The application of soft computing techniques can be largely found in engineering sciences. These include the design and optimization of navigation systems for use in land, sea, and air transportation systems. In this paper, an attempt is made to leverage on novel metaheuristic optimization approaches for designing integrated navigation systems. For this purpose, a simplified version of the inclined planes system optimization (called SIPO) algorithm alongside its two standard and modified versions are used in comparison with the two conventional methods of genetic algorithm and particle swarm optimization. Considerations are made on an INS/GNSS problem with IMU MEMS modules. Outputs are presented in terms of statistical and performance indicators, such as runtime, fitness, convergence, navigation accuracy (velocity, latitude, longitude, altitude, roll, pitch, yaw), and routing along with the ranking of algorithms. Competitive performance and relative superiority of the standard IPO over other methods in evaluating results have been confirmed. So that compared to other state-of-the-art algorithms (GA, PSO, IPO, and MIPO), the best runtime rank with a value of 6/4 by SIPO and the best performance rank of fitness, navigation accuracy for the two assumed IMU modules, and the total rank with values of 4/4, 149/60, 165/60, and 332/128 obtained by IPO, respectively.
- IMU MEMS
- Inclined planes system optimization
- INS/GNSS integrated navigation
- Literature review
- Optimal design
- Soft computing