Local Neighborhood Search Research Articles

Modified salp swarm algorithm for global optimisation

Salp swarm algorithm (SSA) is a newly swarm-based metaheuristic algorithm that simulate the swimming and foraging behaviour of salps in oceans so to search for global optimum solution. Similarly to other metaheuristic algorithms, SSA suffers from poor convergence rate and stagnation in local optima. In this paper, three different improvements to the original population update process are proposed in order to enhance its exploitation and exploration capabilities. The first modification (MSSA1) introduces the concept of local best information to the followers salps update process allowing a better exploration of local search neighbourhood. The second improvement (MSSA2) provide two followers update process. The first is based on a differential evolution combined with a randomly selected local best position, and the second uses a local search in the global best neighbourhood which is triggered by a non-improvement in the corresponding local best. A third modification to the SSA algorithm (MSSA3) penalises a non-improvement of the local best solution by computing a new corresponding follower’s position based on a local jump in the local best neighbourhood for better exploitation. The performances of the proposed algorithms are tested on 27 CEC’15 test suite, and two real-world optimisation problems. A comparative study using nonparametric statistical tests of the obtained results is conducted against those of eight well-known metaheuristics, including the original SSA. The results indicate an overall distinctive performance of all three modification compared to the remaining algorithms, while MSSA1 scored generally better than MSSA2 and MSSA3.

A hyper heuristic for the green vehicle routing problem with simultaneous pickup and delivery

This paper studies the green vehicle routing problem with simultaneous pickup and delivery (G-VRPSPD). It aims to minimize fuel consumption costs while satisfying customer pickup and delivery demands simultaneously. The fuel consumption is directly proportional to green house gas emissions. We mathematically formulate the problem, and develop a hyper-heuristic (HH-ILS) algorithm based on iterative local search and variable neighborhood descent heuristics to effectively solve the problem. Extensive computational experiments are conducted to analyze the impact of the G-VRPSPD and the HH-ILS. We investigate the effect of green objective function on total fuel consumption cost by comparing the G-VRPSPD with the VRPSPD. We perform comparative analysis to investigate the performance of HH-ILS. We also conduct sensitivity analysis to investigate the performance of neighborhood structures, hyper heuristic and local search. The results show that the green objective function has a significant effect on total fuel consumption cost. The HH-ILS algorithm yields competitive results when compared with the mathematical formulation and the state-of-the-art heuristics in the literature.

Improvement in learning enthusiasm-based TLBO algorithm with enhanced exploration and exploitation properties

Learning enthusiasm-based Teaching Learning Based Optimization (LebTLBO) is a metaheuristic inspired by the classroom teaching and learning method of TLBO. In recent years, it has been effectively used in several applications of science and engineering. In the conventional TLBO and most of its versions, all the learners have the same probability of getting knowledge from others. LebTLBO is motivated by the different probabilities of acquiring knowledge by the learner from others and introduced a learning enthusiasm mechanism into the basic TLBO. In this work, to achieve the enhanced performance of conventional LebTLBO by balancing the exploration and exploitation capabilities, an improved LebTLBO algorithm is proposed. The exploration of LebTLBO has been enhanced by the incorporation of the Opposition Based Learning strategy. Exploitation has been improved by Local Neighborhood Search inspired by the experience of the best solution so far discovered in a local neighborhood of the present solution. On the CEC2019 benchmark functions, the suggested technique is assessed, and computational findings show that it provides promising outcomes over other algorithms. Finally, improved LebTLBO is employed in three engineering problems and the competitive findings demonstrate its potential for a real-world problem such as the localization problem in Wireless Sensor Networks.

