Hybrid Local Search Algorithm Research Articles

Knowledge-Guided Parallel Hybrid Local Search Algorithm for Solving Time-Dependent Agile Satellite Scheduling Problems

As satellite capabilities have evolved and new observation requirements have emerged, satellites have become essential tools in disaster relief, emergency monitoring, and other fields. However, the efficiency of satellite scheduling still needs to be enhanced. Learning and optimization are symmetrical processes of solving problems. Learning problem knowledge could provide efficient optimization strategies for solving problems. A knowledge-guided parallel hybrid local search algorithm (KG-PHLS) is proposed in this paper to solve time-dependent agile Earth observation satellite (AEOS) scheduling problems more efficiently. Firstly, the algorithm uses heuristic algorithms to generate initial solutions. Secondly, a knowledge-based parallel hybrid local search algorithm is employed to solve the problem in parallel. Meanwhile, data mining techniques are used to extract knowledge to guide the construction of new solutions. Finally, the proposed algorithm has demonstrated superior efficiency and computation time through simulations across multiple scenarios. Notably, compared to benchmark algorithms, the algorithm improves overall efficiency by approximately 7.4% and 8.9% in large-scale data scenarios while requiring only about 60.66% and 31.89% of the computation time of classic algorithms. Moreover, the proposed algorithm exhibits scalability to larger problem sizes.

WordBlitz: An Efficient Hard-Label Textual Adversarial Attack Method Jointly Leveraging Adversarial Transferability and Word Importance

Existing textual attacks mostly perturb keywords in sentences to generate adversarial examples by relying on the prediction confidence of victim models. In practice, attackers can only access the prediction label, meaning that the victim model can easily defend against such hard-label attacks by denying access based on the attack’s frequency. In this paper, we propose an efficient hard-label attack approach, called WordBlitz. First, based on the adversarial transferability, we train a substitute model to initialize the attack parameter set, including a candidate pool and two weight tables of keywords and candidate words. Then, adversarial examples are generated and optimized under the guidance of the two weight tables. During optimization, we design a hybrid local search algorithm with word importance to find the globally optimal solution while updating the two weight tables according to the attack results. Finally, the non-adversarial text generated during perturbation optimization is added to the training of the substitute model as data augmentation to improve the adversarial transferability. Experimental results show that WordBlitz surpasses the baseline in terms of better effectiveness, higher efficiency, and lower cost. Its efficiency is especially pronounced in scenarios with broader search spaces, and its attack success rate on a Chinese dataset is higher than on baselines.

Hybrid Local Search Algorithm for Optimization Route of Travelling Salesman Problem

Hybrid Local Search Algorithm for Optimization Route of Travelling Salesman Problem

Reinforcement learning based coarse-to-fine search for the maximum [formula omitted]-plex problem

The maximum k-plex problem is a computationally challenging problem, which emerged from graph-theoretic social network studies in the context of community detection. As a relaxation of the traditional maximum clique problem, intended to identify cohesive structures within a graph that are not necessarily fully connected, the aim of this problem is to find the largest set of vertices in a graph in which all vertices are not adjacent to at most k vertices within this set. This paper introduces an effective hybrid local search algorithm for solving the maximum k-plex problem that combines a stochastic local search method and a reinforcement learning strategy. The proposed approach includes a number of distinguishing features, including: a new coarse-to-fine strategy to balance the search process, a distance-and-quality reward for actions, and a parameter control mechanism based on reinforcement learning. We conduct a computational analysis of the key components of the proposed algorithm, assessing their impact on the overall performance. Extensive experiments for the maximum k-plex problem (k=2,3,4,5) on 80 benchmark instances from the second DIMACS Challenge demonstrate that the proposed approach competes favourably with the state-of-the-art algorithms from the literature.

Stochastic multi-objective optimization approaches in a real-world oil field waterflood management

Multi-objective differential evolution with hybrid local dynamic search algorithm (HMODE-DLS) and non-dominated sorting genetic algorithm (NSGA-II) are applied successfully in a reservoir sector model of a Middle Eastern real-world oil field under waterflood development. The Pareto solutions of the two objectives of maximum oil production and minimum water production obtained by the two algorithms are analyzed and compared in this study. The sector model consists of four oil producers and three water injectors and it is run for ten years with twenty time-steps of six months each. A total of 140 decision variables are defined as bottom-hole pressures for the four producers and water injection rates for the three water injectors at each time step (i.e. 7 variables by 20 time-steps). The optimal Pareto solution is obtained at 12,600 function evaluations by HMODE-DLS with 70% convergence improvement compared to 42,000 objective evaluations by NSGA-II. Results show that NSGA-II generated more optimum solutions in the lower range (i.e. less than 480 thousand m3 (Mm3) total oil production) whereas, the hybrid MODE-DLS has a more optimum solution in the higher range of the Pareto front. The net flow method (NFM) is usedin this study to select the best optimal solutions from the Pareto front solutions based on the weight of the objective function chosen by the decision-maker. By using the entropy weight criteria, the best optimal solution of high oil production determined is 540.45 Mm3 at the HMODE-DLS Pareto front. On the other hand, the best optimal solution of low oil production obtained is 469.93 Mm3 at the NSGA-II Pareto front. The combined optimal Pareto fronts obtained by NSGA-II and HMODE-DLS algorithms offer good spread and diverse solutions for the decision-maker to select and operate the field based on the operation conditions and constraints.

