Iterated Greedy Search Research Articles

Optimisation of multiple clustering based undersampling using artificial bee colony: Application to improved detection of obfuscated patterns without adversarial training

Attack detection is one of the main features required in modern defence systems. Despite the ongoing research, it remains challenging for a typical mechanism like network-based intrusion detection system (NIDS) to catch up with evolving adversarial attacks. They specifically aim to confuse a machine-learning based predictor. Without the knowledge of adversarial patterns, the best approach is generalising signatures learned from a dataset of legitimate connections and known intrusions. This work focuses on analysing non-payload traffics so that the resulting techniques can be exploited to a range of network-based applications. It investigates a novel means to deal with the problem of imbalanced classes. An optimised undersampling method is introduced to select a subset of majority-class representatives initially created through an ensemble clustering procedure. A weighted combination of criteria representing distributions within and between classes is proposed as the objective function for a global optimisation using the artificial bee colony (ABC). This approach usually outperforms its baselines and other state-of-the-art undersampling models, with ABC being more effective using the global best strategy than a random selection of solutions or an iterative greedy search. The paper also details the parameter analysis offering a heuristic guide for potential taking up of the proposed techniques.

How to improve efficiency for prefabricated component production scheduling with worker allocation and order outsourcing?

Prefabricated building is an advanced building mode that is actively promoted in China owing to its high-efficiency, energy-saving and labour-saving characteristics. However, because of the limited production capacity and production resources of the final assembly parts production plant, it is usually impossible for a factory to accept all orders. Therefore, the factory will consider outsourcing part of the orders to reduce delay penalties. In addition, the allocation of workers in the production process will significantly affect the processing time of orders. Regarding those two issues, a production scheduling integrated optimization model, considering worker allocation and order outsourcing, is established to maximize net profit. To address the complexity of problem solving caused by the NP-hard nature of this problem, a hybrid improved genetic–accelerated iterative greedy search algorithm is developed. Finally, a numerical example is delivered to verify that the proposed algorithm has improved solution quality and convergence.

A novel predictive–reactive scheduling method for parallel batch processor lot-sizing and scheduling with sequence-dependent setup time

In practical printed circuit boards (PCB) drilling production systems, multiple spindle processing capabilities and uncertainties are commonplace. This paper addresses predictive–reactive scheduling, considering parallel batch processor lot-sizing and scheduling with sequence-dependent setup times in a dynamic environment. We propose a predictive scheduling algorithm named S-AGAIG to balance machine spindle utilization and order tardiness in steps. Multiple splitting solutions are first formed by using a split heuristic algorithm to size the sub-lots of orders. Then the adaptive genetic algorithm with iterated greedy search is applied to select the solutions and scheduling. Furthermore, we present a schedule repair strategy based on sub-lot co-processing considering the impact of critical sub-lots, and construct a scheduling stability metric for rescheduling. In 36 sets of case experiments encompassing diverse load and machine type configurations, S-AGAIG showcases a 21.27% enhancement in average tardiness performance when compared to its closest rival. Across 28 cases involving varying disturbances, the framework demonstrates exceptional robustness.

A bi-criteria approach to the truck-multidrone routing problem

In the rapidly expanding field of e-commerce logistics, the optimisation of last-mile delivery solutions is paramount. This paper introduces a novel methodology that addresses this challenge by generating approximate Pareto fronts in a hybrid truck-drone delivery system. Specifically, we examine a generalized single truck multi-drone problem that allows multi-visit flight missions and rendezvous points distinct from launch locations. Our goal is providing decision-makers with a portfolio of optimal routing solutions that balance service time and environmental impact, criteria that are increasingly shaping decision-making in this domain. To achieve this, we introduce a bivector coding scheme inspired by flow-shop scheduling problems and implement a Simulated Annealing algorithm. This algorithm features an advanced stopping mechanism, negating the need for manual adjustments by utilizing a distinctive blend of a domination rate and a Kalman filter. Importantly, our framework employs an iterated greedy search algorithm to evolve from initial solutions towards identifying non-dominated solutions sets, which are then ranked using a hypervolume coefficient. To validate our methodology, we conduct a sensitivity analysis on two different size instances using a full factorial design of experiments. Our analysis reveals crucial insights into the impact of the number of drones, their autonomy, and their flight speed settings. From it, we conclude that it is a robust and adaptable framework for its practical application for obtaining Pareto fronts solutions among which picking the ultimate routing to be implemented.

