Island Model Parallel Genetic Algorithm Research Articles

Optimal Sensors/Actuators Placement in Smart Structure Using Island Model Parallel Genetic Algorithm

Determination of optimal placements of sensors/actuators in large structures is a difficult job as large number of possible combinations leads to a very high computational time and storage. Therefore, this kind of optimization problem demands a parallel implementation of the optimization schemes. Island model genetic algorithm (GA) being inherently parallel has been used for searching optimal placements of collocated sensors/actuators. Numerical simulations have been done for determination of optimal placements of collocated PZT sensors and actuators in smart fiber reinforced shell structures using island model parallel GA (IMPGA) in conjunction with electro-mechanical finite element analysis with an objective of maximizing the controllability index. It has been observed that the present IMPGA-based formulation (due to its migration scheme) not only makes it possible to determine optimal sensors/actuators locations for large structures but also leads to a better solution at a much reduced and achievable computational time. Results from scalability analysis also show that the efficacy of the present method of using IMPGA for determination of optimal sensors/actuators location based on FEA will be more pronounced when actually used for real life problems requiring large number of sensors and actuators.

Context-Dependent Fuzzy Systems With Application to Time-Series Prediction

In this paper, we introduce an implementation of a fuzzy system whose parameters are mutable according to the context. The construction of the system is done via two steps. First, we build a based type-1 Takagi-Sugeno-Kang (TSK) fuzzy system whose membership functions will be later adjusted to the situation by means of contextual transformation to reflect the influence of context in the interpretation of fuzzy sets. Second, an iterative algorithm is performed to identify the transformation matrix, which is used to scale the membership functions of the reference-based fuzzy sets in each of the contexts. The identification of the premise part of the based fuzzy system is performed via a combination of an island model parallel genetic algorithm and a space search memetic algorithm, while the identification of the consequent parameters of the system is done via an improved QR Householder least-squares method. The proposed system is evaluated using the well-known Mackey-Glass time-series prediction benchmark dataset and has shown better accuracy than any other previous works concerning the same problem.

Jobshop lot streaming with routing flexibility, sequence-dependent setups, machine release dates and lag time

Lot streaming is a technique of splitting production lots into smaller sublots in a multi-stage manufacturing system so that operations of a given lot can overlap. This technique can reduce the manufacturing makespan and is an effective tool in time-based manufacturing. Research on lot streaming models and solution procedures for flexible jobshops has been limited. The flexible jobshop scheduling problem is an extension of the classical jobshop scheduling problem by allowing an operation to be assigned to one of a set of eligible machines during scheduling. In this paper we develop a lot streaming model for a flexible jobshop environment. The model considers several pragmatic issues such as sequence-dependent setup times, the attached or detached nature of the setups, the machine release date and the lag time. In order to solve the developed model efficiently, an island-model parallel genetic algorithm is proposed. Numerical examples are presented to demonstrate the features of the proposed model and compare the computational performance of the parallel genetic algorithm with the sequential algorithm. The results are very encouraging.

Optimization of FRP composites against impact induced failure using island model parallel genetic algorithm

The present paper deals with optimization of hybrid fiber reinforced plastic (FRP) laminated plates subjected to impact loading using island model parallel genetic algorithm (IMPGA). Finite element method (FEM) and IMPGA have been used to obtain optimum laminates in terms of minimizing the cost, weight or both cost and weight of Graphite/Epoxy (T300/5208)–Aramid/Epoxy (Kevlar 49) hybrid laminates. Impact induced delamination and matrix cracking have been used as failure criteria for the optimization of laminate. Fiber orientation, material and thickness in each lamina as well as number of lamina in the laminate have been used as design variables. Multi-objective approach has been used to achieve the optimum design of a laminate for combined normalized weighted cost and weight minimization. The results obtained from the integrated module show that IMPGA with FEM can lead to a global optimal solution for both single as well as multiple objective functions. It has been observed that better convergence of objective function could be obtained using IMPGA in comparison to sequential GA (SGA). Further, a significant reduction in computational time has been observed with IMPGA in comparison to SGA and the computational time decreases with increased number of processors.

A hybrid genetic algorithm for the job shop scheduling problems

The Job Shop Scheduling Problem (JSSP) is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on genetics algorithm to address JSSP. We design a scheduling method based on Single Genetic Algorithm (SGA) and Parallel Genetic Algorithm (PGA). In the scheduling method, the representation, which encodes the job number, is made to be always feasible, the initial population is generated through integrating representation and G&T algorithm, the new genetic operators and selection method are designed to better transmit the temporal relationships in the chromosome, and island model PGA are proposed. The scheduling methods based on genetic algorithm are tested on five standard benchmark JSSP. The results are compared with other proposed approaches. Compared to traditional genetic algorithm, the proposed approach yields significant improvement in solution quality. The superior results indicate the successful incorporation of a method to generate initial population into the genetic operators.

The Backboard Wiring Problem: A Placement Algorithm

The Backboard Wiring Problem: A Placement Algorithm