Neighborhood Search Technique Research Articles

Maximizing Nash Social Welfare Based on Greedy Algorithm and Estimation of Distribution Algorithm.

The Nash social welfare (NSW) problem is relevant not only to the economic domain but also extends its applicability to the field of computer science. However, maximizing Nash social welfare is an APX-hard problem. In this study, we propose two approaches to enhance the maximization of Nash social welfare. First, a general greedy algorithm (GA) capable of addressing the Nash social welfare problem for both agents with identical and differing valuations was presented. It is proven that the proposed algorithm aligns with the previous greedy algorithm when all agents possess identical valuations. Second, an innovative method for solving the Nash social welfare problems using evolutionary algorithms was developed. This approach integrates the Estimation of Distribution Algorithms (EDAs) with neighborhood search techniques to improve the maximization process of Nash social welfare. Finally, the proposed algorithms were implemented across a range of instances with the objective of maximizing Nash social welfare. The experimental results indicate that the approximation solutions derived from the Estimation of Distribution Algorithm outperform those obtained via the greedy algorithm.

Efficient Truss Design: A Hybrid Geometric Mean Optimizer for Better Performance

The emphasis on sustainable development is increasingly recognized as a global imperative, leading to significant transformations in methods and technologies within the construction industry. Specifically, the optimization of the mass of truss structures is aimed at enhancing sustainable construction efforts. Such optimization is crucial for reducing dependence on natural resources and contributing to a decrease in CO2 emissions from the production and transport of construction materials. This study presents an innovative approach to the truss design challenge using a hybrid geometric mean optimizer (hGMO). The geometric mean optimizer (GMO) faces challenges in effectively exploring the solution space and avoiding local optima. To address these issues, GMO has been integrated with the variable neighborhood search (VNS) technique, thereby enhancing its exploration capabilities. The effectiveness of the hGMO model has been evaluated through four distinct truss design scenarios: 10‐bar, 52‐bar, 72‐bar, and 200‐bar structures. The results demonstrate that hGMO outperforms traditional methods, achieving optimal weights of 5060.915 lb, 1902.605 kg, 389.334 lb, and 25453.62 lb, respectively. These findings confirm that hGMO is a crucial tool in advancing sustainable construction practices by focusing on the strategic optimization of truss structure mass.

Adaptive Multi-Objective Algorithm for the Sustainable Electric Vehicle Routing Problem in Medical Waste Management

This paper addresses the complex issue of managing medical waste transportation using electric vehicles, with the goal of minimizing both energy consumption and the risks associated with hazardous waste. A multi-objective mixed-integer linear programming model is introduced, incorporating practical factors such as time windows, partial recharge policy, load-dependent discharge, infection risk, and trips to waste disposal facilities. Our proposed method, a combination of the multi-objective evolutionary algorithm using decomposition (MOEA/D) with adaptive large neighborhood search (ALNS) and local search (LS) techniques, is referred to as MOEA/D-ALNS. This method demonstrates superior performance compared with the non-dominated sorting genetic algorithm, NSGA-II, modified MOEA/D and MOEA/D-LNS in benchmark instances with realistic assumptions. Our experimental results revealed an inverse correlation between energy consumption and risk objectives. Sensitivity analyses showed that eliminating time-window constraints results in more energy-efficient and safer routes while maintaining a slightly lower battery energy level can strike an ideal balance between energy consumption, risk, and battery health. This research contributes to the understanding of infectious medical waste management with its consideration of electric vehicles and waste disposal. It lays a solid foundation for future studies aiming to improve the sustainability and efficiency of medical waste routing practices.

Scatter search for capacitated minimum spanning tree

We develop a scatter search algorithm for the classical capacitated minimum spanning tree (CMST) problem. We address both the homogeneous and the more difficult heterogeneous variant of the problem. The CMST is central in a wide range of network design applications in industrial engineering, routing and logistics, and communication networks. Since the problem is NP-Complete, heuristic solution methods are often required to find high-quality solutions within practical time limits. Research efforts have been focused on complex algorithm designs combining advanced neighborhood search techniques with exact solution methods or hybrids of genetic algorithms with various alternative search techniques and likewise supplemented with optimization procedures. In this study we go back to the roots and offer an alternative algorithm that competes with the best algorithms in the literature and both avoids complicated artifacts and is simpler to implement. Key features of the proposed algorithm include an oscillation neighborhood search method characterized by two classes of neighborhoods and an evolutionary search procedure based on a scatter search framework.

