Iterated Local Search Research Articles

AnnoDUF: A Web-Based Tool for Annotating Functions of Proteins Having Domains of Unknown Function.

The rapid expansion of biological sequence databases due to high-throughput genomic and proteomic sequencing methods has left a considerable number of identified protein sequences with unclear or incomplete functional annotations. Domains of unknown function (DUFs) are protein domains that lack functional annotations but are present in numerous proteins. To address the challenge of finding functional annotations for DUFs, we have developed a computational method that efficiently identifies and annotates these enigmatic protein domains by utilizing the position-specific iterative basic local alignment search tool (PSI-BLAST) and data mining techniques. Our pipeline identifies putative potential functionalities of DUFs, thereby decreasing the gap between known sequences and functions. The tool can also take user input sequences to annotate. We executed our pipeline on 5111 unique DUF sequences obtained from Pfam, resulting in putative annotations for 2007 of these. These annotations were subsequently incorporated into a comprehensive database and interfaced with a web-based server named "AnnoDUF". AnnoDUF is freely accessible to both academic and industrial users, via the World Wide Web at the link http://bts.ibab.ac.in/annoduf.php. All scripts used in this study are uploaded to the GitHub repository, and these can be accessed from https://github.com/BioToolSuite/AnnoDUF.

Integrating storage allocation with manual order picking and replenishment operations in a distribution centre

ABSTRACT This paper introduces a mathematical programming formulation and a simulation-based heuristic for the allocation of storage positions to products picked by human operators on man-aboard vehicles traveling through the warehouse of a wholesale company. In this problem, the tactical level of the assignment decisions affects the operational level of the picking process. We propose a simulation-optimisation framework that integrates the two. Our formulation of the storage location assignment problem also handles the constraint according to which a picking position should be paired with a (vertical) replenishment position for a given item. To solve realistic instances, we design an iterated local search (ILS) metaheuristic with an embedded discrete-event simulator (DES) that evaluates the most promising moves at each iteration. The DES allows reproducing the handling operations performed by multiple order pickers under uncertainty, mutual interferences and congestion-related phenomena. Overall, the flexible simulation-optimisation (SO) framework evaluates the operational times and daily productivity of the order picking organisation. Numerical results are presented for real data, under an S-shape picking policy with a skip-and-go rule to deal with lacking items. Under a proper tuning of the ILS parameters, the SO framework allows to achieve a nearly 17% improvement in warehouse productivity.

On the impact of co-optimizing station locations, trip assignment, and charging schedules for electric buses

As many public transportation systems around the world transition to electric buses, the planning and operation of fleets can be improved via tailored decision-support tools. In this work, we study the impact of jointly locating charging facilities, assigning electric buses to trips, and determining when and where to charge the buses. We propose a mixed integer linear program that co-optimizes planning and operational decisions jointly and an iterated local search heuristic to solve large-scale instances. Herein, we use a concurrent scheduler algorithm to generate an initial feasible solution, which serves as a starting point for our iterated local search algorithm. In the sequential case, we first optimize trip assignments and charging locations. Charging schedules are then determined after fixing the optimal decisions from the first level. The joint model, on the other hand, integrates charge scheduling within the local search procedure. The solution quality of the joint and sequential iterated local search models are compared for multiple real-world bus transit networks. Our results demonstrate that joint models can help further improve operating costs by 14.1% and lower total costs by about 4.1% on average compared with sequential models. In addition, energy consumption costs and contracted power capacity costs have been reduced significantly due to our integrated planning approach.

Multi-AUVs cooperative path planning in 3D underwater terrain and vortex environments based on improved multi-objective particle swarm optimization algorithm

This paper presents an improved multi-objective particle swarm optimization (IMOPSO) algorithm for cooperative path planning of multiple autonomous underwater vehicles (multi-AUVs) in 3D underwater terrain and vortex environment. Traditional PSO algorithms often struggle with local optima in high-dimensional, complex search spaces. To address this, we propose enhancements including Sine double-sequence perturbation interpolation for initial particle population generation, adaptive differential evolution strategies, iterative local search, and traction operations to enhance global search capability and avoid local optima. Additionally, cubic spline interpolation is employed to smooth the planned paths. The objective function is tailored to the ocean currents environment, considering path length, smoothness, deflection angle, and current impact, with a penalty function method to handle multi-AUVs cooperation and environmental constraints optimization for optimal energy consumption. Simulation results demonstrate that the proposed algorithm effectively plans short-range, high-security global paths for multi-AUVs, verifying its effectiveness and practicality in meeting the navigation requirements in ocean currents environments.