Better Process Mapping and Sparse Quadratic Assignment

Communication and topology-aware process mapping is a powerful approach to reduce communication time in parallel applications with known communication patterns on large, distributed memory systems. We address the problem as a quadratic assignment problem (QAP) and present algorithms to construct initial mappings of processes to processors and fast local search algorithms to further improve the mappings. By exploiting assumptions that typically hold for applications and modern supercomputer systems such as sparse communication patterns and hierarchically organized communication systems, we obtain significantly more powerful algorithms for these special QAPs. Our multilevel construction algorithms employ perfectly balanced graph partitioning techniques and exploit the given communication system hierarchy in significant ways. We present improvements to a local search algorithm of Brandfass et al. (2013) and further decrease the running time by reducing the time needed to perform swaps in the assignment as well as by carefully constraining local search neighborhoods. We also investigate different algorithms to create the communication graph that is mapped onto the processor network. Experiments indicate that our algorithms not only dramatically speed up local search but also, due to the multilevel approach, find much better solutions in practice.

A multi-start evolutionary local search for the one-commodity pickup and delivery traveling salesman problem

This article addresses the one-commodity pickup and delivery traveling salesman problem (1-PDTSP), which is a generalization of the well-known traveling salesman problem. The 1-PDTSP aims to find a Hamiltonian tour in which a set of supply points (pickup locations), demand points (delivery locations) are visited and, the total traveled distance is minimized. We propose a hybrid metaheuristic based on multi-start evolutionary local search and variable neighborhood descent to solve the 1-PDTSP. To test the performance of our algorithm, we solve instances with up to 500 nodes available in the literature and we demonstrate that our approach is able to provide competitive results when comparing to other existing strategies. Since a direct application of the 1-PDTSP arises as the bicycle repositioning problem, we also use our metaheuristic algorithm to solve a set of real-case instances based on EnCicla, the bicycle sharing system in the Aburra Valley (Antioquia, Colombia).

A two-phase local search with a discrete-event heuristic for the omnichannel vehicle routing problem

This work tackles a pick up and delivery vehicle routing problem that emerges from the integration of delivery problems from two neighbouring layers of a supply chain: (i) retailer replenishment; and (ii) online customers deliveries, where online customer orders have to be picked up from retailers on route. This integration is motivated by recent developments in marketing, e.g.: the move to the omnichannel marketing paradigm, in which companies manage multiple sales channels in a seamless, unified, and integrated way. This paper proposes a novel model and an original solving approach. In order to tackle the complex capacity constraints, previous models propose to solve a delivery vehicle assignment problem before solving the associated vehicle routing problem. In contrast, our model deals with the whole routing problem by considering only the capacity feasible routing decisions. Our solving approach is based on a two-phase algorithm. In the first phase, a discrete-event constructive heuristic is employed. In the second phase, the most ‘promising’ solutions obtained in the previous phase are refined using a sequence of local search neighbourhoods. A series of extensive computational experiments show that our algorithm is able to identify new best-known solutions for the vast majority of problem instances. Also, for a set of small instances we obtain improved lower bounds compared to a previously proposed lower bound formulation.

A multicommodity unpaired pickup and delivery vehicle routing problem with split loads and unloads

PurposeThis paper aims to explore the commodity transshipment planning among customers, which is commonly observed in production/sales enterprises to save the operational costs.Design/methodology/approachA mixed integer programming (MIP) model is built and five types of valid inequalities for tightening the solution space are derived. An improved variable neighborhood search (IVNS) algorithm is presented combining the developed multistart initial solution strategy and modified neighborhood local search procedure.FindingsExperimental results demonstrate that: with less decision variables considered, the proposed model can solve more instances compared to the existing model in previous literature. The valid inequalities utilized to tighten the searching space can efficiently help the model to obtain optimal solutions or high-quality lower bounds. The improved algorithm is efficient to obtain optimal or near-optimal solutions and superior to the compared algorithm in terms of solution quality, computational time and robustness.ractical implicationsThis research not only can help reduce operational costs and improve logistics efficiency for relevant enterprises, but also can provide guidance for constructing the decision support system of logistics intelligent scheduling platform to cater for centralized management and control.Originality/valueThis paper develops a more compact model and some stronger valid inequalities. Moreover, the proposed algorithm is easy to implement and performs well.

