Low-level Heuristics Research Articles

Learning-aided Neighborhood Search for Vehicle Routing Problems.

The Vehicle Routing Problem (VRP) is a classic optimization problem with diverse real-world applications. The neighborhood search has emerged as an effective approach, yielding high-quality solutions across different VRPs. However, most existing studies exhaustively explore all considered neighborhoods with a pre-fixed order, leading to an inefficient search process. To address this issue, this paper proposes a Learning-aided Neighborhood Search algorithm (LaNS) that employs a cutting-edge multi-agent reinforcement learning-driven adaptive operator/neighborhood selection mechanism to achieve efficient routing for VRP. Within this framework, two agents serve as high-level instructors, collaboratively guiding the search direction by selecting perturbation/improvement operators from a pool of low-level heuristics. Furthermore, to equip the agents with comprehensive information for learning guidance knowledge, we have developed a new informative state representation. This representation transforms the spatial route structures into an image-like tensor, allowing us to extract spatial features using a convolutional neural network. Comprehensive evaluations on diverse VRP benchmarks, including the capacitated VRP (CVRP), multi-depot VRP (MDVRP) and cumulative multi-depot VRP with energy constraints, demonstrate LaNS's superiority over the state-of-the-art neighborhood search methods as well as the existing learning-guided neighborhood search algorithms.

A Monte Carlo hyper-heuristic algorithm with low-level heuristics reward prediction for missile path planning

A Monte Carlo hyper-heuristic algorithm with low-level heuristics reward prediction for missile path planning

A reinforcement learning hyper-heuristic algorithm for the distributed flowshops scheduling problem under consideration of emergency order insertion

A reinforcement learning hyper-heuristic algorithm for the distributed flowshops scheduling problem under consideration of emergency order insertion

An efficient Q-learning integrated multi-objective hyper-heuristic approach for hybrid flow shop scheduling problems with lot streaming

An efficient Q-learning integrated multi-objective hyper-heuristic approach for hybrid flow shop scheduling problems with lot streaming

Explainable Optimisation through Online and Offline Hyper-heuristics

Abstract - Research in the explainability of optimisation techniques has largely focused on metaheuristics and their movement of solutions around the search landscape. Hyper-heuristics create a different challenge for explainability as they make use of many more operators, or low-level heuristics and learning algorithms which modify their probability of selection online. This paper describes a set of methods for explaining hyper-heuristics decisions in both online and offline scenarios using selection hyper-heuristics as an example. These methods help to explain various aspects of the function of hyper-heuristics both at a particular juncture in the optimisation process and through time. Visualisations of each method acting on sequences provide an understanding of which operators are being utilised and when, and in which combinations to produce a greater understanding of the algorithm-problem nexus in hyper-heuristic search. These methods are demonstrated on a range of problems including those in operational research and water distribution network optimisation. They demonstrate the insight that can be generated from optimisation using selection hyper-heuristics, including building an understanding of heuristic usage, useful combinations of heuristics and heuristic parameterisations. Furthermore the dynamics of heuristic utility are explored throughout an optimisation run and we show that it is possible to cluster problem instances according to heuristic selection alone, providing insight into the perception of problems from a hyper-heuristic perspective.

Multi-robot task allocation for optional tasks with hidden workload: Using a model-based hyper-heuristic strategy

Multi-robot task allocation for optional tasks with hidden workload: Using a model-based hyper-heuristic strategy

Knowledge-enhanced multidimensional estimation of distribution hyper-heuristic evolutionary algorithm for semiconductor final testing scheduling problem

Knowledge-enhanced multidimensional estimation of distribution hyper-heuristic evolutionary algorithm for semiconductor final testing scheduling problem

A Self-Learning Hyper-Heuristic Algorithm Based on a Genetic Algorithm: A Case Study on Prefabricated Modular Cabin Unit Logistics Scheduling in a Cruise Ship Manufacturer.

