Gathering Strength, Gathering Storms: Knowledge Transfer via Selection for VRPTW

Abstract

Recently, due to the growth in machine learning and data mining, for scheduling applications in China’s industrial intelligence, we are quite fortunate to witness a paradigm of evolutionary scheduling via learning, which includes a new tool of evolutionary transfer optimization (ETO). As a new subset in ETO, single-objective to multi-objective/many-objective optimization (SMO) acts as a powerful, abstract and general framework with wide industrial applications like shop scheduling and vehicle routing. In this paper, we focus on the general mechanism of selection that selects or gathers elite and high potential solutions towards gathering/transferring strength from single-objective problems, or gathering/transferring storms of knowledge from solved tasks. Extensive studies in vehicle routing problems with time windows (VRPTW) on well-studied benchmarks validate the great universality of the SMO framework. Our investigations (1) contribute to a deep understanding of SMO, (2) enrich the classical and fundamental theory of building blocks for genetic algorithms and memetic algorithms, and (3) provide a completive and potential solution for VRPTW.

Evolutionary Process for Engineering Optimization in Manufacturing Applications: Fine Brushworks of Single-Objective to Multi-Objective/Many-Objective Optimization

“…Quite obviously and intuitively, the measure of the "hamming distance (dissimilarity) and positional BB (similarity) work from opposite sides to the same characterization" [6,49]. Here is the detail of C. In [54], "science clustering" (because it was published at journal of "science", so we call it "science clustering") is based on the deep observation that the centers of clusters in a sample space are characterized by both "a relatively higher density than points in their neighborhoods" [6,49] and "a relatively long distance from points that have higher densities" [6,49]. For the PFSP here, we implement science clustering via a hamming distance metric, which is widely accepted and used in evolutionary computation research.…”

“…To our surprise, the hamming distance also focuses on the work of mining the positional BBs. Quite obviously and intuitively, the measure of the "hamming distance (dissimilarity) and positional BB (similarity) work from opposite sides to the same characterization" [6,49].…”

“…L is an ordinary insertion operator. Here is the detail of C. In [54], "science clustering" (because it was published at journal of "science", so we call it "science clustering") is based on the deep observation that the centers of clusters in a sample space are characterized by both "a relatively higher density than points in their neighborhoods" [6,49] and "a relatively long distance from points that have higher densities" [6,49]. For the PFSP here, we implement science clustering via a hamming distance metric, which is widely accepted and used in evolutionary computation research.…”

“…We further discuss the gather [6] or transfer coefficient between the auxiliary tasks for boosting the core task. (The third surgical knife of the auxiliary task).…”

Distribution Management Problem: Heuristic Solution for Vehicle Routing Problem with Time Windows (VRPTW) in the Moroccan Petroleum Sector

“…It is therefore inevitable to proceed to its resolution by heuristic approaches, which provide feasible and appreciable solutions in a reasonable time [11]. Recently, researchers treat VRPTW by different ways [12][13][14][15][16][17][18].…”