Guided Local Search Research Articles

Comparing Optimized Sound Absorption Coefficient of Aluminum Foam with Local Search Algorithm, Genetic Algorithm, and Particle Swarm Optimization

Introduction: The principle of passive sound control is based on the phenomenon of sound absorption by absorbers. The factors affecting sound absorption include porosity, pore size, pore opening size, thickness, and air flow resistance. Materials and methods: In this study, the authors compared the optimization results of the effective parameters on sound absorption coefficient (AC) using the three optimization methods: Guided Local Search (GLS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The programming was done in MATLAB software. Thicknesses of 5, 10, 20, 30 and 40 mm were chosen for optimization at frequencies of 500 to 3000 Hz. Results: In frequencies above 2 kHz (thickness 5 to 40 mm), the three optimal methods had the same performance and estimated AC of 1. At low frequencies of 2 kHz and thicknesses of 30 and 40 mm, GA and PSO methods obtained an AC of 1. Conclusion: It seems that the GA and PSO optimization algorithm are suitable methods to optimize the AC of metal foam in low and high frequencies.

Optimization of emergency logistics delivery path based on guided local search algorithm

Nowadays, the demand for risk response is increasing in countries worldwide, leading to the development of emergency-related industries as strategic emerging sectors. However, the emergency logistics industry is facing increasingly critical distribution issues. This study applies K-means clustering analysis to convert multiple distribution centers into multiple single distribution center problems. It then compares and analyzes the vehicle routing model with time windows for emergency logistics delivery in multiple distribution centers using guided local search (GLS), taboo search (TS), and simulated annealing (SA) algorithm. The results demonstrate that the GLS algorithm outperformed both the SA and TS algorithm in optimizing emergency logistics delivery paths for multiple distribution centers. The GLS algorithm proved to be more effective in solving this problem. This study confirms the contemporary value of emergency logistics distribution problems and offers practical insights into optimizing emergency logistics distribution paths in multiple distribution centers.

Optimization of vehicle routing problem using guided local search and simulated annealing

Transportation concerns in the supply chain are important because they have the potential to greatly raise logistical requirements. The proposed solution to the issue is the vehicle routing problem. The Heterogeneous Fleet Vehicle Routing Problem (HFVRP) model will be implemented in this study using the metaheuristic methodology together with the Guided Local Search (GLS) and Simulated Annealing (SA) approaches for case studies. Python 3.10 software was used to analyze the SA and GLS, and the Google OR-Tools module was used. Implementing the SA and GLS algorithm based on data from the case study in a logistics company involved in the distribution of chicken feed and gas is the goal of this project. By doing so, it will be possible to determine whether the current situation is ideal or if it could be improved to reduce the number of vehicles used, find the best route, lower shipping costs, and increase efficiency. The findings of this study show that when compared to the GLS algorithm, research employing SA produces better outcomes in terms of cost and route management. According to actual data, SA increases income by an average amount between 3% and 10%.

Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem

The solid waste collection problem refers to truck route optimisation to collect waste from containers across various locations. Recent concerns exist over the impact of solid waste management on the environment. Hence, it is necessary to find feasible routes while minimising operational costs and fuel consumption. In this paper, in order to reduce fuel consumption, the number of trucks used is considered in the objective function along with the waste load and the travelling time. With the current computational capabilities, finding an optimal solution is challenging. Thus, this study aims to investigate the effect of well-known metaheuristic methods on this problem’s objective function and computational times. The routing solver in the Google OR-tools solver is utilised with three well-known metaheuristic methods for neighbourhood exploration: a guided local search (GLS), a tabu search (TS), and simulated annealing (SA), with two initialisation strategies, Clarke and Wright’s algorithm and the nearest neighbour algorithm. Results showed that optimal solutions are found in faster computational times than using only an IP solver, especially for large instances. Local search methods, notably GLS, have significantly improved the route construction process. The nearest neighbour algorithm has often outperformed the Clarke and Wright's methods. The findings here can be applied to improve operations in Saudi Arabia’s waste management sector.