Scheduling aerial resource operations for the extinction of large-scale wildfires

The significant increase in large-scale wildfire events in recent decades, caused primarily by climate change, has resulted in a growing number of aerial resources being used in suppression efforts. Present-day management lacks efficient and scalable algorithms for complex aerial resource allocation and scheduling for the extinction of such fires, which is crucial to ensuring safety while maximizing the efficiency of operations. In this work, we present a Mixed Integer Linear Programming (MILP) optimization model tailored to large-scale wildfires for the daily scheduling of aerial operations. The main objective is to achieve a prioritized target water flow over all areas of operation and all time periods. Minimal target completion across individual areas and time periods and total water output are also maximized as secondary and ternary objectives, respectively. An efficient and scalable multi-start heuristic, combining a randomized greedy approach with simulated annealing employing large neighborhood search techniques, is proposed for larger instances. A diverse set of problem instances is generated with varying sizes and extinction strategies to test the approaches. Results indicate that the heuristic can achieve (near)-optimal solutions for smaller instances solvable by the MILP, and gives solutions approaching target water flows for larger problem sizes. The algorithm is parallelizable and has been shown to give promising results in a small number of iterations, making it applicable for both night-before planning and, more time-sensitive, early-morning scheduling.

An Iterative Three-Stage Neighborhood Search for Solving Precedence Constrained Agricultural Land Investment Problem

The use of neighborhood search techniques to address a practical issue faced by agricultural investors is examined in this study. The problem is named as agricultural land investment problem with precedence constraints and it has an essential impact on agriculture issues. The tackled problem can be viewed as a variant of the well-known classical 0-1 knapsack problem where precedence constraints are imposed on pairs of items. Precedence constraints take into account a precedence relation between items. This paper first simulates the considered problem as precedence constraints knapsack problem and presents a mathematical representation model. Then, an iterative three-stage neighborhood search method is proposed for optimizing the problem. The proposed method consists of three stages. First stage applies a greedy procedure in order to construct a feasible solution. Second stage applies local search procedures in order to enhance the quality of the solutions at hand. Third and last, in order to broaden the search space, a random neighborhood destruction approach is introduced. Finally, the effectiveness of the suggested approach is assessed and contrasted with the outcomes obtained by greedy and local search techniques. The presented method is competitive and efficient since it produces excellent solutions in a reasonable amount of time.

A generic optimization framework for scheduling problems under machine deterioration and maintenance activities

This paper introduces a novel generic optimization framework for scheduling problems with machine deterioration and maintenance activities. The framework employs computationally efficient and robust data structures for schedules along with templates for implementing optimization algorithms making it applicable within Industry 4.0. The concept includes, but not limited to, the optimization objectives such as the maximum completion time, the maximum lateness (or tardiness), the total (weighted) number of late jobs, the total (weighted) completion times, the total (weighted) tardiness, the just-in-time. A set of example algorithms based on iterative local search, i.e. Nawaz–Enscore–Ham’s method (NEH), simulated annealing, genetic algorithm, scatter search algorithm, artificial bee colony were embedded within the framework to demonstrate its robustness. The theoretical and experimental analysis presented in the paper proved the high efficiency of the proposed approach; it traverses areas containing optimal solutions and at the same time it is characterized by a low computational complexity of related procedures to calculate criterion values or to generate new solutions. Furthermore, if the optimization methods are enhanced by the proposed data structures and procedures, their standard computational complexity is not increased even for complex scheduling problems including maintenance activities. The proposed framework modular architecture enables further integration with new algorithms aiming at improvement of neighbourhood search techniques. Its robustness, efficiency, and modularity can bring advantages in various decision support systems.

Connectivity Analysis of WSN Nodes using Neighborhood Search Technique (WSNNST)

A new approach to wireless sensor network (WSN) communication through multi-hop routing is investigated in this work. The proposed approach utilizes search radius expansion in order to find highest energy nodes within a WSN network and preserves the energy of the lowest energy nodes. This approach which is built on trust in terms of providing good quality communication channels between a source node and a destination node considers energy as one parameter of trust that will enable strong, reliable communication channels and shorter routes to be used in a WSN. This approach enables efficient energy consumption during data communication, but with higher level energy consumption due to expanded search radius, which can be minimized using search time limit. The work also relates through mathematical equations, number of discovered routes, maximum hops, and search time to the considered search radius. The overall objective of this work is to enable faster and efficient channel communication between WSN nodes, through gradual cost effective search in neighborhoods. This is achieved by uncovering shorter and less number of routes within an acceptable time limits. The proposed and simulated approach presented in this work differ in terms of the gradual expansion of search and the equal increments in search radius compared to other used techniques. The presented work also adds a unique feature of allowing re-charging of low energy nodes using the already established routes connecting high energy nodes through the registration of weak nodes locations. A final general expression relates search radius to other WSN variables is also presented.

