Applying Tabu Search Research Articles

Applying Tabu Search Optimisation in Zone Distribution System Reconfiguration A Stochastic Model

The main idea in the distribution system is to reconfigure the network and to change the sectionalizing switches' state to reduce power loss and maintain system stability. In network reconfiguration, a key role is to find the appropriate network topology that does have the lowest losses during any conditions present within the network. Reliability, security, and system operating limitations are the most significant parts of the network reconfiguration that are suitable. In the work, the authors suggested to apply Tabu search optimisation technique to solve the problem of network reorganisation in the distribution system by creating a stochastic model of a virtual zone based on data prepared in a previous study by the first author. The stochastic model consists of four feeders and 106 buses. The validity of the suggested technique is verified by relying on standard practices and its ability to establish a correct network topology in the system distribution. This investigation determines that the suggested technique is excellently suitable for creating a virtual zone network and can be applied in reality.

A Self-Organized Task Distribution Framework for Module-Based Event Stream Processing

Tackling bottleneck and privacy issues of cloud computing, we attempt to push event stream processing down to devices which are currently empowered to compute and communicate at the edge of the networks. To accomplish that, we propose a self-organized task distribution framework that is composed of multiple brokers collaborating through our module-based event stream processing engine called EdgeCEP . Our system request is event-dependent specified in a brand-new event specification language; still, the event is stored and processed by the relational database. We newly formulate the problem of self-organized task distribution subjective to preferable constraints of computation and communication. The solution for each broker to find individual optimal decision is to apply tabu search with flow-based greedy move regarding pre-ranking flow table. Many experiments are conducted to study and evaluate the performance of the proposed system. The simulation shows that the proposed flow optimization outperforms the naive algorithm, concretely, 2-times more tasks getting processed and successfully delivered within the same fixed period. The proposed edge-centric method achieves data traffic 7-times less than the cloud-centric approach. The prototype engines have been deployed and evaluated in the real environment.

Applying Tabu Search in Finding an Efficient Solution for the OVRP

In the open vehicle routing problem (OVRP), the target would be to reduce the amount of vehicles after reducing the whole distance (or time) travelled. Every route begins at the depot and comes to an end at a customer, going to many customers, every once, en route, without returning to the depot. The demand of every customer has to be completely fullled by a single vehicle. The total demand serviced by each vehicle has to not go over vehicle capacity. A highly eective tabu search for open vehicle routing called Three Strategies Tabu Search (TSTS) heuristic for this problem is suggested. The TSTS depends on three strategies MOVE, EXCHANGE and SWAP. Computational results on fourteen standard benchmark problem instances demonstrate that the suggested TSTS is comparable in terms of solution quality for the best performing published heuristics.

Tabu search heuristic for efficiency of dynamic facility layout problem

The facility layout problem (FLP) is a well-researched problem of finding positions of departments on a plant floor such that departments do not overlap and some specific critical criteria involved in FLP are optimized. In addition, the facility layout dramatically influences the efficiency of material handling within a manufacturing system. In order to ensure optimal performance within a manufacturing system, the facility layout should reflect changes throughout time. However, the static facility layout problem with constant material flows between departments may not be a realistic scenario because a manufacturing facility is a dynamic system that constantly evolves. On the other hand, layout problem has multi-objective nature, and it is necessary to apply various criteria and goals for determining a good facility layout. As a result, this research is about determining a solution for dynamic facility layout problem (DFLP) with equal departments by applying data envelopment analysis (DEA) with consideration of some specific criteria which are cost, adjacency, and distance requested. Cost as a negative criterion is minimized and used as an input, whereas adjacency and distance requested are considered positive criteria and used as output of DEA model. In the proposed algorithm, at first, the initial layout is generated, and then its neighborhood is created by the pair exchange and reverse strategy. By applying tabu search heuristic using diversification strategy which includes “frequency-based memory,” “penalty function,” and “dynamic tabu list size” to the DEA model, the most desired efficient layout is obtained. Two data sets taken from the relevant literature are used to test and evaluate the performance of the proposed heuristic. Computational experiments show that the proposed heuristic outperforms other heuristics presented in relevant literature, and the most efficient layout is achieved.

