Reactive Tabu Search Research Articles

An application of reactive tabu search for service restoration in distribution systems and its comparison with the genetic algorithm and parallel simulated annealing

Service restoration in distribution systems can be formulated as a combinatorial optimization problem. It is the problem to determine power sources for each load considering various operational constraints in distribution systems. Up to now, the problem has been dealt with using conventional methods such as the branch and bound method, expert systems, neural networks, and fuzzy reasoning. Recently, modern heuristic methods such as genetic algorithms (GA), simulated annealing (SA), and tabu search (TS) have been attracting notice as efficient methods for solving large combinatorial optimization problems. Moreover, reactive tabu search (RTS) can solve the parameter tuning problem, which is recognized as the essential problem of the TS. Therefore, RTS, GA, and SA can be efficient search methods for service restoration in distribution systems. This paper develops an RTS for service restoration and compares RTS, GA, and PSA (parallel SA) for the problem. The feasibility of the proposed methods is shown and compared on a typical distribution system model with promising results. © 2000 Scripta Technica, Electr Eng Jpn, 133(3): 71–82, 2000

A particle swarm optimization for reactive power and voltage control considering voltage security assessment

Summary form only given, as follows. This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an online VVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values of generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. The method considers voltage security using a continuation power now and a contingency analysis technique. The feasibility of the proposed method is demonstrated and compared with reactive tabu search (RTS) and the enumeration method on practical power system models with promising results.

Solving the pickup and delivery problem with time windows using reactive tabu search

The pickup and delivery problem with time windows requires that a group of vehicles satisfy a collection of customer requests. Each customer request requires the use of a single vehicle both to load a specified amount of goods at one location and to deliver them to another location. All requests must be performed without violating either the vehicle capacity or the customer time window stipulated at each location. In this paper, we present a reactive tabu search approach to solve the pickup and delivery problem with time windows using three distinct move neighborhoods that capitalize on the dominance of the precedence and coupling constraints. A hierarchical search methodology is used to dynamically alternate between neighborhoods in order to negotiate different regions of the solution space and adjust search trajectories. In order to validate our technique's effectiveness, we have constructed a new set of benchmark problems for the pickup and delivery problem with time windows based on Solomon's benchmark vehicle routing problem with time windows data sets. Computational results substantiate the solution quality and efficiency of our reactive tabu search approach.

Reactive local search techniques for the maximum k-conjunctive constraint satisfaction problem ( MAX-k-CCSP)

In this paper the performance of the Hamming-based Reactive Tabu Search algorithm (H-RTS) previously proposed for the Maximum Satisfiability problem is studied for the different Maximum k-Conjunctive Constraint Satisfaction problem. In addition, the use of non-oblivious functions recently proposed in the framework of approximation algorithms is investigated. In particular, two relevant special cases of the Maximum k-Conjunctive Constraint Satisfaction problem are considered: Maximum Directed Cut and Maximum Independent Set in cubic graphs. The preliminary diversification-bias analysis of the basic components shows a remarkable difference between the two problems, and the derived predictions are then validated by extensive experiments with the complete H-RTS algorithm. The performance of H-RTS is compared with that of Simulated Annealing and simple Repeated Local Search.

Ant colonies for the quadratic assignment problem

This paper presents HAS–QAP, a hybrid ant colony system coupled with a local search, applied to the quadratic assignment problem. HAS–QAP uses pheromone trail information to perform modifications on QAP solutions, unlike more traditional ant systems that use pheromone trail information to construct complete solutions. HAS–QAP is analysed and compared with some of the best heuristics available for the QAP: two versions of tabu search, namely, robust and reactive tabu search, hybrid genetic algorithm, and a simulated annealing method. Experimental results show that HAS–QAP and the hybrid genetic algorithm perform best on real world, irregular and structured problems due to their ability to find the structure of good solutions, while HAS–QAP performance is less competitive on random, regular and unstructured problems.

電圧信頼度を考慮したParticle Swarm Optimizationによる電圧無効電力制御方式の検討

Summary form only given, as follows. This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an online VVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values of generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. The method considers voltage security using a continuation power now and a contingency analysis technique. The feasibility of the proposed method is demonstrated and compared with reactive tabu search (RTS) and the enumeration method on practical power system models with promising results.

Searching the Higgs with the neurochip Totem

We show that neural network classifiers can be helpful in discriminating Higgs production events from the huge background at LHC, assuming the case of a mass value M H ∼ 200 GeV. We use the high performance neurochip TOTEM, trained by the Reactive Tabu Search algorithm (RTS), which could be used for on-line purposes. Two different sets of input variables are compared.

Reactive Tabu Search and Sensor Selection in Active Structural Acoustic Control Problems

A Reactive Tabu Search (RTS) is examined. In addition to a dynamic tabu tenure RTS also detects when the search has entered an unproductive area and restarts RTS based on distinctive features of the unproductive area. We explore the effectiveness of RTS over a static tabu list (of a kind used in many implementations) for a two variable unconstrained discrete optimization model with 9 nearly identical minima and 513 other local minima. One of the key features of this problem is that the two-dimensional domain allows us to provide graphical descriptions of the performance of RTS. We then apply RTS to a sensor selection problem in active structural acoustic control. The objective in this problem is to select a set of 8 sensors out of 462 potential sensor locations so that the noise measured at the 8 chosen sensors is as close as possible to the noise measured at all 462. Computational experiments for data taken from a laboratory test article at NASA Langley Research Center are provided.

