A Novel Generalised Meta-Heuristic Framework for Dynamic Capacitated Arc Routing Problems

The capacitated arc routing problem (CARP) is a challenging combinatorial optimisation problem abstracted from typical real-world applications, like waste collection and mail delivery. However, few studies considered dynamic changes during the vehicles' service, which can make the original schedule infeasible or obsolete. The few existing studies are limited by dynamic scenarios that can suffer single types of dynamic events, and by algorithms that rely on special operators or representations, being unable to benefit from the wealth of contributions provided by the static CARP literature. Here, we provide the first mathematical formulation for dynamic CARP (DCARP) and design a simulation system to execute the CARP solutions and generate DCARP instances with several common dynamic events. We then propose a novel framework able to generalise all existing static CARP optimisation algorithms so that they can cope with DCARP instances. The framework has the option to enhance optimisation performance for DCARP instances based on a restart strategy that makes no use of past history, and a sequence transfer strategy that benefits from past optimisation experience. Empirical studies are conducted on a wide range of DCARP instances. The results highlight the need for tackling dynamic changes and show that the proposed framework significantly improves over existing algorithms.

Memetic Search for Vehicle Routing with Simultaneous Pickup-Delivery and Time Windows

The vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW) has attracted much attention in the last decade, due to its wide application in modern logistics involving bi-directional flow of goods. In this paper, we propose a memetic algorithm with efficient local search and extended neighborhood, dubbed MATE, for solving this problem. The novelty of MATE lies in three aspects: 1) an initialization procedure which integrates an existing heuristic into the population-based search framework, in an intelligent way; 2) a new crossover involving route inheritance and regret-based node reinsertion; 3) a highly-effective local search procedure which could flexibly search in a large neighborhood by switching between move operators with different step sizes, while keeping low computational complexity. Experimental results on public benchmark show that MATE consistently outperforms all the state-of-the-art algorithms, and notably, finds new best-known solutions on 44 instances (65 instances in total). A new benchmark of large-scale instances, derived from a real-world application of the JD logistics, is also introduced, which could serve as a new and more practical test set for future research.

Robust Reinforcement Learning for General Video Game Playing

Reinforcement learning has successfully learned to play challenging board and video games. However, its generalization ability remains under-explored. The General Video Game AI Learning Competition aims at designing agents that are capable of learning to play different games levels that were unseen during training. This paper presents the games, entries and results of the 2020 General Video Game AI Learning Competition, held at the Sixteenth International Conference on Parallel Problem Solving from Nature and the 2020 IEEE Conference on Games. Three new games with sparse, periodic and dense rewards, respectively, were designed for this competition and the test levels were generated by adding minor perturbations to training levels or combining training levels. In this paper, we also design a reinforcement learning agent, called Arcane, for general video game playing. We assume that it is more likely to observe similar local information in different levels rather than global information. Therefore, instead of directly inputting a single, raw pixel-based screenshot of current game screen, Arcane takes the encoded, transformed global and local observations of the game screen as two simultaneous inputs, aiming at learning local information for playing new levels. Two versions of Arcane, using a stochastic or deterministic policy for decision-making during test, both show robust performance on the game set of the 2020 General Video Game AI Learning Competition.

Few-shots Parameter Tuning via Co-evolution

Generalization, i.e., the ability of addressing problem instances that are not available during the system design and development phase, is a critical goal for intelligent systems. A typical way to achieve good generalization is to exploit vast data to train a model. In the context of heuristic search, such a paradigm is termed parameter tuning or algorithm configuration, i.e., configuring the parameters of a search method based on a set of "training" problem instances. However, compared to its counterpart in machine learning, parameter tuning could more often suffer from the lack of training instances, and the obtained configuration may fail to generalize. This paper suggests competitive co-evolution as a remedy to this challenge and proposes a framework named Co-Evolution of Parameterized Search (CEPS). By alternately evolving a configuration population and an instance population, CEPS is capable of obtaining generalizable configurations with few training instances. The advantage of CEPS in improving generalization is analytically shown. Two concrete instantiations, namely CEPS-TSP and CEPS-VRPSPDTW, are also presented for the Traveling Salesman Problem (TSP) and the Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows (VRPSPDTW), respectively. Computational results on the two problems confirm the advantages of CEPS over state-of-the-art parameter tuning methods.