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

The blockchain network uses a Proof-of-Work (PoW) mechanism to validate transactions and keep the blockchain growth safe against tampering, but it is hugely energy-consuming with no benefit to the peer-to-peer network participants. In this paper, we proposed a blockchain network for distributing products to different locations based on the use of the Proof of Useful Work mechanism, in which miners use computing resources to optimize the traveling salesman problem (TSP) as an alternative to solving mathematical problems that represent the basis of the traditional PoW mechanism to get a new block. According to this proposed blockchain, it not only receives and securely stores the distribution locations but also improves the paths for salesmen when traveling between different locations during the transportation process. This strategy aims to take advantage of the miners’ efforts to minimize the traveled distance by applying the clustering technique and computing the shortest path by Guided Local Search (GLS) for each cluster at the same time. According to the tested results on TSP-LIB instances, the used strategy works efficiently with an average of 0.08 compared to the rest of the meta-heuristics, and the proposed architecture reduced total distances with an average of 0.025%. In addition, the block generation time in the blockchain decreased by 11.11% compared to other works.

Identification of road links with the gravest network impacts when blocked concurrently

The identification of a combination of links which can cause the gravest impact on network performance, when interrupted simultaneously, is of great practical importance. The links can be spatially dispersed due to an event such as an earthquake or due to a combination of accidents and disasters. This task is, thus, extremely computationally demanding when applied to real-world networks. We focused on approaches which are both capable of accomplishing this task and where the computational time is acceptable for application in practice. We tested three algorithms based on known heuristic methods: Simulated Annealing (SA), Guided Local Search (GLS) and Variable Neighborhood Search (VNS). The algorithms were modified in the sense of adjusting the searching neighborhood. All the algorithms were subsequently applied to four actual road networks in order to evaluate the impacts of complete simultaneous blockage of four and ten links. The results suggest that the modified SA algorithm identified scenarios with worse consequences than the algorithms based on GLS and VNS. The SA results, for the setting with four interrupted links, were even comparable with those obtained from a deterministic algorithm (which evaluates the entire state space). The algorithm based on SA was also performing best for situations with ten concurrently blocked links. The approach based on SA is thus suitable when modeling the potential impacts of events where a large number of concurrently blocked links is expected. Network managers will thus be able to monitor the immediate state of the network and potential risks related to network disintegration.

PE-DCA: Penalty elimination based data center allocation technique using guided local search for IaaS cloud

In Cloud computing the user requests are passaged to data centers (DCs) to accommodate resources. It is essential to select the suitable DCs as per the user requests so that other requests should not be penalized in terms of time and cost. The searching strategies consider the execution time rather than the related penalties while searching DCs. In this work, we discuss Penalty Elimination-based DC Allocation (PE-DCA) using Guided Local Search (GLS) mechanism to locate suitable DCs with reduced cost, response time, and processing time. The PE-DCA addresses, computes, and eliminates the penalties involved in the cost and time through iterative technique using the defined objective and guide functions. The PE-DCA is implemented using CloudAnalyst with various configurations of user requests and DCs. We examine the PE-DCA and the execution after-effects of various costs and time parameters to eliminate the penalties and observe that the proposed mechanism performs best.

MOEA/D-GLS: a multiobjective memetic algorithm using decomposition and guided local search

This paper proposes an idea of using well studied and documented single-objective optimization methods in multiobjective evolutionary algorithms. It develops a hybrid algorithm which combines the multiobjective evolutionary algorithm based on decomposition (MOEA/D) with guided local search (GLS), called MOEA/D-GLS. It needs to optimize multiple single-objective subproblems in a collaborative way by defining neighborhood relationship among them. The neighborhood information and problem-specific knowledge are explicitly utilized during the search. The proposed GLS alternates among subproblems to help escape local Pareto optimal solutions. The experimental results have demonstrated that MOEA/D-GLS outperforms MOEA/D on multiobjective traveling salesman problems.

