The performance analysis of hyper-heuristics algorithms over examination timetabling problems

When the project is scheduled with a given set of resources, it is difficult to find the optimal solution. Resourceconstrained scheduling problems (RCPSP) are generally NP-hard. In this paper, a high level heuristic procedure “Tabu Search Algorithm (TSA)” is proposed to provide good solutions to resource-constrained, deterministic activity duration project scheduling problems. We present the application results of the computational tabu search and OPL-CPLEX algorithm and compare them with that of earlier applicable researches along with a discussion about further research. Our computational results are presented, which establish the superiority of tabu search over the existing heuristic algorithms. Two different solution strategies are also discussed, namely tabu search and OPLCPLEX exact algorithm approach which can be used with the proposed model. Due to the execution time, we have shown that OPL-CPLEX's algorithm is a valid method with medium scale RCPSPs. For the considered deterministic problems, a good agreement has been obtained between theoretical and experimental results.

The loss minimization problem is one of the most important problems to save the operational cost in distribution systems. Therefore, more efficient approaches are required to handle such a combinatorial problem. This paper presents an efficient meta-heuristic method for reconfiguration of distribution systems. A Tabu Search (TS) algorithm is used to reconfigure distribution systems so that active power losses are globally minimized with turning on / off sectionalizing switches. TS algorithm is introduced with some modifications such as using a tabu list with variable size according to the system size; this should lead to robust algorithm, and prevent cycling. Also, a random multiplicative move is used in the search process to diversify the search toward unexplored regions. A new method to check the radial topology of the system is presented. The proposed method is applied to 32-node and 69-node distribution systems. The obtained results using the proposed TS approach are compared with results obtained using Simulated Annealing (SA) approach and Parallel Tabu Search (PTS) approach in the previous work to examine the performance.

At all educational institutions, timetabling is a conventional problem that has always caused numerous difficulties and demands that need to be satisfied. For the examination timetabling problem, those matters can be defined as complexity in scheduling exam events or non-deterministic polynomial hard problems (NP-hard problems). In this study, the latest approach using an ant colony optimisation (ACO) which is the ant system (AS) is presented to find an effective solution for dealing with university exam timetabling problems. This application is believed to be an impressive solution that can be used to eliminate various types of problems for the purpose of optimising the scheduling management system and minimising the number of conflicts. The key of this feature is to simplify and find shorter paths based on index pheromone updating (occurrence matrix). With appropriate algorithm and using efficient techniques, the schedule and assignation allocation can be improved. The approach is applied according to the data set instance that has been gathered. Therefore, performance evaluation and result are used to formulate the proposed approach. This is to determine whether it is reliable and efficient in managing feasible final exam timetables for further use.

Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solution goes through two successive processes (heuristic selection and move acceptance) until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems with different characteristics. Some previous studies show that different combinations of heuristic selection and move acceptance as hyper-heuristic components might yield different performances. This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.

In this paper, we address the flowpath design issue of Automated Guided Vehicle Systems (AGVSs). In particular, we concentrate on the design of unidirectional flowpaths (i.e. vehicles are restricted to travel only in one direction along a given segment of the flowpath). We have developed intelligent heuristics — simulated annealing and tabu search algorithms for the design of unidirectional AGVSs. Different versions of simulated annealing and tabu search algorithms are implemented. Our extensive computational results indicate that both simulated annealing and tabu search yield solutions of adequate quality for all practical purposes. A tabu search implementation with the use of a frequency-based memory structure dominates all tested heuristics in terms of solution quality (i.e. percent deviation from optimality), with an impressive average performance over 45 test problems of less than 0.85% deviation from optimality.

