Showing 2 results for Local Search
H. Ghohani Arab, M. R. Ghasemi, M. Miri,
Volume 3, Issue 4 (10-2013)
Abstract
Weighted Uniform Simulation (WUS) is recently presented as one of the efficient simulation methods to obtain structural failure probability and most probable point (MPP). This method requires initial assumptions of failure probability to obtain results. Besides, it has the problem of variation in results when it conducted with few samples. In the present study three strategies have been presented that efficiently enhanced capabilities of WUS. To this aim, a progressively expanding intervals strategy proposed to eliminate the requirement to initial assumptions in WUS, while low-discrepancy samples simultaneously employed to reduce variations in failure probabilities. Moreover, to improve the accuracy of MPP, a new simple local search method proposed and combined with the simulation that strengthened the method to obtain more accurate MPP. The capabilities of proposed strategies investigated by solving several structural reliability problems and obtained results compared with traditional WUS and common reliability methods. Results show that proposed strategies efficiently improved the capabilities of conventional WUS.
M. Yousefikhoshbakht,
Volume 11, Issue 1 (1-2021)
Abstract
The capacity vehicle routing problem (CVRP) is one of the most famous issues in combinatorial optimization that has been considered so far, and has attracted the attention of many scientists and researchers today. Therefore, many exact, heuristic and meta-heuristic methods have been presented in recent decades to solve it. In this paper, due to the weaknesses in the particle swarm optimization (PSO), a hybrid-modified version of this algorithm called PPSO is presented to solve the CVRP problem. In order to evaluate the efficiency of the algorithm, 14 standard examples from 50 to 199 customers of the existing literature were considered and the results were compared with other meta-heuristic algorithms. The results show that the proposed algorithm is competitive with other meta-heuristic algorithms. Besides, this algorithm obtained very close answers to the best known solutions (BKSs) for most of the examples, so that the seven BKSs were produced by PPSO.