Meta-Hashing for Remote Sensing Image Retrieval

With the explosive growth of the volume and resolution of high-resolution remote-sensing (HRRS) images, the management of them becomes a challenging task. The traditional content-based remote-sensing image retrieval (CBRSIR) technologies cannot meet what we expect due to the large volume of image archives and complex contents within HRRS images. As a successful approximate nearest neighborhood (ANN) search technique, Hash learning has received wide attention, especially when deep convolutional neural networks (DCNNs) appear. Due to DCNNs&#x2019; strong capacity of feature learning, many DCNN-based hashing methods have been proposed and achieved good performance for large-scale CBRSIR tasks. Nevertheless, their limitation is that a large of labeled training samples should be collected for training the deep models. To overcome this limitation, this article, therefore, develops a new supervised hash learning method for the large-scale HRRS CBRSIR task based on meta-learning, which could achieve well-retrieval performance with a few labeled training samples. First, taking the characteristics of HRRS into account, we develop a self-adaptive convolution (SAP-Conv) block and design a hashing net based on the block. SAP-Conv can learn robust features from HRRS images by exploring their multiscale information. Second, to enhance the generalization of the hashing net under a few labeled training samples, the hash learning is formulated in a meta-way, and we name it meta-hashing. Meta-hashing can effectively preserve the similarities between support and query set, and the similarities between samples within support set by the developed loss function. To further improve the performance of meta-hashing, we expand it to a dynamic version named dynamic-meta-hashing, in which the numbers of support and query are changeable in the training phase. Experimental results counted on the three widely used HRRS datasets demonstrate our dynamic-meta-hashing and meta-hashing can achieve promising performance in large-scale HRRS CBRSIR tasks based on a few training samples. Our source codes are available at <uri>https://github.com/TangXu-Group/Meta-hashing</uri>.

A hybrid moth flame optimization and variable neighbourhood search technique for optimal design of IIR filters

A hybrid moth flame optimization and variable neighbourhood search technique for optimal design of IIR filters

Artificial bee colony algorithm based on multiple neighborhood topologies

In recent years, as an effective global optimization technique, artificial bee colony (ABC) algorithm has attracted increasing attention for its good performance yet easy implementation. However, the solution search equation of ABC emphasizes exploration over exploitation, which greatly affects the convergence speed and accuracy of ABC. To solve this problem, many ABC variants have been developed to enhance exploitation by using the superior individuals. However, the concept of neighborhood topology has rarely been considered, which has significant effect on the dissemination of search information among individuals. Hence, in this work, we propose a new ABC variant based on multiple neighborhood topologies (named ABC-MNT). In ABC-MNT, three kinds of neighborhood topologies are used for different individuals to perform diverse abilities of disseminating search information, contributing to a better balance between exploration and exploitation. Considering the characteristics of different neighborhood topologies, three modified solution search equations are assigned to the three neighborhood topologies, respectively, to generate offspring. Furthermore, to preserve search experience of the scout bee phase, the global neighborhood search and opposition-based learning techniques are used. Extensive experiments are carried out on two widely used test suites, and eight well-established ABC variants and five other evolutionary algorithms are included in the performance comparison. The comparison results verify that ABC-MNT can obtain better or at least comparable performance on most of the benchmark functions.

An Effective Decomposition-Based Stochastic Algorithm for Solving the Permutation Flow-Shop Scheduling Problem

This paper presents an effective stochastic algorithm that embeds a large neighborhood decomposition technique into a variable neighborhood search for solving the permutation flow-shop scheduling problem. The algorithm first constructs a permutation as a seed using a recursive application of the extended two-machine problem. In this method, the jobs are recursively decomposed into two separate groups, and, for each group, an optimal permutation is calculated based on the extended two-machine problem. Then the overall permutation, which is obtained by integrating the sub-solutions, is improved through the application of a variable neighborhood search technique. The same as the first technique, this one is also based on the decomposition paradigm and can find an optimal arrangement for a subset of jobs. In the employed large neighborhood search, the concept of the critical path has been used to help the decomposition process avoid unfruitful computation and arrange only promising contiguous parts of the permutation. In this fashion, the algorithm leaves those parts of the permutation which already have high-quality arrangements and concentrates on modifying other parts. The results of computational experiments on the benchmark instances indicate the procedure works effectively, demonstrating that solutions, in a very short distance of the best-known solutions, are calculated within seconds on a typical personal computer. In terms of the required running time to reach a high-quality solution, the procedure outperforms some well-known metaheuristic algorithms in the literature.