Bilinear Assignment Problem: Large Neighborhoods and Experimental Analysis of Algorithms

The bilinear assignment problem (BAP) is a generalization of the well-known quadratic assignment problem. In this paper, we study the problem from the computational analysis point of view. Several classes of neighborhood structures are introduced for the problem along with some theoretical analysis. These neighborhoods are then explored within a local search and variable neighborhood search frameworks with multistart to generate robust heuristic algorithms. In addition, we present several very fast construction heuristics. Our systematic experimental analysis disclosed some interesting properties of the BAP, different from those of comparable models. We have also introduced benchmark test instances that can be used for future experiments on exact and heuristic algorithms for the problem.

GLS and VNS based heuristics for conflict-free minimum-latency aggregation scheduling in WSN

ABSTRACT We consider a conflict-free minimum latency data aggregation problem that occurs in different wireless networks. Given a network that is presented as an undirected graph with one selected vertex (a sink), the goal is to find a spanning aggregation tree rooted in the sink and to define a conflict-free aggregation minimum length schedule along the arcs of the tree directed to the sink. Herewith, at the same time slot, each element of the network can either send or receive at most one message. Only one message should be sent by each network element during the whole aggregation session, and the conflicts caused by signal interference should be excluded. This problem is NP-hard and remains NP-hard even in the case when the aggregation tree is given. Therefore, the development of efficient approximation algorithms is very important for this problem. In this paper, we present new heuristic algorithms based on genetic local search and variable neighbourhood search metaheuristics. We conducted an extensive simulation that demonstrates the superiority of our algorithms compared with the best of the previous approaches.

Memetic Algorithm With Local Neighborhood Search for Bottleneck Supplier Identification in Supply Networks

With the development of lean manufacturing and economic globalization, supply networks increasingly become complex and large-scale, within which thousands of firms inter-depend with each other. Due to these increasing inter-dependencies, disruption of a quite few critical suppliers, namely bottleneck suppliers, can induce high loss to a supply network and even make the whole network dysfunction. Identification of bottleneck suppliers is significantly important for supply network risk management. Thus, in this article, a method based on a memetic algorithm with local neighborhood search (MALNS) is proposed to identify bottleneck suppliers in a two-stage supply network. Firstly, a model based on multipartite network is designed to describe the product supply-demand relations between multiple manufacturers and suppliers, which considers the different roles of manufacturer and supplier and differentiates the products that suppliers supply. To assess the loss caused by supplier disruptions, two performance metrics of supply networks, average product availability rate and manufacturer functioning rate, are presented. Then, a MALNS-based method is proposed to identify bottleneck suppliers, i.e., suppliers whose disruption will decrease both performance metrics most greatly. Finally, a case study based on a real automobile supply network is presented to validate the applicability and effectiveness of the proposed method.

Tabu-Search-Based Pilot Assignment for Cell-Free Massive MIMO Systems

In this correspondence, an efficient pilot assignment scheme based on tabu search (TS) is proposed to mitigate the severe pilot contamination effect caused by pilot reuse in cell-free massive multiple-input multiple-output systems. By exploiting the local neighborhood search, we first define the neighborhood and search objective function. Then, the tabu list is introduced to record the prohibited solutions and to avoid being trapped in the local optimum. In addition, we consider a fair policy for the pilot reused times to further improve the system performance. We propose a TS-based pilot assignment iterative algorithm to search the suboptimal pilot assignment result with low complexity. Numerical results verify that the proposed TS-based pilot assignment scheme can significantly improve the 95%-likely per-user throughput with similar complexity compared to the conventional random and greedy schemes.