A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem

The reliable facility location problem (RFLP) is an important research topic of operational research and plays a vital role in the decision-making and management of modern supply chain and logistics. Through solving RFLP, the decision-maker can obtain reliable location decisions under the risk of facilities' disruptions or failures. In this paper, we propose a novel model for the RFLP. Instead of assuming allocating a fixed number of facilities to each customer as in the existing works, we set the number of allocated facilities as an independent variable in our proposed model, which makes our model closer to the scenarios in real life but more difficult to be solved by traditional methods. To handle it, we propose EAMLS, a hybrid evolutionary algorithm, which combines a memorable local search (MLS) method and an evolutionary algorithm (EA). Additionally, a novel metric called l3-value is proposed to assist the analysis of the algorithm's convergence speed and exam the process of evolution. The experimental results show the effectiveness and superior performance of our EAMLS, compared to a CPLEX solver and a Genetic Algorithm (GA), on large-scale problems.

Continual Local Training for Better Initialization of Federated Models

Federated learning (FL) refers to the learning paradigm that trains machine learning models directly in the decentralized systems consisting of smart edge devices without transmitting the raw data, which avoids the heavy communication costs and privacy concerns. Given the typical heterogeneous data distributions in such situations, the popular FL algorithm \emph{Federated Averaging} (FedAvg) suffers from weight divergence and thus cannot achieve a competitive performance for the global model (denoted as the \emph{initial performance} in FL) compared to centralized methods. In this paper, we propose the local continual training strategy to address this problem. Importance weights are evaluated on a small proxy dataset on the central server and then used to constrain the local training. With this additional term, we alleviate the weight divergence and continually integrate the knowledge on different local clients into the global model, which ensures a better generalization ability. Experiments on various FL settings demonstrate that our method significantly improves the initial performance of federated models with few extra communication costs.

Knee Point Identification Based on Trade-Off Utility

Knee points, characterised as their smallest trade-off loss at all objectives, are attractive to decision makers in multi-criterion decision-making. In contrast, other Pareto-optimal solutions are less attractive since a small improvement on one objective can lead to a significant degradation on at least one of the other objectives. In this paper, we propose a simple and effective knee point identification method based on trade-off utility, dubbed KPITU, to help decision makers identify knee points from a given set of trade-off solutions. The basic idea of KPITU is to sequentially validate whether a solution is a knee point or not by comparing its trade-off utility with others within its neighbourhood. In particular, a solution is a knee point if and only if it has the best trade-off utility among its neighbours. Moreover, we implement a GPU version of KPITU that carries out the knee point identification in a parallel manner. This GPU version reduces the worst-case complexity from quadratic to linear. To validate the effectiveness of KPITU, we compare its performance with five state-of-the-art knee point identification methods on 134 test problem instances. Empirical results fully demonstrate the outstanding performance of KPITU especially on problems with many local knee points. At the end, we further validate the usefulness of KPITU for guiding EMO algorithms to search for knee points on the fly during the evolutionary process.

A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros

Applying latent variable evolution to game level design has become more and more popular as little human expert knowledge is required. However, defective levels with illegal patterns may be generated due to the violation of constraints for level design. A traditional way of repairing the defective levels is programming specific rule-based repairers to patch the flaw. However, programming these constraints is sometimes complex and not straightforward. An autonomous level repairer which is capable of learning the constraints is needed. In this paper, we propose a novel approach, CNet, to learn the probability distribution of tiles giving its surrounding tiles on a set of real levels, and then detect the illegal tiles in generated new levels. Then, an evolutionary repairer is designed to search for optimal replacement schemes equipped with a novel search space being constructed with the help of CNet and a novel heuristic function. The proposed approaches are proved to be effective in our case study of repairing GAN-generated and artificially destroyed levels of Super Mario Bros. game. Our CNet-assisted evolutionary repairer can also be easily applied to other games of which the levels can be represented by a matrix of objects or tiles.