Genetic tabu search for the fuzzy flexible job shop problem

This paper tackles the flexible job-shop scheduling problem with uncertain processing times. The uncertainty in processing times is represented by means of fuzzy numbers, hence the name fuzzy flexible job-shop scheduling. We propose an effective genetic algorithm hybridised with tabu search and heuristic seeding to minimise the total time needed to complete all jobs, known as makespan. To build a high-quality and diverse set of initial solutions we introduce a heuristic method which benefits from the flexible nature of the problem. This initial population will be the starting point for the genetic algorithm, which then applies tabu search to every generated chromosome. The tabu search algorithm relies on a neighbourhood structure that is proposed and analysed in this paper; in particular, some interesting properties are proved, such as feasibility and connectivity. Additionally, we incorporate a filtering mechanism to reduce the neighbourhood size and a method that allows to speed-up the evaluation of new chromosomes. To assess the performance of the resulting method and compare it with the state-of-the-art, we present an extensive computational study on a benchmark with 205 instances, considering both deterministic and fuzzy instances to enhance the significance of the study. The results of these experiments clearly show that not only does the hybrid algorithm benefit from the synergy among its components but it is also quite competitive with the state-of-the-art when solving both crisp and fuzzy instances, providing new best-known solutions for a number of these test instances.

Metaheuristic procedure for a bi-objective supply chain design problem with uncertainty

We consider the design of a two echelon production distribution network with multiple manufacturing plants, distribution centers (DC’s) and a set of candidate warehouses. One of the main contributions of the study is to extend the existing literature by incorporating the demand uncertainty of DC’s within the warehouse location and transportation mode allocation decisions, as well as providing a network design satisfying the both economical and service quality objectives of the decision maker within two echelon supply network setting. In order to take into account the effects of the uncertainty we apply an scenario-based approach and a two-stage stochastic problem is formulated in order to minimize total cost and total service time, simultaneously. Another important contribution is the development of a solution procedure for this bi-objective stochastic problem by applying tabu search within the framework of Multi-objective Adaptive Memory Programming. Results are compared with the optimal Pareto fronts obtained for small instances using the ∊-constraint method and standard branch and bound techniques. Numerical results demonstrate the computational effectiveness of the algorithm proposed. Finally, we include some results that confirm the convenience of including the randomness in the demand parameter.

A Tabu Search Method for a Bi‐Objective Urban Waste Collection Problem

AbstractA bi‐objective model for the collection of waste bins in rural areas within a planning period is analyzed. This model is a real problem raised by local authorities in a rural region of northwestern Spain. The two objectives under consideration are: minimization of transport costs and improvements to the level of service. Specifically, the level of service depends on the frequency of waste collection at each point over the planning period. A solution method for this problem was developed by applying tabu search within the framework of Multiobjective Adaptive Memory Programming (MOAMP) and the results were compared with an implementation of NSGA‐II, a well‐known approach to multiobjective optimization.

Bi-Objective Bus Routing: An Application to School Buses in Rural Areas

The min-max vehicle routing problem (VRP) is a variant of the classical VRP in which the objective is to minimize the duration of the longest route. Examination of the VRP literature indicates that the min-max VRP has received less attention than other variants have over the years. However, the problem has important practical applications, such as those related to routing school buses. In this setting, in addition to the min-max criterion imposed on the time it takes to complete the longest route, school districts are concerned with the minimization of the total distance traveled, which is the objective of the classical VRP. Hence, the problem is formulated as a bi-objective optimization model that trades off service (i.e., the minimization of the longest route) and operational cost (i.e., the minimization of the total distance traveled). We develop a solution procedure for this problem by applying tabu search within the framework of Multiobjective Adaptive Memory Programming and compare it to an implementation of the Non-dominated Sorting Genetic Algorithm—a well-known approach to multiobjective optimization. We also assess the merit of the solution method by comparing our approximations with solution frontiers obtained with an ε-constraint implementation.

Optimal Reconfiguration of Electrical Networks by Applying Tabu Search to Decrease Voltage Sag Indices

This article presents a methodology for electrical network reconfiguration to minimize voltage sags indices. The voltage sag analysis is carried out by means of an analytical method. A tabu search algorithm is implemented to find the optimal configuration in an electrical system to decrease voltage sags indices. The proposed algorithm is implemented in MATLAB® (The MathWorks, Natick, Massachusetts, USA). Several case studies in IEEE test systems of 57 and 118 buses are presented showing the performance of the proposed methodology.