Hyper-heuristic algorithms are known for their flexibility and efficiency, making them suitable for solving engineering optimization problems with complex constraints. This paper introduces a self-learning hyper-heuristic algorithm based on a genetic algorithm (GA-SLHH) designed to tackle the logistics scheduling problem of prefabricated modular cabin units (PMCUs) in cruise ships. This problem can be regarded as a multi-objective fuzzy logistics collaborative scheduling problem. Hyper-heuristic algorithms effectively avoid the extensive evaluation and repair of infeasible solutions during the iterative process, which is a common issue in meta-heuristic algorithms. The GA-SLHH employs a genetic algorithm combined with a self-learning strategy as its high-level strategy (HLS), optimizing low-level heuristics (LLHs) while uncovering potential relationships between adjacent decision-making stages. LLHs utilize classic scheduling rules as solution support. Multiple sets of numerical experiments demonstrate that the GA-SLHH exhibits a stronger comprehensive optimization ability and stability when solving this problem. Finally, the validity of the GA-SLHH in addressing real-world decision-making issues in cruise ship manufacturing companies is validated through practical enterprise cases. The results of a practical enterprise case show that the scheme solved using the proposed GA-SLHH can reduce the transportation time by up to 37%.

Training feedforward neural networks with Bayesian hyper-heuristics

The process of training feedforward neural networks (FFNNs) can benefit from an automated process where the best heuristic to train the network is sought out automatically by means of a high-level probabilistic-based heuristic. This research introduces a novel population-based Bayesian hyper-heuristic (BHH) that is used to train feedforward neural networks (FFNNs). The performance of the BHH is compared to that of ten popular low-level heuristics, each with different search behaviours. The chosen heuristic pool consists of classic gradient-based heuristics as well as meta-heuristics (MHs). The empirical process is executed on fourteen datasets consisting of classification and regression problems with varying characteristics. The BHH is shown to be able to train FFNNs well and provide an automated method for finding the best heuristic to train the FFNNs at various stages of the training process.

A hyper-heuristic algorithm via proximal policy optimization for multi-objective truss problems

A hyper-heuristic algorithm via proximal policy optimization for multi-objective truss problems

Decision-focused neural adaptive search and diving for optimizing mining complexes

Decision-focused neural adaptive search and diving for optimizing mining complexes

Recognition and Prediction of Multi-Level Handling Complexity at Automated Terminals Based on ARIMA

Accurate recognition and prediction of the multi-level handling complexity in automated container terminals (referred to as “automated terminals”) is a prerequisite for improving the effectiveness of scheduling and realizing intelligent operation and maintenance. According to the operating characteristics of the automated terminal equipment, the operating network is constructed of automated terminals that describe the characteristics of operating complexity. We use K-medoids and a light gradient boosting machine (LightGBM) to construct a K-LightGBM model that recognizes multi-level handling complexity. The key lies in the reasonable construction of prediction models. A hyper-heuristic autoregressive integrated moving average (ARIMA) model is proposed to address the problem that the ARIMA is ineffective in predicting nonlinear data. We combine ARIMA and the LightGBM model to establish an ARIMA-LightGBM model to predict multi-level handling and residuals. To improve accuracy, we propose the two residual prediction strategies of direct prediction and limited residual boundary prediction based on the residuals generated by ARIMA. We propose a hyper-heuristic algorithm based on a gradient descent-trust region (GD-TR) to compute the weights of predicted values under the two strategies, which improves the global search capability by GD and TR. The particle swarm optimization algorithm, simulated annealing algorithm, and ant colony optimization algorithm are low-level heuristics. Simulation results show that the proposed model possesses the lowest root mean square error on all characteristics compared to ARIMA, E-ARIMA, and ARIMA-LSTM. Therefore, the proposed model is very effective in improving the accuracy of predicting the multi-level handling complexity at automated terminals.