MILP-based framework for seismic resilience evaluation and enhancement of substations

In order to minimize earthquake impacts, substation rehabilitation requires an optimal sequence for defining repair priorities. In this paper, a continuous rehabilitation process is discretized to reduce the nonlinearity of the seismic resilience evaluation. Thus, a newly proposed post-earthquake rehabilitation strategy based on mixed integer linear programming (MILP) can be applied to find the optimal rehabilitation sequence. In particular, this strategy can evaluate the global optimality of the obtained rehabilitation sequence. A differentiated retrofitting strategy for substation equipment based on iterated local search (ILS) is also proposed to improve the seismic robustness. Case study shows that the MILP rehabilitation strategy can find out the globally optimal rehabilitation sequences in most simulated scenarios, which obviously increases the restorative capacity. Meanwhile, the proposed retrofitting strategy improves the seismic robustness more significantly than other strategies. Thus, the seismic resilience of the substation was enhanced comprehensively by the proposed method.

Design of Routes for Collaborative Robots in the Automobile Painting Process through a Comparison of Perturbative Heuristics for Iterated Local Search

Design of Routes for Collaborative Robots in the Automobile Painting Process through a Comparison of Perturbative Heuristics for Iterated Local Search

Modified-improved fitness dependent optimizer for complex and engineering problems

Fitness dependent optimizer (FDO) is considered one of the novel swarm intelligent algorithms. Recently, FDO has been enhanced several times to improve its capability. One of the improvements is called improved FDO (IFDO). However, according to the research findings, the variants of FDO are constrained by two primary limitations that have been identified. Firstly, if the number of agents employed falls below five, it significantly diminishes the algorithm's precision. Secondly, the efficacy of FDO is intricately tied to the quantity of search agents utilized. To overcome these limitations, this study proposes a modified version of IFDO, called M-IFDO. The enhancement is conducted by updating the location of the scout bee to the IFDO to move the scout bees to achieve better performance and optimal solutions. More specifically, two parameters in IFDO, which are alignment and cohesion, are removed. Instead, the Lambda parameter is replaced in the place of alignment and cohesion. To verify the performance of the newly introduced algorithm, M-IFDO is tested on 19 basic benchmark functions, 10 IEEE Congress of Evolutionary Computation (CECC06 2019), and five real-world problems. M-IFDO is compared against five state-of-the-art algorithms: Improved Fitness Dependent Optimizer (IFDO), Improving Multi-Objective Differential Evolution algorithm (IMODE), Hybrid Sampling Evolution Strategy (HSES), Linear Success-History based Parameter Adaptation for Differential Evolution (LSHADE) and CMA-ES Integrated with an Occasional Restart Strategy and Increasing Population Size and An Iterative Local Search (NBIPOP-aCMAES). The verification criteria are based on how well the algorithm reaches convergence, memory usage, and statistical results. The results show that M-IFDO surpasses its competitors in several cases on the benchmark functions and five real-world problems.

Metaheuristics for solving a flexible flow‐shop scheduling problem with s‐batching machines

AbstractA scheduling problem for a two‐stage flexible flow shop with s‐batching machines motivated by processes in additive manufacturing is considered. A batch is a group of jobs that are processed together on a single machine. Each job belongs to an incompatible family. Only jobs of the same family can be batched together. A maximum batch size is given. A setup time occurs between different batches. The jobs of a batch are processed in a serial manner, that is, the processing time of a batch is the sum of the processing times of the jobs forming the batch. Batch availability is assumed. A job has a weight, a due date, and a release date. The total weighted tardiness is considered as performance measure. We establish a mixed integer linear programming formulation for this scheduling problem. For large‐sized problem instances, an iterative decomposition approach (IDA) is proposed that uses a grouping genetic algorithm or an iterated local search (ILS) scheme to solve the single‐stage subproblems. Moreover, an alternative ILS scheme based on a disjunctive graph representation of the problem at hand is designed. Results of computational experiments based on randomly generated problem instances demonstrate that the IDA hybridized with ILS outperforms the two other schemes.