A hybrid local search algorithm for minimum dominating set problems

Minimum dominating set (MDS) is a well-known NP-hard fundamental graph theory problem having many applications such as mining social networks and bioinformatics. MDS seeks for the minimum subset of vertices in which every vertex not in the selected subset is adjacent to at least one vertex of this subset. In this study, we consider MDS and a complex variant of MDS known as the minimum positive influence dominating set (MPIDS). In MPIDS, the aim is to identify a subset of vertices where each vertex of a graph must be dominated by at least half of its neighbors. To solve these problems, we propose a two-stage hybrid local search algorithm. In the first stage, we propose an adaptive information content-based local search algorithm as an exploratory procedure. This algorithm focuses on generating promising solutions in different areas of the solution space using the problem search history. Moreover, we introduce information content strategy-based neighborhood structure and tolerance-based features to evolve high-quality and diverse solutions. For the second stage, we propose a gain-based deterministic local search algorithm as an exploitation procedure. It navigates feasible neighborhood areas to improve the solution quality. We conducted extensive analysis to evaluate the impact of the proposed components. The proposed algorithm produced very good results and generalized well overall instances of both MDS and MPIDS. Compared to the literature, the proposed algorithm outperformed other algorithms in most of the tested instances.

A Local Search Q-learning Based Approach for Multi-period Vehicle Redeployment

The problem of vehicle redeployment in multi-period systems is studied in this paper. It is a sequential decision making problem that involves optimizing an objective over some planning horizon composed of multiple periods. In this work, the problem is handled with a hybrid local search and Q-learning algorithm. It integrates local search into the Dyna-Q algorithm and combines the system’s tactical and operational decision levels to find a multi-period policy for sharing vehicles between stations while optimizing the objective. Tests were carried out on synthetic and real instances, and the obtained results show how local search integration can improve the basic Dyna-Q algorithm.

An Ultra-Fast Power Prediction Method Based on Simplified LSSVM Hyperparameters Optimization for PV Power Smoothing

With existing power prediction algorithms, it is difficult to satisfy the requirements for prediction accuracy and time when PV output power fluctuates sharply within seconds, so this paper proposes a high-precision and ultra-fast PV power prediction algorithm. Firstly, in order to shorten the optimization time and improve the optimization accuracy, the single-iteration Gray Wolf Optimization (SiGWO) method is used to simplify the iteration process of the hyperparameters of Least Squares Support Vector Machine (LSSVM), and then the hybrid local search algorithm composed of Iterative Local Search (ILS) and Self-adaptive Differential Evolution (SaDE) is used to improve the accuracy of hyperparameters, so as to achieve high-precision and ultra-fast PV power prediction. The power prediction model is established, and the proposed algorithm is applied in a test experiment which can complete the power prediction within 3 s, and the RMSE is only 0.44%. Finally, combined with the PV-storage advanced smoothing control strategy, it is verified that the performance of the proposed algorithm can satisfy the system’s requirements for prediction accuracy and time under the condition of power mutation in a PV power generation system.

An effective hybrid local search approach for the post enrolment course timetabling problem

We address the post enrolment course timetabling (PE-CTT) problem in this paper. PE-CTT is known as an NP-hard problem that focuses on finding an efficient allocation of courses onto a finite number of time slots and rooms. It is one of the most challenging resources allocation problems faced by universities around the world. This work proposes a two-phase hybrid local search algorithm to address the PE-CTT problem. The first phase focuses on finding a feasible solution, while the second phase tries to minimize the soft constraint violations of the generated feasible solution. For the first phase, we propose a hybrid of Tabu Search with Sampling and Perturbation with Iterated Local Search. We test the proposed methodology on the hardest cases of PE-CTT benchmarks. The hybrid algorithm performs well and our results are superior compared to the recent methods in finding feasible solutions. For the second phase, we propose an algorithm called Simulated Annealing with Reheating (SAR) with two preliminary runs (SAR-2P). The SAR algorithm is used to minimize the soft constraint violations by exploiting information collected from the preliminary runs. We test the proposed methodology on three publicly available datasets. Our algorithm is competitive with the standards set by the recent methods. In total, the algorithm attains new best results for 3 cases and new best mean results for 7 cases. Furthermore, it is scalable when the execution time is extended.