An emprical study on multi-meme memetic algorithms which includes Choice Function, Reinforcement Learning and Simple Random Hyper-heuristics *

Hyper-heuristics are designed to be reusable, domain-independent methods for addressing complex computational issues. While there are specialized approaches that work well for particular problems, they often require parameter tuning and cannot be transferred to other problems. Memetic Algorithms combine genetic algorithms and local search techniques. The evolutionary interaction of memes allows for the creation of intelligent complexes capable of solving computational problems. Hyper-heuristics are a high-level search technique that operates on a set of low-level heuristics that directly address the solution. They have two main components: heuristic selection and move acceptance mechanisms. The heuristic selection method determines which low-level heuristic to use, while the move acceptance mechanism decides whether to accept or reject the resulting solution. In this study, we explore a multi-meme memetic algorithm as a hyper-heuristic that integrates and manages multiple hyper-heuristics (Modified Choice Function All Moves, Reinforcement Learning with Great Deluge, and Simple Random Only Improvement) and parameters of heuristics (such as mutation rates and search depth). We conducted an empirical study testing two different variations of the proposed hyper-heuristic. The first algorithm uses the Only Improvement acceptance technique for both Reinforcement Learning and Simple Random, and All Moves for Modified Choice Function. In the second version, the Great Deluge method replaces Only Improvement for Reinforcement Learning. The second algorithm's results outperformed most competitors from the CHeSC2011 competition, achieving the forth-best hyper-heuristic performance.

The performance analysis of hyper-heuristics algorithms over examination timetabling problems

In general, uncapacitated exam timetabling is conducted manually, which can be time-consuming. Many studies aim to automate and optimize uncapacitated exam timetabling. However, pinpointing the most efficient algorithm is challenging since most studies assert that their algorithms surpass previous ones. To identify the optimal algorithm, this research evaluates the performance of four algorithms: Hill climbing (HC), simulated annealing (SA), great deluge (GD), and tabu search (TS) in addressing the exam timetabling problem. The Kempe chain operator’s influence on optimization solutions is also examined. A simple random method is employed to select the low-level heuristic (LLH). The Carter (Toronto) dataset served as the test material, with each algorithm undergoing 200,000 iterations for comparison. The results indicate that the TS algorithm is superior, providing the best solution in 13 instances. The use of a tabu list enhanced the search process’s efficiency by preventing redundant modifications. The Kempe chain LLH exhibited a tendency towards achieving better solutions.

Round-trip hub location problem

In this paper, we introduce a novel network design for the Hub Location Problem, inspired by the round-trip structure commonly used by transport service providers. Our design integrates spoke nodes assigned to a central hub node, creating round-trips where the hub node serves as the starting point, visits all assigned spoke nodes, and returns to the hub. To enhance transportation services and provide additional redundancy, we introduce a new type of nodes called runaway nodes to the network. The motivation for this research arises from two real-life cases encountered during consultancy projects, underscoring the necessity for an optimized network design in transportation services. To address the proposed problem, we introduce a mixed-integer linear programming (MIP) mathematical model. However, due to the problem's complexity, the feasibility of the MIP model is limited to small-scale instances. To tackle medium and large-scale instances, we introduce two hyper-heuristic approaches based on reinforcement learning. These hyper-heuristic approaches harness the power of reinforcement learning to guide the selection of low-level heuristics and improve solution quality. We conduct extensive computational experiments to evaluate the efficiency and effectiveness of the proposed approaches. The results of our experiments affirm the efficiency of the proposed hyper-heuristic approaches, showcasing their ability to discover high-quality solutions for the Hub Location Problem.

A compass-based hyper-heuristic for multi-objective optimization problems

A compass-based hyper-heuristic for multi-objective optimization problems

A reinforcement learning-based hyper-heuristic for AGV task assignment and route planning in parts-to-picker warehouses

A reinforcement learning-based hyper-heuristic for AGV task assignment and route planning in parts-to-picker warehouses

Dynamic metaheuristic selection via Thompson Sampling for online optimization

Dynamic metaheuristic selection via Thompson Sampling for online optimization

Solution of the family traveling salesman problem using a hyper-heuristic approach

Solution of the family traveling salesman problem using a hyper-heuristic approach

A feedback learning-based selection hyper-heuristic for distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time

A feedback learning-based selection hyper-heuristic for distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time