THE TABU SEARCH APPLICATION: AN APPROACH TO MINE MARKET BASKET DATA

ABSTRACT The economic value of data mining is well established. Market basket analysis is one of the most well-known data mining techniques that is used to discover the customer purchase patterns. However, as the size of the dataset increases, the mining process becomes increasingly cumbersome and time consuming. In this paper, we propose to apply Tabu search metaheuristic (TS) to find global optimal solutions for large scale market basket datasets. In preparation for Tabu search, a market basket dataset is converted to a graph-based network. The result of an experiment with a large scale supermarket dataset show that the proposed TS algorithm produces highly accurate solutions in reduced computational times comparing to the traditional algorithm used by SAS Enterprise Miner. Keywords Market basket, Tabu search, Meta-heuristic

Automatic playlist generation by applying tabu search

In this paper, we propose a solution to the problem of playlist generation. In order to capture user listening preference and recommend playlists, we maintain user profiles by keeping listening history. Then, we apply the sequential pattern mining algorithm with multiple minimum supports on user profiles to derive constraints. Given a set of derived constraints, we apply the tabu search to generate playlists which match constraints as much as possible. Finally, we implement our prototype and perform experiments to show the feasibility, efficiency, and effectiveness of our approach.

First-level tabu search approach for solving the multiple-choice multidimensional knapsack problem

We apply tabu search to the multiple-choice multidimensional knapsack problem. An initial solution involves a simple greedy heuristic. A neighbourhood is generated and a move is selected based on the best feasible neighbouring solution. The search continues for a fixed number of iterations and tabu search structures are used to improve the search process. We study the problem structure of available, benchmark, test problem instances and develop a new, more diverse set of test problem instances whose results better generalise to practice than do results obtained using existing test problems. We report the results of testing our tabu search approach versus existing solution approaches as applied to the available and our new test problem instances. The computational results show that our proposed approach performs comparably or better than the legacy heuristic approaches and clearly demonstrates that test problems affects how well results generalise to practice.

Applying tabu search and simulated annealing to the optimal design of sewer networks

Optimizations of sewer network designs create complicated and highly nonlinear problems wherein conventional optimization techniques often get easily bogged down in local optima and cannot successfully address such problems. In the past decades, heuristic algorithms possessing robust and efficient global search capabilities have helped to solve continuous and discrete optimization problems and have demonstrated considerable promise. This study applied tabu search (TS) and simulated annealing (SA) to the optimization of sewer network designs. For a case study, this article used the sewer network design of a central Taiwan township, which contains significantly varied elevations, and the optimal designs from TS and SA were compared with the original official design. The results show that, in contrast with the original design's failure to satisfy the minimum flow-velocity requirements, both TS and SA achieved least-cost solutions that also fulfilled all the constraints of the design criteria. According to the average performance of 200 trials, SA outperformed TS in both robustness and efficiency for solving sewer network optimization problems.

Planificación de Trayectorias Libres de Colisión para Múltiples UAVs usando el Perfil de Velocidad

This paper presents a method for collision resolution among multiple UAVs sharing the airspace with non-cooperative aircrafts. The proposed method finds collision free trajectories by changing the velocity profile of the different vehicles involved in the conflict taking into account other mobile objects or non-cooperative vehicles. The objective is to find the closest solution to the initially planned trajectories for the UAvs. The collision resolution process has two main steps: initialization using tree search and final solution applying Tabu search. The paper presents different simulations showing the efficiency of the proposed method for collision resolution in real-time.

Optimal purchase of two-itemized drugs for a disease

This paper submits a mathematical model called the two-itemized drugs purchasing decision (TDPD) model to conduct the optimal purchase of two-itemized drugs for healing a given disease. This TDPD model considers not only the annual estimated number of patients suffering from the given disease, the treatment courses, and the unit profit of each drug, but also the strength of the patient's perception on treatment quality, the different purchasing price of each drug with quantity discounts, the ordering cost, the holding cost, and other related costs to determine the optimal order quantity of each drug for achieving the maximum profit. A computerized solving program proposed in this paper applies tabu search technique as the main program and the all-units quantity discounts for economic order quantity as the sub-program to solve such a combinatorial problem, as well as perform sensitivity analyses on the changes by the strength of the patient's perception on treatment quality and the changes of annual estimated number of patients for the given disease. Two drugs, Lipitor and Gemnpid, to heal a disease ‘Hyperlipidaemia’ are considered as a case example in this study. This paper contributes a prototype quantitative method of purchasing decision for a given disease to a decision-maker with profound insights.