Feasibility Prediction for Rapid IC Design Space Exploration

The DARPA POSH program echoes with the research community and identifies that engineering productivity has fallen behind Moore’s law, resulting in the prohibitive increase in IC design cost at leading technology nodes. The primary reason is that it requires many computing resources, expensive tools, and even many days to complete a design implementation. However, at the end of this process, some designs could not meet the design constraints and become unroutable, creating a vicious circuit design cycle. As a result, designers have to re-run the whole process after design modification. This research applied a machine learning approach to automatically identify design constraints and design rule checking (DRC) violation issues and help the designer identify design constraints with optimal DRCs before the long detailed routing process through iterative greedy search. The proposed algorithm achieved up to 99.99% design constraint prediction accuracy and reduced 98.4% DRC violations with only a 6.9% area penalty.

Solving two-stage stochastic route-planning problem in milliseconds via end-to-end deep learning

With the rapid development of e-economy, ordering via online food delivery platforms has become prevalent in recent years. Nevertheless, the platforms are facing lots of challenges such as time-limitation and uncertainty. This paper addresses a complex stochastic online route-planning problem (SORPP) which is mathematically formulated as a two-stage stochastic programming model. To meet the immediacy requirement of online fashion, an end-to-end deep learning model is designed which is composed of an encoder and a decoder. To embed different problem-specific features, different network layers are adopted in the encoder; to extract the implicit relationship, the probability mass functions of stochastic food preparation time is processed by a convolution neural network layer; to provide global information, the location map and rider features are handled by the factorization-machine (FM) and deep FM layers, respectively; to screen out valuable information, the order features are embedded by attention layers. In the decoder, the permutation sequence is predicted by long-short term memory cells with attention and masking mechanism. To learn the policy for finding optimal permutation under complex constraints of the SORPP, the model is trained in a supervised learning way with the labels obtained by iterated greedy search algorithm. Extensive experiments are conducted based on real-world data sets. The comparative results show that the proposed model is more efficient than meta-heuristics and is able to yield higher quality solutions than heuristics. This work provides an intelligent optimization technique for complex online food delivery system.

Iterated greedy algorithms enhanced by hyper-heuristic based learning for hybrid flexible flowshop scheduling problem with sequence dependent setup times: A case study at a manufacturing plant

Metaheuristic algorithms offer unique opportunities in problem solving. Although they do not guarantee optimality, it has been shown by numerous publications that they can achieve excellent results in acceptable time. Particularly in real-life production systems, which are mostly comprised of complex discrete optimization problems, the merit should be finding appropriate and efficient solutions in shorter periods rather than waiting for the optimum solution in whole shift. Accordingly, this paper presents a learning iterated greedy search metaheuristic to minimize the maximum completion time in a hybrid flexible flowshop problem with sequence dependent setup times encountered at a manufacturing plant. The proposed algorithm is comprised of four main phases. The first phase employs NEH heuristic to generate an initial solution. Additionally, in order to introduce diversity, some replications are occasionally allowed to start with random solutions. Destruction mechanism to perturb the current solution is used in the next phase. It is followed by a construction procedure, which is used to repair the partial solution obtained after destruction. Finally, a descent neighborhood search enhanced by a hyper-heuristic based learning is applied to the repaired solution in the fourth phase. Thus, algorithm adaptively learns and promotes the most efficient low-level heuristic out of a heuristics pool and encourages the metaheuristic algorithm in using the promoted low-level heuristic in the final phase. The proposed algorithm along with its several extensions is tested by using real data taken from the mentioned production system. Next, by making use of the same data, the developed algorithms are also compared to eight different algorithms, which are shown to be promising in the related literature. Finally, appropriate statistical tests are applied to demonstrate possible significant improvements among all tested algorithms.

