Double evolutsional artificial bee colony algorithm for multiple traveling salesman problem

Abstract

Abstract:The double evolutional artificial bee colony algorithm (DEABC) is proposed for solving the single depot multiple traveling salesman problem (MTSP). The proposed DEABC algorithm, which takes advantage of the strength of the upgraded operators, is characterized by its guidance in exploitation search and diversity in exploration search. The double evolutional process for exploitation search is composed of two phases of half stochastic optimal search, and the diversity generating operator for exploration search is used for solutions which cannot be improved after limited times. The computational results demonstrated the superiority of our algorithm over previous state-ofthe-art methods.

A Novel Multi-Robot Task Assignment Scheme Based on a Multi-Angle K-Means Clustering Algorithm and a Two-Stage Load-Balancing Strategy

“…Suppose there are m robots corresponding to the set R = {R i , 1 ≤ i ≤ m}, R i denotes the i-th robot in R. T = T j , 1 ≤ j ≤ n is the set of task points, T j denoting the j-th task point of T. Therefore, the task assignment problem of m robots starting from a fixed starting point to complete n task points and then returning to the original starting point can be converted to a multi-travel merchant model for solving [30][31][32].…”

“…The total cost of the current system is calculated as 522,846.70. B , as shown in Equation (30). The difference rate between k B and Bk B is denoted as e2 B , as shown in Equation (31).…”

“…B Bk i is the cost of the i-th set obtained using the BKCA algorithm. The difference rate between B k and B Mk is denoted as B e1 , as shown in Equation (30). The difference rate between B k and B Bk is denoted as B e2 , as shown in Equation (31).…”

PERMS: An efficient rescue route planning system in disasters

“…Their experiments show that the algorithm is more efficient than SA, while its time cost is larger than SA, and does not take disaster environmental factors into account. Xue et al [27] proposed a double evolutional artificial bee colony algorithm (DEABC) where a half-stochastic optimal search strategy has been used for improving traditional exploitation-based search operators, with positive effects in search efficiency and fitness guidance. Two different half-stochastic optimal search operators have been used in exploitation-based search to make up the double evolutional process, and exploration has been significantly improved for different optimisation goals.…”

“…For example, Baranwal et al [26] discuss a heuristic algorithm based on maximum entropy principle (MEP) and the deterministic annealing algorithm. Xue et al [27] propose a double evolutional artificial bee colony algorithm and in [28], a genetic ant colony optimisation algorithm is introduced.…”