradient based methods. When the problem size increases, classical methods may be insufficient. A study [1] proves that classical methods are not sufficient in finding the optimum working points of the locomotive
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