A new cooperative framework for parallel trajectory-based metaheuristics

In this paper, we propose the Parallel Elite Biased framework (PEB framework) for parallel trajectory-based metaheuristics. In the PEB framework, multiple search processes are executed concurrently. During the search, each process sends its best found solutions to its neighboring processes and uses the received solutions to guide its search. Using the PEB framework, we design a parallel variant of Guided Local Search (GLS) called PEBGLS. Extensive experiments have been conducted on the Tianhe-2 supercomputer to study the performance of PEBGLS on the Traveling Salesman Problem (TSP). The experimental results show that PEBGLS is a competitive parallel metaheuristic for the TSP, which confirms that the PEB framework is useful for designing parallel trajectory-based metaheuristics.

A two-phase local search algorithm for the ordered clustered travelling salesman problem

The ordered clustered travelling salesman problem (OCTSP) is an extension of the classical travelling salesman problem where the set of vertices is partitioned into clusters with a prespecified order. The objective is to find a minimum cost Hamiltonian tour such that the vertices in any cluster are visited contiguously and the clusters are visited in the given order. This paper proposes a two-phase approach using the 2-Opt heuristic and the guided local search (GLS) metaheuristic for obtaining approximate solutions of high-quality to the OCTSP. The first phase attempts to find an optimal Hamiltonian cycle for each cluster. The resulted cycles will be concatenated in the second phase, which then focuses on finding an optimal Hamiltonian cycle for the OCTSP. Computational results confirm the effectiveness of the proposed approach in terms of solution quality and computational time, in comparison with a hybrid genetic algorithm (HGA) on symmetric instances obtained from the travelling salesman problem instance library (TSPLIB).

A two-phase local search algorithm for the ordered clustered travelling salesman problem

The ordered clustered travelling salesman problem (OCTSP) is an extension of the classical travelling salesman problem where the set of vertices is partitioned into clusters with a prespecified order. The objective is to find a minimum cost Hamiltonian tour such that the vertices in any cluster are visited contiguously and the clusters are visited in the given order. This paper proposes a two-phase approach using the 2-Opt heuristic and the guided local search (GLS) metaheuristic for obtaining approximate solutions of high-quality to the OCTSP. The first phase attempts to find an optimal Hamiltonian cycle for each cluster. The resulted cycles will be concatenated in the second phase, which then focuses on finding an optimal Hamiltonian cycle for the OCTSP. Computational results confirm the effectiveness of the proposed approach in terms of solution quality and computational time, in comparison with a hybrid genetic algorithm (HGA) on symmetric instances obtained from the travelling salesman problem instance library (TSPLIB).

An efficient guided local search approach for multiuser detection in UWB systems

In this paper, we tackled the performance of a multiuser detection (MUD) in ultra wideband (UWB) systems over extreme multipath channel model (CM4) based on TH-PPM and DS-PAM in a rake receiver. For better performance at receiver side, a novel guided local search (GLS) algorithm is proposed for UWB systems. Simulation results show a significant bit error rate (BER) performance gain can be achieved based on the proposed GLS-MUD method, with conventional detector (CD). In addition, the technique is very promising when the number of interfering users increases.

EB-GLS: an improved guided local search based on the big valley structure

Local search is a basic building block in memetic algorithms. Guided Local Search (GLS) can improve the efficiency of local search. By changing the guide function, GLS guides a local search to escape from locally optimal solutions and find better solutions. The key component of GLS is its penalizing mechanism which determines which feature is selected to penalize when the search is trapped in a locally optimal solution. The original GLS penalizing mechanism only makes use of the cost and the current penalty value of each feature. It is well known that many combinatorial optimization problems have a big valley structure, i.e., the better a solution is, the more the chance it is closer to a globally optimal solution. This paper proposes to use big valley structure assumption to improve the GLS penalizing mechanism. An improved GLS algorithm called Elite Biased GLS (EB-GLS) is proposed. EB-GLS records and maintains an elite solution as an estimate of the globally optimal solutions, and reduces the chance of penalizing the features in this solution. We have systematically tested the proposed algorithm on the symmetric traveling salesman problem. Experimental results show that EB-GLS is significantly better than GLS.