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

2차원 저장소 적재는 NP-hard 문제로서 그 문제의 정확한 해를 구하는 것이 어려운 것으로 알려져 있으며, 이의 더 좋은 해를 얻기 위해 유전자(genetic) 알고리즘, 시뮬레이티드 어닐링(simulated annealing), 타부서치(tabu search)등과 같은 근사적 접근법이 제안되어 왔다. 하지만 분지한계(branch-and-bound)나 타부서치 기법들을 이용한 기존의 대표적인 근사 알고리즘들은 휴리스틱 알고리즘의 해에 기반을 둠으로 효율성이 낮고 반복수행에 의한 계산시간이 길다. 따라서 본 논문에서는 이러한 근사 알고리즘의 복잡성을 간소화하고, 알고리즘의 효율성을 높이기 위해 적재가능성을 판단하는 적합성 함수(fitness function)를 정의하고 이를 이용하여 어떤 특정 개체의 적재영역을 판단하는데 영향을 주는 적재영역의 수를 계산한다. 또한, 이들을 이용한 새로운 휴리스틱 알고리즘을 제안하였다. 끝으로 기존의 휴리스틱 또는 메타휴리스틱 기법과의 비교실험을 통해 기존의 휴리스틱 알고리즘인 FFF와 FBS에 비해 97%의 결과가 같거나 우수하였으며, 타부서치 알고리즘에 비해 86%의 결과가 같거나 우수한 것으로 나타났다. The two-dimensional bin packing problem(2D-BPP) has been known to be NP-hard, and it is difficult to solve the problem exactly. Many approximation methods, such as genetic algorithm, simulated annealing and tabu search etc, have been also proposed to gain better solutions. However, the existing approximation algorithms, such as branch-and-bound and tabu search, have shown the low efficiency and the long execution time due to a large of iterations. To solve these problems, we first define the fitness function to simplify and increase the utility of algorithm. The function decides whether an item is packed into a given area, and as an important information for a packing strategy, the number of subarea that can accommodate a given item is obtained from the variant of the fitness function. Then we present a heuristic algorithm BF for 2D bin packing, constructed by the fitness function and subarea. Finally, the effectiveness of the proposed algorithm will be expressed by the comparison experiments with the heuristic and the metaheuristic of the literatures. As comparing with existing heuristic algorithms and metaheuristic algorithms, it has been found that the packing rate of algorithm BP is the same as 97% as existing heuristic algorithms, FFF and FBS, or better than them. Also, it has been shown the same as 86% as tabu search algorithm or better.

This study presents two enhanced learning algorithms used for discovering probabilistic graphical models based on the Bayesian Network (BN) structure. The two heuristic structure learning algorithms, namely Tabu Search (TS) and Simulated Annealing (SA), were empirically evaluated and compared regarding efficiency. These algorithms were applied to real-life data sets for the vertebral column. A data set containing values for six biomechanical features was used to classify patients into three categories, namely, Disk Hernia (DH), Spondylolisthesis (SL), and Normal (NO), and two categories, namely, Abnormal (AB) and NO. The results indicated that SA is a more effective algorithm than TS. However, the empirical results obtained using TS indicated that the TS algorithm is promising because of its relatively simple network structure.

Tabu search with simulated annealing for solving a location–protection–disruption in hub network

Recently, the use of the particle swarm optimization (PSO) technique for the reconstruction of microwave images has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, the basic PSO algorithm is easily trapping into local minimum and may lead to the premature convergence. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes di-cult. To overcome the premature convergence of PSO, we propose a new hybrid algorithm of particle swarm optimization (PSO), simulated annealing (SA) and tabu search algorithm (TS) for solving the scattering inverse problem. The incorporation of tabu search (TS) and simulated annealing (SA) as local improvement approaches enable the hybrid algorithm to overleap local optima and intensify its search ability in local regions. Reconstructions of dielectric scatterers from experimental inverse- scattering data are flnally presented to demonstrate the accuracy and e-ciency of the hybrid technique.

This paper considers a classic model of weighted single machine scheduling problem and aims to improve it to a real-world application through fuzzy set theory. For this purpose, processing times and due dates of jobs are defined as fuzzy numbers. In the proposed model, two objectives are considered to be minimized: average tardiness and number of tardy jobs. The objectives are converted into fuzzy statement through fuzzy arithmetic. Because the problem is NP-hard, it is proposed to solve the model through three well-known meta-heuristic algorithms as Simulated Annealing, Tabu Search, and Genetic Algorithm albeit with some modifications. Comparative analysis of algorithms is attainable through different experimented results on generated benchmark problems with different sizes and difficulties. Efficiency of the developed algorithms is analyzed on the different versions of the model which come from the assumption of different parameters and/or components. Algorithms' behaviors to managerial insights are also considered in some sensitivity analysis experiments. The obtained results show the applicability of the proposed model in real-world scheduling problems.

