A Multipopulation Multiobjective Ant Colony System Considering Travel and Prevention Costs for Vehicle Routing in COVID-19-Like Epidemics

Abstract

As transportation system plays a vastly important role in combatting newly-emerging and severe epidemics like the coronavirus disease 2019 (COVID-19), the vehicle routing problem (VRP) in epidemics has become an emerging topic that has attracted increasing attention worldwide. However, most existing VRP models are not suitable for epidemic situations, because they do not consider the prevention cost caused by issues such as viral tests and quarantine during the traveling. Therefore, this paper proposes a multi-objective VRP model for epidemic situations, named VRP4E, which considers not only the traditional travel cost but also the prevention cost of the VRP in epidemic situations. To efficiently solve the VRP4E, this paper further proposes a novel algorithm named multiobjective ant colony system algorithm for epidemic situations, termed MOACS4E, together with three novel designs. First, by extending the efficient "multiple populations for multiple objectives" framework, the MOACS4E adopts two ant colonies to optimize the travel and prevention costs respectively, so as to improve the search efficiency. Second, a pheromone fusionbased solution generation method is proposed to fuse the pheromones from different colonies to increase solution diversity effectively. Third, a solution quality improvement method is further proposed to improve the solutions for the prevention cost objective. The effectiveness of the MOACS4E is verified in experiments on 25 generated benchmarks by comparison with six state-of-the-art and modern algorithms. Moreover, the VRP4E in different epidemic situations and a real-world case in Manuscript

A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization

“…Keeping this in mind, we will target our future work on the new inter-task similarity measurement mechanism. In addition, we will apply the BoKT framework to other evolutionary computation algorithms like particle swarm optimization [52]- [54] and ant colony optimization [55]- [57], and also extend the BoKTbased algorithms to help efficiently solve some challenging real-world optimization problems [58]- [60].…”

A Novel Evolutionary Algorithm With Column and Sub-Block Local Search for Sudoku Puzzles

“…Traditional Sudoku solution methods are ineffective in solving complex and high-dimension Sudoku puzzles because the Sudoku puzzles have a huge search space [21]. Evolutionary computation algorithms, such as GA [22]- [24], ant colony optimization (ACO) [25]- [27], particle swarm optimization (PSO) [28]- [32], differential evolution [34]- [36], and estimation of distribution algorithms [37] have shown promising performance in solving Sudoku puzzles and many other complex or real-world problems [38]- [41]. For example, > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < Mantere and Koljonen [15] adopted GA to solve the Sudoku puzzle, but this work could not effectively solve difficult Sudoku puzzles.…”

Graph-Based Deep Decomposition for Overlapping Large-Scale Optimization Problems

“…Since evolutionary computation algorithms (including evolutionary algorithms (EAs) [11], [12] and swarm intelligence algorithms [13], [14]) are skilled in maintaining the solution diversity and finding optimal solutions in global optimization [15], [16], [17], [18], [19], [20], [21], they have been widely studied and extensively applied to solve the LSOPs [22], [23], [24]. EAs used for the LSOPs can be classified into two main categories according to whether decompose the problems.…”