Tabu Search metaheuristics for global optimization of electromagnetic problems

This paper presents a Tabu Search based strategy which has been applied to optimize the objective function associated to the design of a solenoids system. For this kind of problem, Tabu Search strategies are able to find the global minimum with considerably better performance than that obtained using a Simulated Annealing algorithm. Algorithms used to perform the self-tuning of Tabu Search parameters have also been developed, achieving interesting improvements in terms of computing time and quality of the solutions. Such algorithms are an original development of Reactive Tabu Search and they are based on the past history of the search. Tabu Search strategies have also been applied in continuous optimization of the same problem with encouraging results.

A Reactive Tabu Search Metaheuristic for the Vehicle Routing Problem with Time Windows

This article develops a reactive tabu search metaheuristic for the vehicle routing and scheduling problem with time window constraints. Reactive tabu search dynamically varies the size of the list of forbidden moves to avoid cycles as well as an overly constrained search path. Intensification and diversification strategies are examined as ways to achieve higher quality solutions. The λ-interchange mechanism of Osman is used as the neighborhood structure for the search process. Computational results on test problems from the literature as well as large-scale real-world problems are reported. The metaheuristic achieves solutions that compare favorably with previously reported results.

Role of neural networks in the search of the Higgs boson at LHC

We show that neural network classifiers can be helpful to discriminate Higgs production from background at LHC in the Higgs mass range M= 200 GeV. We employ a common feed-forward neural network trained by the backpropagation algorithm for off-line analysis and the neural chip Totem, trained by the Reactive Tabu Search algorithm, which could be used for on-line analysis.

Solving the Traveling-Salesman Problem with Time Windows Using Tabu Search

This paper presents a robust tabu search approach to the traveling-salesman problem with time windows. The approach uses a two-level tour hashing scheme within a reactive tabu search procedure to detect unique repeated solutions and to promote a more diverse search. Strong feasibility conditions restrict the neighborhood of candidate solutions. The search returns optimal, or near-optimal, solutions within a fraction of the time required by optimal methods. The algorithm’s search neighborhood is not restricted to feasible solutions, and superior marginally infeasible solutions are reported to enhance the decision making process. The paper presents computational results for 145 problems from the literature.

The continuous reactive tabu search: Blending combinatorial optimization and stochastic search for global optimization

A novel algorithm for the global optimization of functions (C-RTS) is presented, in which a combinatorial optimization method cooperates with a stochastic local minimizer. The combinatorial optimization component, based on the Reactive Tabu Search recently proposed by the authors, locates the most promising “boxes”, in which starting points for the local minimizer are generated. In order to cover a wide spectrum of possible applications without user intervention, the method is designed with adaptive mechanisms: the box size is adapted to the local structure of the function to be optimized, the search parameters are adapted to obtain a proper balance of diversification and intensification. The algorithm is compared with some existing algorithms, and the experimental results are presented for a variety of benchmark tasks.

TOTEM: A HIGHLY PARALLEL CHIP FOR TRIGGERING APPLICATIONS WITH INDUCTIVE LEARNING BASED ON THE REACTIVE TABU SEARCH

The training of a Multi-Layer Perceptron (MLP) classifier is considered as a Combinatorial Optimization task and solved using the Reactive Tabu Search (RTS) method. RTS needs only forward passes (no derivatives) and does not require high precision network parameters. TOTEM, a special-purpose VLSI chip, was developed to take advantage of the limited memory and processing requirements of RTS: the final system effects a very close match between hardware and training algorithm. The RTS algorithm and the design of TOTEM are discussed, together with the operational characteristics of the VLSI chip and some preliminary training and generalization tests on triggering tasks.

Local search with memory: benchmarking RTS

The purpose of this work is that of presenting a version of the Reactive Tabu Search method (RTS) that is suitable for constrained problems, and that of testing RTS on a series of constrained and unconstrained Combinatorial Optimization tasks. The benchmark suite consists of many instances of the N-K model and of the Multiknapsack problem with various sizes and difficulties, defined with portable random number generators. The performance of RTS is compared with that of Repeated Local Minima Search, Simulated Annealing, Genetic Algorithms, and Neural Networks. In addition, the effects of differenthashing schemes and of the presence of a simple “aspiration” criterion in the RTS algorithm are investigated.

Training neural nets with the reactive tabu search

In this paper the task of training subsymbolic systems is considered as a combinatorial optimization problem and solved with the heuristic scheme of the reactive tabu search (RTS). An iterative optimization process based on a "modified local search" component is complemented with a meta-strategy to realize a discrete dynamical system that discourages limit cycles and the confinement of the search trajectory in a limited portion of the search space. The possible cycles are discouraged by prohibiting (i.e., making tabu) the execution of moves that reverse the ones applied in the most recent part of the search. The prohibition period is adapted in an automated way. The confinement is avoided and a proper exploration is obtained by activating a diversification strategy when too many configurations are repeated excessively often. The RTS method is applicable to nondifferentiable functions, is robust with respect to the random initialization, and effective in continuing the search after local minima. Three tests of the technique on feedforward and feedback systems are presented.

Simulated annealing and Tabu search in the long run: A comparison on QAP tasks

Simulated Annealing (SA) and Tabu Search (TS) are compared on the Quadratic Assignment Problem. A recent work on the same benchmark suite argued that SA could achieve a reasonable solution quality with fewer function evaluations than TS. The discussion is extended by showing that the conclusions must be changed if the task is hard or a very good approximation of the optimal solution is desired, or if CPU time is the relevant parameter. In addition, a recently proposed version of TS (the Reactive Tabu Search) solves the problem of finding the proper list size with an automatic memory-based reaction mechanism.