Soft Computing

This article presented a parallel cooperative hybrid algorithm for solving traveling salesman problem. Although heuristic approaches and hybrid methods obtain good results in solving the TSP, they cannot successfully avoid getting stuck to local optima. Furthermore, their processing duration unluckily takes a long time. To overcome these deficiencies, we propose the parallel cooperative hybrid algorithm (PACO-3Opt) based on ant colony optimization. This method uses the 3-Opt algorithm to avoid local minima. PACO-3Opt has multiple colonies and a master–slave paradigm. Each colony runs ACO to generate the solutions. After a predefined number of iterations, each colony primarily runs 3-Opt to improve the solutions and then shares the best tour with other colonies. This process continues until the termination criterion meets. Thus, it can reach the global optimum. PACO-3Opt was compared with previous algorithms in the literature. The experimental results show that PACO-3Opt is more efficient and reliable than the other algorithms.

Parameter optimization is an important step for support vector regression (SVR), since its prediction performance greatly depends on values of the related parameters. To solve the shortcomings of traditional grid search algorithms such as too many invalid search ranges and sensitivity to search step, an improved grid search algorithm is proposed to optimize SVR for prediction. The improved grid search (IGS) algorithm is used to optimize the penalty parameter and kernel function parameter of SVR by automatically changing the search range and step for several times, and then SVR is trained for the optimal solution. The available of the method is proved by predicting the values of soil and plant analyzer development (SPAD) in rice leaves. To predict SPAD values more quickly and accurately, some dimension reduction methods such as stepwise multiple linear regressions (SMLR) and principal component analysis (PCA) are processed the training data, and the results show that the nonlinear fitting and prediction performance of accuracy of SMLR-IGS-SVR and PCA-IGS-SVR are better than those of IGS-SVR.