Optimisation of transportation service network using κ-node large neighbourhood search

The Service Network Design Problem (SNDP) is generally considered as a fundamental problem in transportation logistics and involves the determination of an efficient transportation network and corresponding schedules. The problem is extremely challenging due to the complexity of the constraints and the scale of real-world applications. Therefore, efficient solution methods for this problem are one of the most important research issues in this field. However, current research has mainly focused on various sophisticated high-level search strategies in the form of different local search metaheuristics and their hybrids. Little attention has been paid to novel neighbourhood structures which also play a crucial role in the performance of the algorithm. In this research, we propose a new efficient neighbourhood structure that uses the SNDP constraints to its advantage and more importantly appears to have better reachability than the current ones. The effectiveness of this new neighbourhood is evaluated in a basic Tabu Search (TS) metaheuristic and a basic Guided Local Search (GLS) method. Experimental results based on a set of well-known benchmark instances show that the new neighbourhood performs better than the previous arc-flipping neighbourhood. The performance of the TS metaheuristic based on the proposed neighbourhood is further enhanced through fast neighbourhood search heuristics and hybridisation with other approaches.

Genetic algorithm hybridized by a guided local search to solve the emergency coverage problem

The management of emergency logistics is addressed by several researchers. This paper addresses the ambulance allocation in order to cover sectors in the Rabat region of Morocco. Our model takes into account the dynamic and stochastic nature of emergency calls arrival. This work proposes a mathematical model of the coverage problem, resolved using a genetic algorithm (GA) initialised by a heuristic and hybridised by a guided local search (GLS). We consider 12 emergency locations; seven hospitals of the region and five fire stations. These algorithms are approved comparing to the optimal solutions done by Cplex software. As a result, the GA hybridised by a GLS provides a distribution of ambulances in each potential waiting site (hospital or fire station), and minimises the total lateness of emergency intervention.

The multi-vehicle profitable pickup and delivery problem

The transportation industry expanded rapidly in a highly competitive environment. Logistics companies with insufficient volume of transport capacities are forced to make a selection of customers that they can integrate efficiently into their tours. This is of particular relevance in the pickup and delivery market, where shipments from several different customers can be moved on the same vehicle. In the literature, however, the problem of customer selection has not been applied for the given class of pickup and delivery problems so far. We want to fill this gap by introducing the multi-vehicle profitable pickup and delivery problem (MVPPDP), where multiple carriers transport goods from a selection of pickup customers to the corresponding delivery customers within given travel time limits. For this problem, we propose a method based on general variable neighborhood search (GVNS). We conduct experiments with two different variants of this method, namely a sequential (GVNSseq) and a self-adaptive (GVNSsa) version. Additionally, we compare it to an algorithm based on Guided Local Search (GLS), which is known to find good solutions for related problems very fast. The performance of these methods is examined on the basis of data instances with up to 1000 customer requests. In an experimental study, we observe that both variants of GVNS with 11 neighborhoods outperform GLS with regard to solution quality for all sizes of test instances. However, for medium sized and large instances, GLS shows an advantage in average runtimes.

O uso da metaheurística Guided Local Search para resolver o problema de escala de motoristas de ônibus urbano

Neste artigo é aplicada a metaheurística <em>Guided Local Search</em> (GLS) para resolver o Problema de Programação de Tripulações de Ônibus Urbano (PPT). O PPT consiste em encontrar um conjunto de jornadas a serem designadas aos motoristas que realizarão a operação diária com o menor custo. A GLS tem como princípio penalizar características indesejáveis presentes na solução corrente, com o objetivo de escapar de soluções ótimas locais. Como heurística de busca local, foi utilizada a heurística <em>Variable Neighborhood Descent</em>, que explora diferentes estruturas de vizinhança para encontrar um mínimo local. De acordo com pesquisa realizada pelos autores, esta abordagem é inédita para resolver o PPT. A implementação proposta foi testada com dados de problemas reais de uma empresa que opera em uma região metropolitana de Belo Horizonte. Os resultados obtidos são similares àqueles presentes na literatura, havendo possibilidades de melhorias, visto que a GLS pode ser explorada em diferentes aspectos.