An Iterated Local Search Heuristic for the Multi-Trip Vehicle Routing Problem with Multiple Time Windows

This paper studies the multi-trip vehicle routing problem with multiple time windows, which extends the multi-trip vehicle routing problem by deciding not only the sequence of customers that each vehicle serves but also the service time window of each customer. It also requires that the delivery service time is within the selected time windows and that the total demand of the customers served by the vehicle on each trip does not exceed the maximum carrying capacity. For solving the studied problem, we develop a mixed integer linear programming model with the objective of minimizing the total travel distance of vehicles and design a tailored iterative local search heuristic. Within the framework of the iterative local search, an improved Solomon greedy insertion algorithm suitable for multiple time windows and multi-trip scenarios is designed to generate the initial solution, and local search operators such as Or-opt and Relocate, as well as Random Exchange perturbation operations, are also developed. The experiment results demonstrate the effectiveness of the proposed model and algorithm and confirm that by providing customers with multiple time windows option, carriers can flexibly plan vehicle routes and select appropriate service time windows, thereby reducing the number of vehicles used and the total distance travelled and improve delivery success.

Team orienteering with possible multiple visits: Mathematical model and solution algorithms

This study addresses a new team orienteering problem in which each point can be visited possibly multiple times with decreasing scores. The problem is to determine a set of routes from a starting to a finishing point within an allowed travel time limit for the objective of maximizing the total collected score. A mixed integer programming model is developed to represent the problem mathematically. Then, due to the complexity of the problem, a basic iterated local search (ILS) algorithm is proposed that incorporates the ordinary perturbation and local search operators. Then, it is improved in two ways: (a) an extended ILS algorithm with multi-visit based local search operators and (b) a hybrid ILS algorithm with a simulated annealing technique to permit non-improving moves in the local search step. To test the performance of the ILS algorithms, computational experiments were done on modified benchmark instances under the cases of linear, convex and concave score decreases, and the results show that the hybrid ILS algorithm improves the others significantly and also gives near optimal solutions for small sized test instances. Finally, a case study was done for a naval warship reconnaissance operation, and the results are reported.

Heuristic approaches for scheduling of a two-machine flowshop with outsourcing lead-time

This article studies a scheduling problem of a two-machine flowshop , considering outsourcing lead-time, where outsourcing a job incurs outsourcing cost as well as outsourcing lead-time. Each job has an option of being outsourced, i.e. the first operation of the outsourced job is handled by an external subcontractor while the second operation must be processed in-house after its delivery. The objective is to find a schedule for minimizing the weighted sum of the outsourcing costs and the total completion time. For the problem, the subset of jobs to be outsourced and the processing order of jobs are interwoven, implying that decisions have to be made simultaneously. This study examines the properties of the optimal solution and proposes various heuristics based on iterative local search and the biased random-key genetic algorithm. Extensive computational experiments are carried out in various settings.

The capacity matching problem of the third-party shared manufacturing platform with capacity time windows and order splitting

The development of sharing economy and new information technologies has promoted the emergence of third-party shared manufacturing platforms (TPSMPs). In the shared manufacturing context, one main challenge to TPSMPs is the matching of manufacturing enterprises with insufficient production capacity (capacity demanders) and those with overcapacity (capacity suppliers), where available capacities of capacity suppliers are within time ranges, and orders of capacity demanders can be split. In this capacity matching problem with capacity time windows and order splitting (CMPCTW-OS), each capacity demander's order needs to be delivered on time, while each capacity supplier can also match with multiple capacity demanders and fulfil orders of the capacity demanders by sequence. A mathematical model for the CMPCTW-OS is developed to maximise the total profit of the TPSMP. Then, we design a two-stage heuristic algorithm to solve this model. In the first stage, the inserting algorithm (IA) is used to obtain an initial feasible solution. In the second stage, the iterated local search (ILS) is applied to optimise and improve the initial feasible solution. Finally, in numerical simulation experiments, the effectiveness of IA-ILS has been verified by comparison with the GUROBI solver.

Minimizing the maximum tardiness for a permutation flow shop problem under the constraint of sequence independent setup time

In this work, we will study a permutation flow shop scheduling problem under the constraint of sequence independent setup time. In our case, each machine requires a certain setup time to process all the different jobs assigned to it. Hence, this setup time will be independent of sequence of jobs, but will depend only on the nature of machine. The optimization objective is to minimize the maximum tardiness criterion. To solve this optimization problem, an exact method, heuristics and metaheuristics are the three main resolution methods that we have used. The exact method is represented by the mixed integer linear programming (MILP) model. In terms of the second category of resolution methods, we have been focused on two methods, the first is a modified heuristic based on Johnson rule (HBJR) while the second is based on the Nawaz–Enscore–Ham (NEH) algorithm. Finally, three metaheuristics have been used, namely the iterated local search (ILS) method, the iterated greedy (IG) algorithm and the genetic algorithm (GA). Our numerical results indicate that for the problems with small size instances, the NEH heuristic outperforms HBJR approach, while for relatively large size instances, the developed IG algorithm gives best results than both other metaheuristics ILS and GA.

