Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles

Abstract

Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines, and the rationality of their routes plays the direct impact on operation safety and energy consumption. Rich studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil, however, less works are for electric trackless rubber-tyred vehicles. Furthermore, energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving, especially the limited cruising ability of electric trackless rubber-tyred vehichles (TRVs). To address this issue, an energy consumption model of an electric trackless rubber-tyred vehicle is formulated, in which the effects from total mass, speed profiles, slope of roadways, and energy management mode are all considered. Following that, a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance, allowable load, and endurance power. As a problem-solver, an improved artificial bee colony algorithm is put forward. More especially, an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific space. In order to assign onlookers to some promising food sources reasonably, their selection probability is adaptively adjusted. For a stagnant food source, a knowledge-driven initialization is developed to generate a feasible substitute. The experimental results on four real-world instances indicate that improved artificial bee colony algorithm (IABC) outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

“…This oversight might result in the population towards local optima within the decision space [43]. The interaction between decision variables is characterized by how variables x i and x j jointly influence the behavior of the objective value, such as its monotonicity or convexity [47,48]. In summary, x i alone cannot determine the directional change of the objective function f (x), the value of x j must also be considered.…”