A hybrid local search algorithm for consistent periodic vehicle routing problem

A hybrid local search algorithm for consistent periodic vehicle routing problem

A Hybrid Local Search Algorithm for the Consistent Periodic Vehicle Routing Problem

A Hybrid Local Search Algorithm for the Consistent Periodic Vehicle Routing Problem

An Iterated Hybrid Local Search Algorithm for Pick-and-Place Sequence Optimization

This paper shows the results of our study on the pick-and-place optimization problem. To solve this problem efficiently, an iterated hybrid local search algorithm (IHLS) which combines local search with integer programming is proposed. In the section of local search, the greedy algorithm with distance weight strategy and the convex-hull strategy is developed to determine the pick-and-place sequence; in the section of integer programming, an integer programming model is built to complete the feeder assignment problem. The experimental results show that the IHLS algorithm we proposed has high computational efficiency. Furthermore, compared with the genetic algorithm and the memetic algorithm, the IHLS is less time-consuming and more suitable in solving a large-scale problem.

A simple and robust Monte Carlo hybrid local search algorithm for the facility location problem

ABSTRACTIn this article, a new Monte Carlo hybrid local search algorithm (Hyb-LS) is proposed for solving the uncapacitated facility location problem. Hyb-LS is based on repeated sampling using two local search strategies based on best improvement and randomized neighbourhood search. A major advantage of Hyb-LS for its practical use is that the number of restarts is its only parameter to tune. The algorithm is also simple to reimplement, scalable and robust to changes in coefficients within a problem instance. The stopping criterion for local search is learned automatically. Experimental results are presented for four representative and contrasting cost and distance models. The results obtained by Hyb-LS are compared to the optimal or near-optimal solutions found by a mixed integer linear programming (MILP) solver with a generous time limit. For three out of the four models, Hyb-LS obtains better solutions than the upper bound found by the MILP solver for at least one instance.

Bi-level hybrid local search approach for three-dimensional loading problem with balancing constraints

This paper presents a bi-level hybrid local search (BHLS) algorithm for the three-dimensional loading problem with balancing constraints (3DLP-B), where several rectangular boxes with even densities but different sizes are loaded into a single cubic bin to meet the requirements of the space or capacity utilization and the balance of the center of gravity. The proposed algorithm hybridizes a novel framed-layout procedure in which the concept of the core block and its generation strategy are introduced. Once the block-loading sequence has been determined, we can load one block at a time by the designed construction heuristic. Then, the double-search is introduced; its external search procedure generates a list of compact packing patterns while its internal search procedure is used to search the core-block frames and their best distribution locations. The approach is extensively tested on weakly to strongly heterogeneous benchmark data. The results show that it has better performance in improving space utilization rate and balanced condition of the placement than existed techniques: the overall averages from 79.85% to 86.45% were obtained for the balanced cases and relatively high space-usage rate of 89.44% was achieved for the unbalanced ones.

A Hybrid Local Search Algorithm for the Sequence Dependent Setup Times Flowshop Scheduling Problem with Makespan Criterion

This paper focuses on the flowshop scheduling problem with sequence dependent setup times (FSSP-SDST), which has been an investigated object for decades as one of the most popular scheduling problems in manufacturing systems. A novel hybrid local search algorithm called HLS is presented to solve the flowshop scheduling problem with sequence dependent setup times with the criterion of minimizing the makespan. Firstly, the population is initialized by the Nawaz-Enscore-Hoam based problem-specific method (NEHBPS) to generate high quality individuals of the current population. Then, a global search embedded with a light perturbation is designed to produce a new population. After that, to improve the quality of individuals in the current population, a single insertion-based local search is applied. Meanwhile, a further local search strategy based on the insertion-based local search is used to find better solutions for the individuals which are non-improved. Finally, the heavy perturbation is used to explore potential solutions in the neighbor region. To validate the performance of HLS, we compare our proposed algorithm with other competitive algorithms on Taillard benchmark problems. From the experimental results, it can be concluded that the proposed algorithm outperforms the benchmark algorithms.