Minimizing Carbon Emission in Hybrid Flow Shop Scheduling: A Comparative Analysis of Flow-based and Set Partitioning Formulations

This article addresses the Hybrid Flow Shop Green Scheduling Problem with Sequence and Machine Dependent Setup Times (HFSGSP-SMDST) in the context of Industry 4.0. The objective is to minimize carbon emission while optimizing scheduling. Two mathematical formulations, a flow-based positional formulation (FB) and a set partitioning positional formulation (HCG), are proposed to provide tighter lower bounds. The FB formulation is solved using a commercial solver, while the HCG algorithm incorporates an Iterated Local Search (ILS) to generate initial solutions and an upper bound for the problem. Computational experiments demonstrate that HCG achieves better lower bounds, although it requires more time compared to FB. The proposed algorithms offer valuable solutions for carbon emission optimization and scheduling in Industry 4.0, with a trade-off between lower bounds and convergence speed.

Enhanced Global Optimization With Parallel Global and Local Structures for Real-Time Control Systems

In practice, objective functions of real-time control systems can have multiple local minimums or can dramatically change over the function space, making them hard to optimize. To efficiently optimize such systems, in this paper, we develop a parallel global optimization framework that combines direct search methods with parallel Bayesian optimization. It consists of an iterative global and local search that searches broadly through the entire global space for promising regions and then efficiently exploits each local promising region. We prove the asymptotic convergence properties of the proposed framework and conduct several numerical experiments to illustrate its empirical performance. We also provide a real-time control problem to illustrate the efficiency of our proposed algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by a collision avoidance problem of vessels aided with onboard agent-based simulations. The simulation on one vessel can predict potential conflicts with other vessels on a pre-defined trajectory. In heavy congestion regions, the environment is highly dynamic and thus it is difficult to find a much safer alternative trajectory if collision is predicted on the current one. Moreover, for such real-time decisions, the control system should be quick in response to improve safety. The proposed metamodel based algorithm is designed for quick decision in such highly dynamic systems. The algorithm employs a decomposition of the response surface to better handle the multi-modal surface resulting from the highly dynamic environment. Specifically, it first looks at the large-scale trend globally (filter out the many local fluctuations that may otherwise trap the algorithm) to locate potential promising regions and then proceeds to this local regions for more detailed local search. To make quick decisions, it uses fast direct search algorithms in the local search phase and applies a parallel search scheme to enjoy the abundant computing power. Both the theoretical analysis and the simulation studies demonstrate that the proposed algorithm can provide better decisions quickly. We also note that this algorithm is not limited to real-time control or simulation-based system. In the case where each run of the experiment is expensive and the budget is limited for the final decision and when the response function is multi-modal, this algorithm can hopefully become a quite efficient and competitive approach. The multi-modal responses have broad applications in the area of control, planning and operations research, such as robot navigating and reinforcement learning.

Assessing the In-house Logistics flows in the automotive industry

The Internal Logistics Routing Management (ILRM) system in a car-assembling company consists of a set of logistics activities that a company does to guarantee that the workstations in an assembly line do not run out of materials. In this work, we present and study one of these activities, the design of the supplying routes in SEAT S.A., a subsidiary company of the Volkswagen Group. The ILRM is an operational and strategical system because it impacts the company’s performance and involves the employment of both complex operational processes and systems. We evaluate the company’s current ILRM system using fixed routes and propose new scenarios to be implemented, in which variable-routing concepts are considered. Concerning the demand aspects, we evaluate three scenarios: the current demand, an automatic ordering system, and a forecasting demand obtained through a representation of a Manufacturing Resource Planning (MRP) system. To evaluate and optimize these scenarios, we propose an Integer Linear Programming (ILP) model and an Iterated Local Search (ILS) algorithm to calculate variable routes to ILRM systems. Furthermore, we present a simulation procedure to evaluate and compare all these scenarios in a realistic environment using real data from the company. From the optimization point of view, the ILS was able to reduce the total distance covered throughout the considered time horizon and generate no backorders. Finally, we describe the advantages and challenges of each scenario. This proposed methodology may be applied and extended to any manufacturing company.

