Genetic algorithms vs. Tabu search in timetable scheduling

The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.

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.

In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA) and tabu search (TS) is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy.

The combination of principal component analysis, genetic algorithm and tabu search in 3d molecular similarity

Hybrid genetic algorithm and tabu search for finite capacity material requirement planning system in flexible flow shop with assembly operations

Two heuristic techniques, the genetic algorithm (GA) and Tabu search (TS), both with an embedded linear programming routine for earthwork allocation, were compared to a manually designed forest road profile. The manually designed road length was 345.7m and its average gradient was 14.1%. The best costs of the profiles designed by GA and TS, without changing the placement of control points, were less than that designed manually. The best cost found by GA was almost the same as the global optimum solution. While TS could not find a better solution than GA, it usually found a good solution in less time. It was not possible to search all alternatives by changing the placement of control points and find the global optimum solution within a reasonable time. However, it can be concluded from the results that both GA and TS found good solutions within a reasonable time. Since it is not possible to manually evaluate many alternatives, road designers should find heuristic techniques helpful for design of the road profile. Moreover, the effect of the number of control points on construction costs was examined. The results indicated that increasing the number of control points reduces the construction costs. However, driving safety and comfort might be decreased.

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

The scheduling problem is a typical time table problem in educational administration. For such a NP complete problems, when the genetic algorithm solves this problem, it has precociousness phenomenon and quickly converges not to the global optimal solution but to the local optimal solution. Therefore, we use the advantage of local search method and partial matching crossover operation to solve TTP problem with the genetic algorithm combining search algorithm. We do a lot of experiments and evaluate the performance of the improved genetic algorithm. The experiment results show that genetic search algorithm is a more superior algorithm to apply to the TTP problem.

A Petri net with controller is used to model discrete events in flexible job shop scheduling, the objective of scheduling problems is to minimize make-span, the scheduling results is obtained based on genetic and Tabu Search (TS) algorithm. It is proved capable of providing optimized schedule to the job-shop where the machine tool and manpower resources are both constrained. After crossover and mutation operations, an optimal or suboptimal scheduling plan can be found. The result of the test shows that this method is feasible and efficient. Job shop scheduling is the primary content and critical technology of the production management of CIMS, in the past during 30 years, a lot of researchers and results focus on it, but most of them deal with single objective and resource scheduling problems which are fixed processing route and constrained by machine, and production period is its optimal objective. Nevertheless, the production styles are variable and processing route is determined under the special condition. Moreover, job shop scheduling problem is the most difficult combined constraints optimal problem and typical NP hard problem which can't be solved in polynomial time by an effective algorithm. Up to date, there are some traditional algorithms to solve JSP problem (1-2). About paper (3), Petri net is used to construct JSP model and L1 which combines branch and bound algorithm with dynamic program is used to search its optimal solution. And paper (4), the job sequence is assigned by genetic code firstly, and then filtered beam search which based on breadth-first algorithm is adopted to search. In recent years, the Petri net as a discrete event dynamic system modeling and analysis tool(5), has been successfully applied to the flexible manufacturing system modeling, analysis and control. Some researchers put forward to be based on Petri network scheduling method, but only on the production line itself can not reflect the modeling, as in the production line of external control scheduling strategy influence. A hybrid algorithm is proposed to solve dual-resource scheduling problem in flexible production environment, and Petri net model with a controller, which can effectively model control. It combines the advantage of global search ability of GA with the self-adaptive merit of Tabu Search (TS) and improves its convergence. It is proved capable of global and part searching ability and efficient.

Most scheduling problems require either exponential time or space to generate an optimal answer [7]. Instruction scheduling is an instance of a general scheduling problem and Dewitt [8] uses this fact to show instruction scheduling is a NP-complete problem. This paper applies Genetic Algorithms, Tabu Search, and list scheduling to the instruction scheduling problem and compares the results obtained by each.

The Job shop scheduling problem with generic time lags is a generalization of the job shop problem. It is defined as a job shop problem with minimal and maximal delays between starting times of operations of different jobs. In this paper, we propose a combination of genetic algorithm and tabu search to solve Job Shop Problem with Generic Time Lags. Benchmark instances are used to investigate the performance of the proposed metaheuristic. The results show that the proposed combination of metaheuristics improves the efficiency.

The timetabling problem is well known to be an NP-complete combinatorial problem. The problem becomes even more complex when addressed to individual timetables of students. The core of dealing with the problem in this application is a timetable builder based on mixed direct-indirect encoding evolved by a genetic algorithm with a self-adaptation paradigm, where the parameters of the genetic algorithm are optimized during the same evolution cycle as the problem itself. The aim of this paper is to present an encoding for self-adaptation of genetic algorithms that is suitable for timetabling problems. Compared to previous approaches we designed the encoding for self-adaptation of not only one parameter or several ones but for all possible parameters of genetic algorithms at the same time. The proposed self-adaptive genetic algorithm is then applied for solving the real university timetabling problem and compared with a standard genetic algorithm. The main advantage of this approach is that it makes it possible to solve a wide range of timetabling and scheduling problems without setting parameters for each kind of problem in advance. Unlike common timetabling problems, the algorithm was applied to the problem in which each student has an individual timetable, so we also present and discuss the algorithm for optimized enrollment of students that minimizes the number of clashing constraints for students.

Scheduling problems in real systems often require sequence-dependent setup times. The topic of sequence-dependent setup times has not been addressed adequately, and improved competitiveness is thus not achieved. This study proposes a hybrid approach that takes advantage of simulated annealing (SA) and tabu search (TS) to solve single-machine tardiness problems with sequence-dependent setup times. To verify the proposed approach, experiments were conducted on benchmark problem sets that included both the weighted and un-weighted tardiness problems. The results show that the performance of the hybrid approach is superior to that of the SA, genetic algorithm, TS and ant colony optimization approaches, and is comparable with the Tabu-VNS approach. And the proposed approach found new upper bound values for many benchmark problems with an acceptable computational time.

A Neurogenetic approach for the resource-constrained project scheduling problem

Three heuristic search methods, Genetic Algorithm, Simulated Annealing and Tabu Search are implemented to invert Rayleigh wave phase velocity for shallow S-wave velocity profiling in seismic surface wave surveying. Unlike linearized least-squares inversion, they do not require derivative calculation, or an initial model, only the forward modeling calculation. In this study, the performances of the three heuristic techniques are compared with numerical experiments. With common paramerization and search limits, the three algorithms can accurately reconstruct shallow S-wave profiles with and without a velocity reversal from the synthetic data without any specific initial models. The Genetic Algorithms and the Simulated Annealing show fast convergence. However, the Simulated Annealing can find models with the smallest misfits. It is also found that the decrease of the misfits is the most gradual in the Tabu Search.