Branch, bound and remember algorithm for two-sided assembly line balancing problem

This research presents a new branch, bound and remember (BBR) algorithm to minimize the number of mated-stations in two-sided assembly lines. The proposed methodology modifies the Hoffman heuristic to achieve high-quality upper bounds, and employs two new dominance rules, referred to as memory-based maximal load rule and memory-based extended Jackson rule, to prune the sub-problems. The BBR algorithm also employs several other improvements to enhance the performance, including renumbering the tasks and new lower bounds. Computational results demonstrate that BBR achieves the optimal solutions for all the tested instances within 1.0 s on average, including two optimal solutions for the first time. Comparative study shows that BBR outperforms the current best exact method (branch and bound algorithm) and the current best heuristic algorithm (iterated greedy search algorithm). As a consequence, the proposed BBR can be regarded as the state-of-the-art method for TALBP.

Analysis of variable neighborhood descent as a local search operator for total weighted tardiness problem on unrelated parallel machines

Variable Neighborhood Descent (VND) is a metaheuristic commonly used as a local search operator of other metaheuristics. This work analyzes the hypothesis in which the substitution of the local search operator by VND may increase the performance of three metaheuristics (Iterated Greedy Search - IGS, Artificial Bee Colony - ABC, and Genetic Algorithm - GA), proposed in the literature for the solution of minimization problems of the total weighted tardiness in Unrelated Parallel Machines environments. For the validation of this hypothesis, six neighborhood structures are proposed, considering the characteristics of the problem for reducing the search space exploitation. The analysis is carried out considering three VND variations, two neighborhood structures exploitation order, as well as exploitation by the First Improvement and Best Improvement methods. The Taguchi Robust Parameter method is used to design a specific configuration of the VND for each metaheuristic. Additionally, some experiments to analyze the contribution of each neighborhood structure for the convergence of the metaheuristics are performed. The results show that three neighborhood structures, act together, and domain the convergence influence of the local search. The results also show that the use of VND as a local search operator in place of those original local search increases the performance of all metaheuristics evaluated. Moreover, the results achieved by the metaheuristics integrated to the VND in the most evaluated scenarios have become equivalent or better, on average, than state-of-the-art approaches.

Learning ensembles of priority rules for online scheduling by hybrid evolutionary algorithms

This paper studies the computation of ensembles of priority rules for the One Machine Scheduling Problem with variable capacity and total tardiness minimization. Concretely, we address the problem of building optimal ensembles of priority rules, starting from a pool of rules evolved by a Genetic Programming approach. Building on earlier work, we propose a number of new algorithms. These include an iterated greedy search method, a local search algorithm and a memetic algorithm. Experimental results show the potential of the proposed approaches.

Minimizing flowtime in a flowshop scheduling problem with a biased random-key genetic algorithm

In this paper, we advance the state of the art for solving the Permutation Flowshop Scheduling Problem with total flowtime minimization. For this purpose, we propose a Biased Random-Key Genetic Algorithm (BRKGA) introducing on it a new feature called shaking. With the shaking, instead to full reset the population to escape from local optima, the shaking procedure perturbs all individuals from the elite set and resets the remaining population. We compare results for the standard and the shaking BRKGA with results from the Iterated Greedy Search, the Iterated Local Search, and a commercial mixed integer programming solver, in 120 traditional instances. For all algorithms, we use warm start solutions produced by the state-of-the-art Beam-Search procedure. Computational experiments show the efficiency of proposed BRKGA, in addition to identify lower and upper bounds, as well as some optimal values, among the solutions.