A fast tabu search algorithm for the group shop scheduling problem

Three types of shop scheduling problems, the flow shop, the job shop and the open shop scheduling problems, have been widely studied in the literature. However, very few articles address the group shop scheduling problem introduced in 1997, which is a general formulation that covers the three above mentioned shop scheduling problems and the mixed shop scheduling problem. In this paper, we apply tabu search to the group shop scheduling problem and evaluate the performance of the algorithm on a set of benchmark problems. The computational results show that our tabu search algorithm is typically more efficient and faster than the other methods proposed in the literature. Furthermore, the proposed tabu search method has found some new best solutions of the benchmark instances.

Applying tabu search to backup path planning for multicast networks

More and more multimedia applications rely on the ability of networks to provide multicast communications. Network survivability becomes a critical issue in network planning, design, and operation to reduce the impacts of failures in high-speed networks. A rapid and cost-effective restorable network must be planned to maintain network survivability and use network bandwidth effectively. This paper considers the problem of preplanning delay-constrained backup path for multicast networks to minimize the total cost of all the backup paths. Based on the path restoration scheme, a tabu search (TS) algorithm is proposed to find a near optimal solution for the problem under investigation. Diversification and intensification strategies are suggested. The computational experiments are conducted to demonstrate the performance of the proposed TS algorithm. The results show that it offers high-quality solutions within a short amount of time.

Bounded Variables nonlinear Multiple Criteria Optimization using Scatter search

This paper introduces an adaptation of multiple criteria scatter search to deal with nonlinear continuous vector optimization problems on bounded variables, applying Tabu Search approach as diversification generator method. Frequency memory and another escape mechanism are used to diversify the search. A relation Pareto is apply in order to designate a subset of the best generated solutions to be reference solutions. A choice function called Kramer Selection is used to divide the reference solution in two subsets. The Euclidean distance is used as a measure of dissimilarity in order to find diverse solutions to complement the subsets of high quality current Pareto solutions to be combined. Convex combination is used as a combined method. The performance of this approach is evaluated on several test problems taken from the literature.

Design-Optimization of Mechanical Components Using the “Multiple Objective Tabu Search” Method

The multiple objective version of the tabu search (TS algorithm was initially developed by Baykasoglu et al [1-3]. The idea of applying tabu search to multiple objective optimization, inspired from its solution structure, in which tabu search works with more than one solution (neighborhood solutions) at a time. This situation creates the opportunity to evaluate multiple objectives simultaneously in one run. To enable the original tabu search algorithm to work with more than one objective the selection and updating stages were redefined. Other stages are identical to the original tabu search algorithm. In this paper, mutiple objective tabu search algorithm is used to solve mechanical component design problems) with multiple objectives. Although there exists a number of classical techniques, meta-heuristic algorithms including TS have an edge over the classical methods in that they can find multiple Pareto-optimal solutions in one single run. In the paper four mechanical component design problems borrowed from the literature are solved. The results are compared with several other solution techniques including multiple objective genetic algorithms. It is observed that in many of the cases the multiple objective tabu search algorithm can find better and much wider spread of solutions than the compared algorithms.

A Comparative Study Of Global Optimization Approaches To Meg Source Localization

It is well-known that the problem of MEG source localization can be cast as an optimization problem. So far, there have been many works in which various optimization methods were adopted for source localization. In this paper, we compare the performance of three typical and widely used optimization techniques for a specific MEG source localization problem. We first introduce a hybrid algorithm by combining genetic and local search strategies to overcome disadvantages of conventional genetic algorithms. Second, we apply the tabu search, a widely used optimization method in combinational optimization and discrete mathematics, to source localization. To the best of our knowledge, this is the first attempt in the literature to apply tabu search to MEG/EEG source localization. Third, in order to further compare the performance of the above algorithms, simulated annealing is also applied to MEG source localization problem. The computer simulation results show that our local genetic algorithm is the most effective approach to dipole localization, and the tabu search method is also a very good strategy for this problem.