Genetic Search Process Research Articles

Network-based hybrid genetic algorithm for scheduling in FMS environments

Scheduling in flexible manufacturing systems (FMS) must take account of the shorter lead-time, the multiprocessing environment, the flexibility of alternative workstations with different processing times, and the dynamically changing states. The best scheduling approach, as described here, is to minimize makespan t M, total flow time t F, and total tardiness penalty p T. However, in the case of manufacturing system problems, it is difficult for those with traditional optimization techniques to cope with this. This article presents a new flow network-based hybrid genetic algorithm (hGA) approach for generating static schedules in a FMS environment. The proposed method is combined with the neighborhood search technique in a mutation operation to improve the solution of the FMS problem, and to enhance the performance of the genetic search process. We update the change in swap mutation and the local search-based mutation ration. Numerical experiments show that the proposed flow network-based hGA is both effective and efficient for FMS problems.

Three-Dimensional Defect Reconstruction From Eddy-Current NDE Signals Using a Genetic Local Search Algorithm

This paper introduces a model-based approach to reconstruct three-dimensional defect profiles from eddy-current nondestructive evaluation signals. The method casts the defect characterization problem as an exercise in maximization of an appropriate cost function. The method uses an edge-based finite-element forward model to simulate the underlying physical process and a genetic search algorithm to solve the optimization (maximization) problem. The paper presents techniques to reduce the computation cost for evaluating the cost function as well as local search methods to speed the genetic search process. Test results confirm the validity of the approach.

Robust tuning of power system stabilisers using a Lyapunov method based genetic algorithm

A genetic algorithm based method is proposed to tune the parameters of a power system stabiliser (PSS). This method integrates the classical parameter optimisation approach, involving the solution of a Lyapunov equation, within a genetic search process. It also ensures that for any operating condition within a pre-defined domain, the system remains stable when subjected to small perturbations. The optimisation criterion employs a quadratic performance index that measures the quality of system dynamic response within the tuning process. The solution thus obtained is globally optimal and robust. The proposed method has been tested on two different PSS structures: the lead-lag PSS and the derivative PSS. System dynamic performances with PSS tuned using the proposed technique are highly satisfactory for different load conditions and system configurations.

Eight heuristic planning techniques applied to three increasingly difficult wildlife planning problems

As both spatial and temporal characteristics of desired future conditions are becoming important measures of forest plan success, forest plans and forest planning goals are becoming complex. Heuristic techniques are becoming popular for developing alternative forest plans that include spatial constraints. Eight types of heuristic planning techniques were applied to three increasingly difficult forest planning problems where the objective function sought to maximize the amount of land in certain types of wildlife habitat. The goal of this research was to understand the relative challenges and opportunities each technique presents when more complex difficult goals are desired. The eight heuristic techniques were random search, simulated annealing, great deluge, threshold accepting, tabu search with 1-opt moves, tabu search with 1-opt and 2-opt moves, genetic algorithm, and a hybrid tabu search / genetic algorithm search process. While our results should not be viewed as universal truths, we determined that for the problems we examined, there were three classes of techniques: very good (simulated annealing, threshold accepting, great deluge, tabu search with 1-opt and 2-opt moves, and tabu search / genetic algorithm), adequate (tabu search with 1-opt moves, genetic algorithm), and less than adequate (random search). The relative advantages in terms of solution time and complexity of programming code are discussed and should provide planners and researchers a guide to help match the appropriate technique to their planning problem. The hypothetical landscape model used to evaluate the techniques can also be used by others to further compare their techniques to the ones described here.

An Adaptive Genetic Algorithm for Manufacturing Cell Formation

An adaptive genetic approach is proposed as an effective means of providing the optimal solution to the manufacturing cell formation problem in the design of cellular manufacturing systems. The proposed approach generates the optimal formation of machine cells and part families by sequencing the rows and columns of a machine-part incidence matrix, so as to maximise the bond energy of the incidence matrix. In order to enhance the performance of the genetic search process, an adaptive scheme is adopted, so that the genetic parameters can be adjusted during the genetic search process. The effectiveness of the proposed approach is demonstrated by applying it to two numerical examples and 11 benchmark problems obtained from the literature. The computational results show that the proposed approach provides a powerful but simple means of solving the manufacturing cell formation problem and thus facilitates the design of cellular manufacturing systems.

A genetic algorithm for scheduling flexible manufacturing systems

General job shop scheduling and rescheduling with alternative route choices for an FMS environment is addressed in this paper. A genetic algorithm is proposed to derive an optimal combination of priority dispatching rules “pdrs” (independentpdrs one each for one Work Cell “WC”), to resolve the conflict among the contending jobs in the Giffler and Thompson “GT” procedure. The performance is compared with regard to makes-pan criteria and computational time. The optimal WCwise-pdr is proved to be efficient in providing optimal solutions in a reasonable computational time. Also, the proposed GA based heuristic method is extended to revise schedules on the arrival of new jobs, and on the failure of equipment to address the dynamic operation mode of flexible manufacturing systems. An iterative search technique is proposed to find the best route choice for all operations to provide a feasible and optimal solution. The applicability and usefulness of the proposed methodology for the operation and control of FMS in real-time are illustrated with examples. The scope of the genetic search process and future research directions are discussed.

A genetic algorithm-based scheduler for setup-constrained FMC

This paper presents a scheduling problem of a flexible manufacturing cell (FMC), which consists of a computer controlled machining centre, a robot, a buffer storage, a setup platform constrained with two job-mounting tables, and a rail guided vehicle. In recent years, much interest has been shown in the application of genetic algorithm (GA) in schedule generation because of its evolutionary nature. In this context, a GA has been proposed for the purpose of generating dynamic schedules that can subsequently provide a control programme for the operation of its hardware components. The proposed GA procedure has been validated by comparing it with the results of a few processing time-related priority dispatching rules. The makespan time criterion has been considered as a measure of performance in the comparative study. Further, it is indicated that this genetic search process will be very much useful in developing a learning process for the operation and control scheme.

An inductive method with genetic algorithm for learning phrase-structure-rule of natural language

This paper describes an Inductive method with genetic search which learns attribute based phraserule of natural language from set of preclassified examples. Every example is described with some attributes/values. This algorithm takes an example as a seed, generalizes it by genetic process, and makes it cover as many examples as possible. We use genetic operator in population to perform a probabilistic parallel search in rule space and it will reduce greatly possible rule search space compared with many other inductive methods. In this paper, we give description of attribute, word, dictionary and rule at first; then we describe learning algorithm and genetic search process, and at last, we give a computing method about quility of rule C(r).

Design optimization with advanced genetic search strategies

The present paper describes the capabilities of a modern design optimization tool based on the method of genetic search. This stochastic search technique offers a significantly increased probability of locating the global optimum in a design space with multiple relative optima. The program includes an advanced search technique referred to as directed crossover wherein bit positions on the design strings that offer a higher gain during crossover are assigned higher probabilities of selection as crossover sites. This optimization code also includes a multistage genetic search plan that is useful in problems where the design space is large. Multistage search involves successive refinement in the precision with which design variables are represented in the genetic search process. Also included in this program is a newly developed cluster identification technique that automatically determines the center location and the radius of a hypersphere representing a relative-optimum containing region. Cluster information serves to define accurate parameters required for other advanced techniques such as sharing function implementation and mating restrictions.