Green permutation flowshop scheduling problem with sequence-dependent setup times: a case study

Increasing global energy consumption, large variations in its cost and the environmental degradation effects are good reasons for the manufacturing industries to become greener. Green shop floor scheduling is increasingly becoming a vital factor in the sustainable manufacturing. In this paper, a green permutation flowshop scheduling problem with sequence-dependent setup times is studied. Two objectives are considered including minimisation of makespan as a measure of service level and minimisation of total energy consumption as a measure of environmental sustainability. We extend a bi-objective mixed-integer linear programming model to formulate the stated problem. We develop a constructive heuristic algorithm to solve the model. The constructive heuristic algorithm includes iterated greedy (CHIG) and local search (CHLS) algorithms. We develop an efficient energy-saving method which decreases energy consumption, on average, by about 15%. To evaluate the effectiveness of the constructive heuristic algorithm, we compare it with the famous augmented ϵ-constraint method using various small-sized and large-sized problems. The results confirm that the heuristic algorithm obtains high-quality non-dominated solutions in comparison with the augmented ϵ-constraint method. Also, they show that the CHIG outperforms the CHLS. Finally, this paper follows a case-study, with in-depth analysis of the model and the constructive heuristic algorithm.

Heuristics for a flowshop scheduling problem with stepwise job objective function

In this work, we introduce the Flowshop Scheduling Problem with Delivery Dates and Cumulative Payoffs. This problem is a variation of the flowshop scheduling problem with job release dates that maximizes the total payoff with a stepwise job objective function. This paper contributes towards proposing a mathematical formulation for this new problem and an original constructive heuristic. Additionally, we develop a new benchmark of 300 hard instances which are available in a public repository. Besides, we provide primal and dual bounds for them. Extensive computational experiments were carried out taking into account classical constructive heuristics and hybrid local searches. Results show the merit of the FF heuristic when compared to other classical heuristics for flowshop scheduling problems. Additionally, we compare an Iterated Local Search (ILS), an Iterated Greedy Search (IGS), and a Biased Random-Key Genetic Algorithm (BRKGA), both customized for the studied problem, and a commercial mixed integer programming solver. The comparison between these methods showed that the BRKGA starting with a solution from FF heuristic is able to find the best solutions in a very short period of time. Iterated local Search and the commercial solver presented significantly worse results than the aforementioned methods.

A hybrid estimation of distribution algorithm for unrelated parallel machine scheduling with sequence-dependent setup times

A hybrid estimation of distribution algorithm (EDA) with iterated greedy (IG) search (EDA-IG) is proposed for solving the unrelated parallel machine scheduling problem with sequence-dependent setup times (UPMSP-SDST). For makespan criterion, some properties about neighborhood search operators to avoid invalid search are derived. A probability model based on neighbor relations of jobs is built in the EDA-based exploration phase to generate new solutions by sampling the promising search region. Two types of deconstruction and reconstruction as well as an IG search are designed in the IG-based exploitation phase. Computational complexity of the algorithm is analyzed, and the effect of parameters is investigated by using the Taguchi method of design-of-experiment. Numerical tests on 1640 benchmark instances are carried out. The results and comparisons demonstrate the effectiveness of the EDA-IG. Especially, the bestknown solutions of 531 instances are updated. In addition, the effectiveness of the properties is also demonstrated by numerical comparisons.

Tabu-enhanced iterated greedy algorithm: A case study in the quadratic multiple knapsack problem

Iterated greedy search is a simple and effective metaheuristic for combinatorial problems. Its flexibility enables the incorporation of components from other metaheuristics with the aim of obtaining effective and powerful hybrid approaches. We propose a tabu-enhanced destruction mechanism for iterated greedy search that records the last removed objects and avoids removing them again in subsequent iterations. The aim is to provide a more diversified and successful search process with regards to the standard destruction mechanism, which selects the solution components for removal completely at random.We have considered the quadratic multiple knapsack problem as the application domain, for which we also propose a novel local search procedure, and have developed experiments in order to assess the benefits of the proposal. The results show that the tabu-enhanced iterated greedy approach, in conjunction with the new local search operator, effectively exploits the problem-knowledge associated with the requirements of the problem considered, attaining competitive results with regard to the corresponding state-of-the-art algorithms.

