Discrete symbiotic organism search with excellence coefficients and self-escape for traveling salesman problem

Discrete farmland fertility optimization algorithm with metropolis acceptance criterion for traveling salesman problems

“…The compared optimized algorithms are selected because of their use of similar mechanisms in the proposed algorithm and also as the new and strong optimizing algorithms (AHSA‐TS, 68 ECSDSOS, 41 DSCA, 32 NDDE, 69 HDABC, 33 and SOS‐SA 43 ). This comparison is done based on standard deviation (STD), average (Ave.), and PDay (%) for better evaluation, Wilcoxon statistical test 70 with 5% significance is used to discover the remarkable differences based on the results of the suggested model in comparison to other optimization methods.…”

“…The results in Table 6 show that the proposed algorithm was able to achieve the known optimal average in 19 instances, and according to the Best criterion, it is found that the proposed algorithm achieves known optimal value in solving 21 instances. Table 8 states the performance of the suggested model and other optimizing algorithms, such as AHSA-TS, 68 ECSDSOS, 41 DSCA, 32 NDDE, 69 HDABC, 33 and SOS-SA. 43 The suggested model, mainly in large sizes, shows a better performance in comparison to other optimizing algorithms reflecting the strength of the suggested model in escaping local optimizations.…”

“…Table 8 states the performance of the suggested model and other optimizing algorithms, such as AHSA‐TS, 68 ECSDSOS, 41 DSCA, 32 NDDE, 69 HDABC, 33 and SOS‐SA 43 …”

“…The Roulette Wheel selects one of the operators during each performance based on the given probabilities. This method is used in research 41,42 for the selection of neighborhood operators.…”

“…Similarly, Wang et al 41 have improved the discrete algorithm of symbiotic organism search (DSOS) using excellence coefficients and self‐escape to solve the TSP (ECSDSOS). They have chosen shorter edges (paths) to produce better local paths from the self‐escape strategy and the excellence coefficients to avoid being trapped in the local optimum.…”