Mixed integer linear programming model and an effective algorithm for the bi-objective double-floor corridor allocation problem

The bi-objective double-floor corridor allocation problem (bDFCAP) investigated here explores the effective placement of given departments in a double-floor space to minimise the overall flow cost and the corridor length objectives. Within each floor, departments are arranged in two parallel rows on opposite sides along a central corridor without overlapping. In this study, the bDFCAP is formulated as a mixed integer linear programming model, which has improved the performance over the previous one. Thereafter, a genetic algorithm with a variable neighbourhood search technique is designed and employed to solve the bDFCAP in a more effective manner. This technique is utilized to improve the local search capability by adaptively transforming between a deep-searching strategy and broad-searching strategy, and the superior performance of the proposed method is proven through comparisons with two other algorithms in current literature. Besides, the state-of-the-art lower bounds of several benchmark instances are updated.

Protein complex prediction in interaction network based on network motif

The enormous size of Protein–Protein Interaction (PPI) networks demands efficient computational methods to extract biologically significant protein complexes. A wide variety of algorithms have been proposed to predict protein complexes from PPI networks. However, it is still a challenging task to detect protein complexes with high accuracy and manageable sensitivity. In this manuscript, a novel complex prediction algorithm based on Network Motif (CPNM) is proposed. This algorithm addresses the role of proteins in the embeddings of network motif. These roles are used to define feature vectors and feature weights of proteins. Based on these features, a neighborhood search technique predict the protein complexes that consider both the inherent organization of proteins as well as the dense regions in PPI networks. The performance of the proposed algorithm is evaluated using various evaluation metrics like Precision, Recall, F-measure, Sensitivity, PPV, and Accuracy. The research finding indicates that the proposed algorithm outperforms most of the competing algorithms like MCODE, DPClus, RNSC, COACH, ClusterONE, CMC and PROCODE over the PPI network of Saccharomyces cerevisiae and Homo sapiens.

Three-Step Metaheuristic for the Multiple Objective Multiple Traveling Salesmen Problem

In this article, the multiple objective multiple Traveling Salesman Problem is considered. m salesmen have to visit n cities to perform some tasks each taking a given processing time. Two objectives are considered: balance the working loads of different salesmen and minimize their total traveled distance. To solve this NP-hard problem, a 3-phase metaheuristic was developed. In the first 2 phases, the principle of center of mass and a neighborhood search technique are used to assign the n cities to the m salesmen. In the third phase, a TSP solver was used to generate an optimal tour to every salesman using its assigned cities in phase 2. The metaheuristic was tested using TSP benchmarks of different sizes. The obtained results showed almost optimal load balancing for all tested instances and optimal tours in term of total traveled distances. A conducted comparison study showed that the proposed metaheuristic outperforms a recently published clustering algorithm for the workload objective.

Finding the Maximum Multi Improvement on neighborhood exploration

Neighborhood search techniques are often employed to deal with combinatorial optimization problems. Previous works got good results in applying a novel neighborhood search methodology called Multi Improvement (MI). First and best improvement are classical approaches for neighborhood exploration, while the MI has emerged due to the advance of new parallel computing technologies. The MI formalizes the concept of heuristic and exact exploration of independent moves for a given neighborhood structure, however, the advantages of an application of MI face the difficulty to select a great set of independent moves (which can be performed simultaneously). Most of the existing implementations of MI select these moves through heuristic methods, while others have succeeded in implementing exact dynamic programming approaches. In this paper, we propose a formal description for the Maximum Multi Improvement Problem (MMIP), as a theoretical background for the MI. Moreover, we develop three dynamic programming algorithms for solving the MMIP, given a solution tour for a Traveling Salesman Problem and neighborhood operators 2-Opt, 3-Opt, and OrOpt-k. The analysis suggests the rise of a new open topic focused on developing novel efficient neighborhood searches.