Transmission Line Fault Diagnosis Method Based on SDA-ISSA-XGBoost under Meteorological Factors

Transmission lines are directly exposed to the natural environment and are prone to failure due to meteorological factors. A novel approach for diagnosing transmission line faults under various meteorological conditions has been introduced. This method, known as SDA-ISSA-XGBoost, combines the power of Stacked Denoising Autoencoder (SDA), an improved Sparrow Search Algorithm (ISSA) enhanced with chaotic mapping sequences, adaptive weights, improved iterative local search, and a random differential mutation strategy, and eXtreme Gradient Boosting (XGBoost). The process begins with SDA, which extracts essential features from the initial meteorological data. Subsequently, ISSA is applied to optimize the parameters of the XGBoost model. This results in the ISSA-XGBoost fault diagnosis model. The performance of this model is compared to PSO-XGBoost and SSA-XGBoost. The experimental findings demonstrate that the ISSA-XGBoost model achieves an impressive fault diagnosis accuracy of 94.39%, surpassing both PSO-XGBoost and SSA-XGBoost by 6.54% and 3.74%, respectively.

An online surrogate-assisted neighborhood search algorithm based on deep neural network for thermal layout optimization

Thermal layout optimization problems are common in integrated circuit design, where a large number of electronic components are placed on the layout, and a low temperature (i.e., high efficiency) is achieved by optimizing the positions of the electronic components. The operating temperature value of the layout is obtained by measuring the temperature field from the expensive simulation. Based on this, the thermal layout optimization problem can be viewed as an expensive combinatorial optimization problem. In order to reduce the evaluation cost, surrogate models have been widely used to replace the expensive simulations in the optimization process. However, facing the discrete decision space in thermal layout problems, generic surrogate models have large prediction errors, leading to a wrong guidance of the optimization direction. In this work, the layout scheme and its temperature field are represented by images whose relation can be well approximated by a deep neural network. Therefore, we propose an online deep surrogate-assisted optimization algorithm for thermal layout optimization. First, the iterative local search is developed to explore the discrete decision space to generate new layout schemes. Then, we design a deep neural network to build an image-to-image mapping model between the layout and the temperature field as the approximated evaluation. The operating temperature of the layout can be measured by the temperature field predicted by the mapping model. Finally, a segmented fusion model management strategy is proposed to online updates the parameters of the network. The experimental results on three kinds of layout datasets demonstrate the effectiveness of our proposed algorithm, especially when the required computational budget is limited.

The family capacitated vehicle routing problem

In this article, we address the family capacitated vehicle routing problem (F-CVRP), an NP-hard problem that generalizes both the FTSP and the capacitated vehicle routing problem. The F-CVRP has practical application in warehouse management in warehouses with scattered storage. We present several mixed integer linear programming formulations for this problem and establish a theoretical and empirical comparison. We also propose valid inequalities adapted from known routing problems from the literature. Some formulations are solved using a branch-and-cut algorithm, which is tested with a newly generated data set. The computational experiment allows us to identify the instances’ most challenging characteristics and the exact methods’ limitations. Finally, we develop an iterated local search (ILS) algorithm to efficiently obtain feasible solutions for the instances that could not be solved to proven optimality. The ILS algorithm is very efficient and can improve the upper bounds obtained by the exact methods within the set time limit.

Solving the Flying Sidekick Traveling Salesman Problem by a Simulated Annealing Heuristic

This study investigates the flying sidekick traveling salesman problem (FSTSP), in which a truck and an unmanned aerial vehicle work together to make deliveries. This study develops a revised mixed-integer linear programming (MILP) model for the FSTSP. The revised MILP model performs better than the existing model. Due to the FSTSP’s high complexity, we propose an effective heuristic based on simulated annealing (SA) to solve the problem. The novelty of the proposed SA heuristic lies in the new solution representation, which not only determines the visiting sequence of customers but also the service type of customers and rendezvous positions. Another feature of the proposed SA is a new operator specifically designed for the FSTSP. To evaluate the performance of the proposed SA heuristic, we conduct a comprehensive computational study where we fine-tune the parameters of the SA heuristic and compare the performance of the SA heuristic with several state-of-the-art algorithms including hybrid genetic algorithm (HGA) and iterated local search (ILS) in solving existing FSTSP benchmark instances. The results indicate that the proposed SA heuristic outperforms ILS and is statistically competitive with HGA. It obtains best-known solutions for all small FSTSP instances and 29 best-known solutions for the 60 large FSTSP instances, including 20 new best-known solutions.