Energy loss optimization of run-off-road wheels applying imperialist competitive algorithm

• Energy loss of off-road vehicles is optimized by meta-heuristics.
• ICA, GA, PSO and hybridized GA–PSO techniques were applied as optimization tools.
• ICA outperformed the other approaches in convergence to optimal minima.

The novel imperialist competitive algorithm (ICA) has presented outstanding fitness on various optimization problems. Application of meta-heuristics has been a dynamic studying interest of the reliability optimization to determine idleness and reliability constituents. The application of a meta-heuristic evolutionary optimization method, imperialist competitive algorithm (ICA), for minimization of energy loss due to wheel rolling resistance in a soil bin facility equipped with single-wheel tester is discussed. The required data were collected thorough various designed experiments in the controlled soil bin environment. Local and global searching of the search space proposed that the energy loss could be reduced to the minimum amount of 15.46 J at the optimized input variable configuration of wheel load at 1.2 kN, tire inflation pressure of 296 kPa and velocity of 2 m/s. Meanwhile, genetic algorithm (GA), particle swarm optimization (PSO) and hybridized GA–PSO approaches were benchmarked among the broad spectrum of meta-heuristics to find the outperforming approach. It was deduced that, on account of the obtained results, ICA can achieve optimum configuration with superior accuracy in less required computational time.