An improved Intelligent Water Drops algorithm for achieving optimal job-shop scheduling solutions

An improved optimization method based on the intelligent water drops algorithm for the vehicle routing problem

“…Since then, IWD algorithm has been applied to many combinatorial optimization problems such as TSP ( [38]), multiple knapsack [39], vehicle routing [40], unrelated parallel machine scheduling [41] and job shop scheduling [42]; and the result was impressive.…”

“…The Intelligent water drop (IWD) algorithm is inspired from rivers behavior and interaction of water drops and soils in the river beds. This algorithm has been applied to many combinatorial problems such TSP and multiple knapsack problem [39], vehicle routing [40], and scheduling problems (see for example [41][42]), where it has been shown that it is competitive to other meta-heuristic algorithms.…”

An ensemble of intelligent water drop algorithms and its application to optimization problems

“…Only a small number of studies pertaining to the theoretical aspects of the IWD algorithm to improve its performance are available in the literature. As an example, an Enhanced IWD (EIWD) algorithm to solve jobshop scheduling problems was proposed by Niu et al [42]. The following schemes have been introduced to increase diversity of the search space and enhance the original IWD performance: (i) varying the initial soil and velocity values, (ii) employing the conditional probability in the selection probability, (iii) bounding the soil level, (iv) using the elite mechanism to update the soil and (v) combining the IWD algorithm with a local search method.…”

Joint Subcarrier Pairing and Power Allocation in OFDMA Cooperative Relay Networks

“…Its ideas are based on the water drops that flow in nature such that each water drop constructs a solution by traversing the search space of the problem and modifying its environment. The IWD algorithm has been used for solving several NPhard combinatorial optimization problems, such as the Traveling Salesman Problem (TSP) [13], [14], robot path planning problem [15], multidimensional knapsack problem (MKP) [16], job-shop scheduling problem [17], and real-life waste collection problem [18]. A main advantage of this method is that it is able to solve the nonconvex optimization and has low computational complexity and fast convergence.…”