Estimating the Efficient Parameter Values of Different Neighborhood Search Techniques of Simulated Annealing in Forest Spatial Planning Problems

The performances of heuristic algorithms are highly dependent on the parameters used, and usually difficult to determine subjectively. Thus, how to balance the relations between the qualities of the solutions and the values of the parameters has been a hot and lasting topic in the field of optimization research. This article presented a statistical method to estimate the efficient parameter values of three alternative neighborhood search techniques of simulated annealing when applied to forest spatial harvest scheduling problems, as an example. The neighborhood search techniques included: the conventional version of simulated annealing (Method1), and the swapping version (Method2) and the changing version (Method3) of 2-element optimization (2-opt) moves. Results indicated that the performances of different neighborhood search strategies highly depended on the problem size, in which the superiorities of Method2 increased from about 10% for smaller cases (400 units) to approximately 80% of larger cases (>3600 units) when compared the objective function values with Method1 and Method3. The efficient parameter values of the cooling rate (CR), the number of iterations per temperature (Ntem), and the number of iterations used for generating initial solution (Nsol) could be estimated using polynomial functions with the number of units, while the initial temperature (IT) should be estimated using exponential function, where the determination coefficient ($R^{2}$ ) of the fitted functions were all larger than 0.60 [except for Nsol and CR of Mehod1 ($R^{2}=0.32$ and 0.42), CR of Method2 ($R^{2}=0.20$ )]. The number of satisfactory solutions all increased linearly ($R^{2} > 0.85$ ) with the number of units, while the solution efficiency decreased linearly ($R^{2} > 0.30$ ). The verifications of two extra grid datasets indicated that the parameter optimization methods were valid.

A hybrid GRASP for solving the bi-objective orienteering problem

This paper focuses on the bi-objective orienteering problem (BOOP) that arises in the tourist routes design problem in cities. In this multi-objective extension of the well-known orienteering problem (OP), each point of interest has different profits, which could reflect the multiple preferences of tourists. The aim is to find routes, limited in travel time, that visit some points of interest and provide the maximum of the different total collected profits. In order to determine an effective approximation of the Pareto optimal solutions, we propose a hybrid greedy randomised adaptive search procedure (GRASP) in which a general variable neighbourhood search (GVNS) is used as an improvement phase. To evaluate the performance of the proposed approach compared to the Pareto variable neighbourhood search (P-VNS) technique, we have used the test instances and the results provided by the P-VNS taken from the literature. Computational results reveal that the hybrid GRASP algorithm generates better approximations of Pareto-optimal solutions compared to the P-VNS method.

Modeling and solving a bi-objective airport slot scheduling problem

The strategic airport slot allocation problem concerns the scheduling of airlines’ requests for landings and take-offs at congested airports for a series of days within a given scheduling season. Relevant scheduling models dealing with the strategic airport slot allocation problem have employed various combinations of the total schedule displacement criterion with several variations of acceptability metrics. However, most variations of schedule displacement pursued in existing literature do not thoroughly capture the real-world scheduling practice, and, most importantly, do not guarantee the allocation of acceptable/tolerable or viable displacement among competing airlines’ slot requests. In this paper, we propose the formulation of the strategic airport slot allocation problem as a bi-objective resource constrained project scheduling problem with partially renewable resources and non-regular objective functions. We employ two non-regular performance criteria: (i) the total earliness-tardiness and (ii) a dispersion measure aiming to alleviate over-displaced requests. Α novel hybrid heuristic algorithm integrating the Objective Feasibility Pump (FP) algorithm with the Large Neighborhood Search technique (LNS) is proposed. We generate a set of new problem instances originating from the patterns of a data set of actual slot requests for a Greek Regional Airport (GRA) to assess the performance of the algorithm. The computational results indicate that the proposed algorithm is reasonably accurate, and it has the capability to approximate the entire efficient frontier of the problem.

Using hybrid algorithm to reduce non-working distance in intra- and inter-field logistics simultaneously for heterogeneous harvesters

Reducing the non-working distance for agricultural vehicles is beneficial for reducing the total operational costs for farmers. In this study, we optimize the non-working distance in intra- and inter-fields simultaneously for harvesters while harvesting rice. A mixed integer programming is presented for optimizing the rice harvesting problem, where a similar working time for all harvesters are considered based on a real case. Besides, in this model, two kinds of fields, namely moist and non-moist fields are considered, where the first can only be harvested by crawler harvesters while the second can be harvested by any type of harvester. This model aims to find an optimal schedule for the harvesters. A hybrid algorithm using both adaptive large neighborhood search and tabu search techniques is implemented to optimize the problem. A case study is considered to verify the performance of the hybrid algorithm. The results indicate that the hybrid algorithm is an effective method for solving the rice harvesting problem because the total non-working distance could be improved significantly by 15.3%. The proportion of moist fields is analyzed because it influences the schedule significantly. When compared to the traditional method for optimizing the non-working distance for harvesters, the total non-working distance in this method could be reduced by 6.2%. This paper provides farmers with a general framework for reducing the non-working distance in intra- and inter-field logistics, which results in a more efficient performance of the harvesters in the fields.