A hybrid local-search algorithm for robust job-shop scheduling under scenarios

This paper discusses an uncertain job-shop scheduling problem with the makespan as the performance criterion. Uncertain processing times are described by discrete scenarios. A robust optimization model is established for the job-shop scheduling problem based on a set of bad scenarios to hedge against the risk of achieving substandard performances among these bad scenarios. To solve the established problem, a problem-specific neighborhood structure is constructed by uniting multiple single-scenario neighborhoods. The constructed neighborhood structure is applied in a hybrid local-search algorithm of combining the simulated-annealing search and the tabu technique. An extensive computational experiment was conducted. The developed algorithm was compared with two possible alternative algorithms. The computational results show the efficiency of the defined neighborhood structure and the competitiveness of the developed hybrid local-search algorithm for the established model.

Two phased hybrid local search for the periodic capacitated arc routing problem

The periodic capacitated arc routing problem (PCARP) is a challenging general model with important applications. The PCARP has two hierarchical optimization objectives: a primary objective of minimizing the number of vehicles (Fv) and a secondary objective of minimizing the total cost (Fc). In this paper, we propose an effective two phased hybrid local search (HLS) algorithm for the PCARP. The first phase makes a particular effort to optimize the primary objective while the second phase seeks to further optimize both objectives by using the resulting number of vehicles of the first phase as an upper bound to prune the search space. For both phases, combined local search heuristics are devised to ensure an effective exploration of the search space. Experimental results on 63 benchmark instances demonstrate that HLS performs remarkably well both in terms of computational efficiency and solution quality. In particular, HLS discovers 44 improved best known values (new upper bounds) for the total cost objective Fc while attaining all the known optimal values regarding the objective of the number of vehicles Fv. To our knowledge, this is the first PCARP algorithm reaching such a performance. Key components of HLS are analyzed to better understand their contributions to the overall performance.

Local search algorithm with path relinking for single batch-processing machine scheduling problem

The single batch-processing machine problem is to minimize makespan, which has broad applications, including engineering fundamentals and theoretical background. The machine can process several jobs as a batch simultaneously, and every job has three different attributes: job size, processing time, and job arrival. In this paper, a hybrid local search algorithm with path relinking (LP) is devised to solve the problem. We not only generate an optimal initial solution firstly but also pay more attention to improving the solution quality. Three kinds of local searches are applied to enhance the diversity of solutions; the path relinking is adopted to explore better solutions through local tracks connecting the current solution and the best in the elite set. With these approaches, it can keep a balanced rate between exploratory and exploitative capacity. Computational experiments on 40 benchmark instances indicate that LP has the superior convergence and robust performance and it surpasses the current state-of-the-art methods such as genetic algorithm and ant colony optimization, especially for large instances.

A hybrid metaheuristic algorithm for heterogeneous vehicle routing problem with simultaneous pickup and delivery

The Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is a variant of the classical Vehicle Routing Problem (VRP) where the vehicles serve a set of customers demanding pickup and delivery services at the same time. The VRPSPD can arise in many transportation systems involving both distribution and collection operations. Originally, the VRPSPD assumes a homogeneous fleet of vehicles to serve the customers. However, in many practical situations, there are different types of vehicles available to perform the pickup and delivery operations. In this study, the original version of the VRPSPD is extended by assuming the fleet of vehicles to be heterogeneous. The Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery (HVRPSPD) is considered to be an NP-hard problem because it generalizes the classical VRP. For its solution, we develop a hybrid local search algorithm in which a non-monotone threshold adjusting strategy is integrated with tabu search. The threshold function used in the algorithm has an adaptive nature which makes it self-tuning. Additionally, its implementation is very simple as it requires no parameter tuning except for the tabu list length. The proposed algorithm is applied to a set of randomly generated problem instances. The results indicate that the developed approach can produce efficient and effective solutions.

An Efficient Two-Objective Hybrid Local Search Algorithm for Solving the Fuel Consumption Vehicle Routing Problem

The classical model of vehicle routing problem (VRP) generally minimizes either the total vehicle travelling distance or the total number of dispatched vehicles. Due to the increased importance of environmental sustainability, one variant of VRPs that minimizes the total vehicle fuel consumption has gained much attention. The resulting fuel consumption VRP (FCVRP) becomes increasingly important yet difficult. We present a mixed integer programming model for the FCVRP, and fuel consumption is measured through the degree of road gradient. Complexity analysis of FCVRP is presented through analogy with the capacitated VRP. To tackle the FCVRP’s computational intractability, we propose an efficient two-objective hybrid local search algorithm (TOHLS). TOHLS is based on a hybrid local search algorithm (HLS) that is also used to solve FCVRP. Based on the Golden CVRP benchmarks, 60 FCVRP instances are generated and tested. Finally, the computational results show that the proposed TOHLS significantly outperforms the HLS.