The solution of the concurrent layout scheduling problem in the job-shop environment through a local neighborhood search algorithm

The concurrent layout and scheduling problem is an extension of the well-known job-shop scheduling problem with transport delays, in which, in addition to the decisions taken in the classic problem, the location of machines must be selected from a set of possible sites. The aim of this study was to solve this problem by using a local neighborhood search algorithm (LNSA). This algorithm used a random local neighborhood, where neighbors were produced by simple operators commonly used in metaheuristics for scheduling problems. The solution coding used in the LNSA enabled all the calculated solutions to be valid schedules for the problem. The findings of the study were compared with those obtained by Ranjbar, who proposed the benchmark problems. Our method obtained a minor average relative percentage compared to the best-known results. Furthermore, it achieved a smaller makespan in the problems with higher computational complexity, which may aid companies that require customized manufacturing.

An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP

For solving the complex flexible job-shop scheduling problem, an improved genetic algorithm with adaptive variable neighborhood search (IGA-AVNS) is proposed. The improved genetic algorithm first uses a hybrid method combining operation sequence (OS) random selection with machine assignment (MA) hybrid method selection to generate the initial population, and it then groups the population. Each group uses an improved genetic operation for global search, then the better solutions from each group are stored in the elite library, and finally, the adaptive local neighborhood search is used in the elite library for detailed local searches. The simulation experiments are carried out by three sets of international standard examples. The experimental results show that the IGA-AVNS algorithm is an effective algorithm for solving flexible job-shop scheduling problems.

On a multi-trip vehicle routing problem with time windows integrating European and French driver regulations

In the European Union, truck drivers must comply with Regulation (EC) No 561/2006, which provides a set of rules on the daily or weekly working time and on break or rest periods after specific working times. Truck drivers can be back to the depot much earlier than the end of their working period, and be able to perform other trips. The resulting problem is a Multi-Trip Vehicle Routing Problem with Time Windows, where truck drivers have the possibility to accomplish, during their time horizon, more than one trip, combined with a Truck Driver Scheduling Problem. It aimed to find and assign a set of routes to a fleet of vehicles allowing each customer to be visited within its given time window, and the accumulated load to be delivered (or collected) not exceeding the capacity of the vehicle. Each truck driver must comply with applicable hours of service regulations, and the objective is to minimize total transportation cost. To solve this problem, we have developed a hybrid method composed of a genetic algorithm and a local neighborhood search. Experiments have been conducted on instances from the literature and real-life cases from SYSTRANS, a specialized French software company.

Generating Local Search Neighborhood with Synthesized Logic Programs

Local Search meta-heuristics have been proven a viable approach to solve difficult optimization problems. Their performance depends strongly on the search space landscape, as defined by a cost function and the selected neighborhood operators. In this paper we present a logic programming based framework, named Noodle, designed to generate bespoke Local Search neighborhoods tailored to specific discrete optimization problems. The proposed system consists of a domain specific language, which is inspired by logic programming, as well as a genetic programming solver, based on the grammar evolution algorithm. We complement the description with a preliminary experimental evaluation, where we synthesize efficient neighborhood operators for the traveling salesman problem, some of which reproduce well-known results.

Research Report on Automatic Synthesis of Local Search Neighborhood Operators

Constraint Programming (CP) and Local Search (LS) are different paradigms for dealing with combinatorial search and optimization problems. Their complementary features motivated researchers to create hybrid CP/LS solutions, maintaining both the modeling capabilities of CP and the computational advantages of the heuristic-based LS approach. Research presented in this report is focused on developing a novel method to infer an efficient LS neighborhood operator based on the problem structure, as modeled in the CP paradigm. We consider a limited formal language that we call a Neighborhood Definition Language, used to specify the neighborhood operators in a fine-grained and declarative manner. Together with Logic Programming runtime called Noodle, it allows to automatically synthesize complex operators using a Grammar Evolution algorithm.