The r-interdiction median problem with probabilistic protection and its solution algorithm

This paper discusses the critical infrastructure protection problem in supply systems regarding potential intentional attacks. Considering that the protection of a facility cannot be always successful, we present the r-interdiction median problem with probabilistic protection. Defensive resources are allocated according to the degree of importance of service facilities. Computational experiments demonstrate the benefits brought by centralizing resources to a few critical sites, as well as the importance of introducing probabilistic factors. Furthermore, we discuss the problem in a scenario of multiple interdictors. It is found that the worst-case interdictions made by multiple interdictors may cause much more serious system impairment than a single interdictor. Such losses can sometimes be effectively alleviated by adjustment of fortification plans. To solve the problem, we propose an iterated greedy search method which produces good approximations to optimal fortification strategies rather fast, even for instances of considerable size.

Projection-Based and Look-Ahead Strategies for Atom Selection

In this paper, we improve iterative greedy search algorithms in which atoms are selected serially over iterations, i.e., one-by-one over iterations. For serial atom selection, we devise two new schemes to select an atom from a set of potential atoms in each iteration. The two new schemes lead to two new algorithms. For both the algorithms, in each iteration, the set of potential atoms is found using a standard matched filter. In case of the first scheme, we propose an orthogonal projection strategy that selects an atom from the set of potential atoms. Then, for the second scheme, we propose a look ahead strategy such that the selection of an atom in the current iteration has an effect on the future iterations. The use of look ahead strategy requires a higher computational resource. To achieve a trade-off between performance and complexity, we use the two new schemes in cascade and develop a third new algorithm. Through experimental evaluations, we compare the proposed algorithms with existing greedy search and convex relaxation algorithms.

Iterated greedy local search methods for unrelated parallel machine scheduling

This work deals with the parallel machine scheduling problem which consists in the assignment of n jobs on m parallel machines. The most general variant of this problem is when the processing time depends on the machine to which each job is assigned to. This case is known as the unrelated parallel machine problem. Similarly to most of the literature, this paper deals with the minimization of the maximum completion time of the jobs, commonly referred to as makespan ( C max ) . Many algorithms and methods have been proposed for this hard combinatorial problem, including several highly sophisticated procedures. By contrast, in this paper we propose a set of simple iterated greedy local search based metaheuristics that produce solutions of very good quality in a very short amount of time. Extensive computational campaigns show that these solutions are, most of the time, better than the current state-of-the-art methodologies by a statistically significant margin.

Multiple Fundamental Frequency Estimation by Modeling Spectral Peaks and Non-Peak Regions

This paper presents a maximum-likelihood approach to multiple fundamental frequency (F0) estimation for a mixture of harmonic sound sources, where the power spectrum of a time frame is the observation and the F0s are the parameters to be estimated. When defining the likelihood model, the proposed method models both spectral peaks and non-peak regions (frequencies further than a musical quarter tone from all observed peaks). It is shown that the peak likelihood and the non-peak region likelihood act as a complementary pair. The former helps find F0s that have harmonics that explain peaks, while the latter helps avoid F0s that have harmonics in non-peak regions. Parameters of these models are learned from monophonic and polyphonic training data. This paper proposes an iterative greedy search strategy to estimate F0s one by one, to avoid the combinatorial problem of concurrent F0 estimation. It also proposes a polyphony estimation method to terminate the iterative process. Finally, this paper proposes a postprocessing method to refine polyphony and F0 estimates using neighboring frames. This paper also analyzes the relative contributions of different components of the proposed method. It is shown that the refinement component eliminates many inconsistent estimation errors. Evaluations are done on ten recorded four-part J. S. Bach chorales. Results show that the proposed method shows superior F0 estimation and polyphony estimation compared to two state-of-the-art algorithms.

A multi-objective iterated greedy search for flowshop scheduling with makespan and flowtime criteria

In this paper, we tackle the problem of total flowtime and makespan minimisation in a permutation flowshop. For this, we introduce a multi-criteria iterated greedy search algorithm. This algorithm iterates over a multicriteria constructive heuristic approach to yield a set of Pareto-efficient solutions (a posteriori approach). The proposed algorithm is compared against the best-so-far heuristic for the problem under consideration. The comparison shows the proposal to be very efficient for a wide number of multicriteria performance measures. Aside, an extensive computational experience is carried out in order to analyse the different parameters of the algorithm. The analysis shows the algorithm to be robust for most of the considered performance measures.