APPLYING COMBINATORIAL OPTIMIZATION HEURISTICS FOR ENHANCING THE PERFORMANCE OF TH-PPM UWB SYSTEMS: CHAOTIC VERSUS CLASSICAL CODE SEQUENCES

In this article, we propose a process to minimize the Multi-User Interference (MUI) caused by Multiple Access (MA) in a Time Hopping Ultra Wide Band (TH-UWB) system with No Line-Of-Sight (NLOS) multipath channel in a residential environment IEEE 802.15.4a Channel Model 2 (CM2). We discuss both synchronous and asynchronous channels with an Additive White Gaussian Noise (AWGN). The choice of codes allowing multiple access is crucial for minimizing the MUI and to improve the system performance. Recently much works has been done in order to optimize performance by code selection [Bravo et al., 1999; Moreno & Marić, 2000; Guvenc & Arslan, 2004]. This paper is a contribution to this research axis. The minimization of the MUI is formulated as a Combinatorial Optimization Problem (COP) and solved by means of an efficient generic optimization method called Guided Local Search (GLS) [Voudouris, 1997]. In this context, we exploit the richness of chaotic sequences with respect to topologic properties. Simulation results based on the Binary Error Rate (BER) show that optimized chaotic sequences outperforms the classical code sequences.

Tabu assisted guided local search approaches for freight service network design

The service network design problem (SNDP) is a core problem in freight transportation. It involves the determination of the most cost-effective transportation network and the characteristics of the corresponding services, subject to various constraints. The scale of the problem in real-world applications is usually very large, especially when the network contains both the geographical information and the temporal constraints which are necessary for modelling multiple service-classes and dynamic events. The development of time-efficient algorithms for this problem is, therefore, crucial for successful real-world applications. Earlier research indicated that guided local search (GLS) was a promising solution method for this problem. One of the advantages of GLS is that it makes use of both the information collected during the search as well as any special structures which are present in solutions. Building upon earlier research, this paper carries out in-depth investigations into several mechanisms that could potentially speed up the GLS algorithm for the SNDP. Specifically, the mechanisms that we have looked at in this paper include a tabu list (as used by tabu search), short-term memory, and an aspiration criterion. An efficient hybrid algorithm for the SNDP is then proposed, based upon the results of these experiments. The algorithm combines a tabu list within a multi-start GLS approach, with an efficient feasibility-repairing heuristic. Experimental tests on a set of 24 well-known service network design benchmark instances have shown that the proposed algorithm is superior to a previously proposed tabu search method, reducing the computation time by over a third. In addition, we also show that far better results can be obtained when a faster linear program solver is adopted for the sub-problem solution. The contribution of this paper is an efficient algorithm, along with detailed analyses of effective mechanisms which can help to increase the speed of the GLS algorithm for the SNDP.

Using penalties instead of rewards: Solving OCST problems with guided local search

This paper considers the optimal communication spanning tree (OCST) problem. Previous work analyzed features of high-quality solutions and found that edges in optimal solutions have low weight and point towards the center of a tree. Consequently, integrating this problem-specific knowledge into a metaheuristic increases its performance for the OCST problem. In this paper, we present a guided local search (GLS) approach which dynamically changes the objective function to guide the search process into promising areas. In contrast to traditional approaches which reward promising solution features by favoring edges with low weights pointing towards the tree’s center, GLS penalizes low-quality edges with large weights that do not point towards the tree’s center. Experiments show that GLS is a powerful optimization method for OCST problems and outperforms standard EA approaches with state-of-the-art search operators for larger problem instances. However, GLS performance does not increase if more knowledge about low-quality solutions is considered but is about independent of whether edges with low weight or wrong orientation are penalized. This is in contrast to the classical assumption that considering more problem-specific knowledge about high-quality solutions does increase search performance.