A novel hybrid genetic algorithm for the location routing problem with tight capacity constraints

Location routing problem (LRP) is a popular and challenging topic in the field of logistic systems. LRP needs to address the depot location problem and vehicle routing problem at the same time. Till now, different LRP variants have been formulated to better meet realistic requirements. In this study, we focus on the capacitated LRP (CLRP) with tight capacity constraint on both depots and vehicles. To cope with the tight constraints, a hybrid genetic algorithm (HGA) is developed to search not only feasible solution space but also infeasible solution space. The proposed HGA combines the wide exploration capacity of GA, and the fast exploitation capacity of neighbourhood local search. To evolve GA for CLRP, solutions are represented by sets and sequences, and accordingly, a multi-sequence-based crossover is designed for the offspring generation. Moreover, a population management scheme is designed to facilitate GA’s evolution. Experiments are conducted on two benchmark sets and the results show that HGA is quite competitive with existing well-known CLRP algorithms on the classical instances. Furthermore, HGA is able to obtain quite a number of new best solutions on the real-life-like instances with tighter constraints.

Local Search Methods for a Distributed Assembly No-Idle Flow Shop Scheduling Problem

Due to the complexity of a real-practice manufacturing process, various complex constraints should be considered to make the conventional model more suitable for the realistic production. This paper proposes a distributed assembly no-idle flow shop scheduling problem (DANIFSP) with the objective of minimizing the makespan at the assembly stage. The DANIFSP consists of two stages, i.e., production and assembly. The production stage contains several identical flow shops working in parallel, in which all jobs with series of operations that should be allocated to one of these factories and all operations of jobs should be performed in the allocated factories. To satisfy the no-idle constraint, each machine must process jobs without any interruption from the start of processing the job to the completion of processing the last job. In the second assembly stage, the processed jobs are assembled by a single machine. For addressing the DANIFSP, this paper extends three constructive heuristics based on a new job assignment rule and proposes two simple meta-heuristics including iterated local search (ILS) and variable neighborhood search (VNS). A comprehensive calibration and analysis for the proposed algorithms through a design of experiments are carried out. The comparison with recently published algorithms demonstrates the high effectiveness of the proposed ILS and VNS.

A multi-start ILS–RVND algorithm with adaptive solution acceptance for the CVRP

This study proposes a novel hybrid algorithm based on Iterated Local Search (ILS) and Random Variable Neighborhood Descent (RVND) metaheuristics for the purpose of solving the Capacitated Vehicle Routing Problem (CVRP). The main contribution of this work is that two new search rules have been developed for multi-starting and adaptive acceptance strategies, and applied together to enhance the power of the algorithm. A comprehensive experimental work has been conducted on two common CVRP benchmarks. Computational results demonstrate that both multi-start and adaptive acceptance strategies provide a significant improvement on the performance of pure ILS–RVND hybrid. Experimental work also shows that our algorithm is highly effective in solving CVRP and comparable with the state of the art.

A P-hub median network design problem with preventive reliability approach for before and after hub failure

Hubs are vital elements of communication and transportation networks and play an important role in interchanging the flows of information/passenger/goods. For this purpose, designing a highly reliable hub network is very critical, because inefficiency of even a single hub across the network tends to reduce the efficiency of the whole network in transferring the flow appropriately. In this research, a bi-objective mathematical model was designed to study the situations before and after hub failure. Considering reliability, the first objective was to maximize the flow through the network, and the second objective was to prevent wasting the flow due to a possible hub failure. The lexicographic method was used to solve this multi-objective problem with dependent objectives. This method represents an appropriate solution for problems whose objective functions are of different priorities or depend on one another. Various cases of different sizes were used to evaluate the model in terms of reliability. Since the hub location problem is an NP-Hard problem of commonly large dimensions, a hybrid meta-heuristic algorithm called “memetic algorithm” was used to have it solved. The algorithm was a combination of genetic algorithm with simulated annealing algorithm, where simulated annealing algorithm was used for local neighborhood search. Findings indicated that, consideration of the backup hub tends to enhance route reliability, thereby increasing the flow through the network, as compared to the case with no backup hub.