In this study, the objective of minimizing makespan has been considered for a scheduling problem of identical parallel machines with a single server and unavailability constraints. The unavailability constraints correspond to preventive maintenance periods. In this study, the jobs and the maintenance periods are scheduled simultaneously. This scheduling problem has a wide range of potential application areas in the manufacturing environment. In addition, the studied problem is a challenging one from theoretical point of view, due to its NP-Hardness. To conduct the study, a lower bound ( LB ) for the problem, and three metaheuristics namely Simulated Annealing ( SA ), Tabu Search ( TS ), and Genetic Algorithm ( GA ) have been proposed. The best parameters settings of the proposed algorithms were conducted using pilot runs with a Taguchi design. The algorithms performance has been assessed by using a set of test problems generated randomly. These test problems are based on a literature benchmark. The size of the instances, or number of jobs, were up to 500. Along with the performance analysis of the proposed algorithms, the effect of varying processing times and unavailability periods on the performance of the proposed algorithms is studied. The present work provides strong evidence of the efficiency and the performance of the proposed algorithms.

Submission of information relating to college scheduling in college is very important and is usually done on a semester by the academic section. One of these universities is Universitas Kompueter Indonesia (Unikom) which became object of case study. In determining the scheduling of many aspects that must be considered include the components of the course, lecturer, class, space, and time. All of these components are considered so that the schedule of lectures that will be formed does not happen clashing with each other. In addition, scheduling problems will be more complex if there are many additional terms or constraints. In this study aims to be able to make lecture class scheduling replacement with a combination of genetic algorithms and tabuo search. The first step is done by separating the hard and soft constraints that exist in the lecture problem, and then the best individual search is done by cross-over or cross-breeding process to produce new individual and perform the process of mutation or exchange of individual position in order to produce the best fitness value of the previous individual. The advantage of using a tabu search algorithm allows for the absence of duplicate individual processes. Therefore, the combination of these two algorithms is capable of producing good scheduling and the clashing rate of a schedule can be handled.

A double evolutionary pool memetic algorithm is proposed to solve the examination timetabling problem. To improve the performance of the proposed algorithm, two evolutionary pools, that is, the main evolutionary pool and the secondary evolutionary pool, are employed. The genetic operators have been specially designed to fit the examination timetabling problem. A simplified version of the simulated annealing strategy is designed to speed the convergence of the algorithm. A clonal mechanism is introduced to preserve population diversity. Extensive experiments carried out on 12 benchmark examination timetabling instances show that the proposed algorithm is able to produce promising results for the uncapacitated examination timetabling problem.

Tabu search is one of the most effective heuristics for locating high-quality solutions to a diverse array of NP-hard combinatorial optimization problems. Despite the widespread success of tabu search, researchers have a poor understanding of many key theoretical aspects of this algorithm, including models of the high-level run-time dynamics and identification of those search space features that influence problem difficulty. We consider these questions in the context of the job-shop scheduling problem (JSP), a domain where tabu search algorithms have been shown to be remarkably effective. Previously, we demonstrated that the mean distance between random local optima and the nearest optimal solution is highly correlated with problem difficulty for a well-known tabu search algorithm for the JSP introduced by Taillard. In this paper, we discuss various shortcomings of this measure and develop a new model of problem difficulty that corrects these deficiencies. We show that Taillard's algorithm can be modeled with high fidelity as a simple variant of a straightforward random walk. The random walk model accounts for nearly all of the variability in the cost required to locate both optimal and sub-optimal solutions to random JSPs, and provides an explanation for differences in the difficulty of random versus structured JSPs. Finally, we discuss and empirically substantiate two novel predictions regarding tabu search algorithm behavior. First, the method for constructing the initial solution is highly unlikely to impact the performance of tabu search. Second, tabu tenure should be selected to be as small as possible while simultaneously avoiding search stagnation; values larger than necessary lead to significant degradations in performance.