Artificial neural networks are among the most effective learning methods currently known for certain types of problems. But BP training algorithm is based on the error gradient descent mechanism that the weight inevitably fall into the local minimum points. It is well known that simulated annealing (SA) and genetic algorithm (GA) are two global methods and can then be used to determine the opti...

Optimization is a crucial step in the analysis of experimental results. Deterministic methods only converge on local optimums and require exponentially more time as dimensionality increases. Stochastic algorithms are capable of efficiently searching the domain space; however convergence is not guaranteed. This article demonstrates the novelty of the hybrid genetic algorithm (HGA), which combine...

In this paper, a Job shop scheduling problem with a parallel assembly stage and Lot Streaming (LS) is considered for the first time in both machining and assembly stages. Lot Streaming technique is a process of splitting jobs into smaller sub-jobs such that successive operations can be overlapped. Hence, to solve job shop scheduling problem with a parallel assembly stage and lot streaming, deci...

In this paper, we propose to parallelize a Hybrid Genetic Algorithm (HGA) on Graphics Processing Units (GPUs) which are available and installed on ubiquitous personal computers. HGA extends the classical genetic algorithm by incorporating the Cauchy mutation operator from evolutionary programming. In our parallel HGA, all steps except the random number generation procedure are performed in GPU ...

Today’s logistic systems in companies depend on optimum solutions of Facility Location-Allocation (FLA) problems in order to minimize cost values the company is dealing with. Therefore, FLA plays an important role in nowadays business environment. In this paper, a Hybrid Genetic Algorithm (HGA) is proposed to solve FLA. The HGA is a combination of Genetic Algorithm and Tabu Search while NSGA II...

Flow-shop scheduling problem (FSP) deals with the scheduling of a set of jobs that visit a set of machines in the same order. The FSP is NP-hard, which means that an efficient algorithm for solving the problem to optimality is unavailable. To meet the requirements on time and to minimize the make-span performance of large permutation flow-shop scheduling problems in which there are sequence dep...

In this paper, a dynamic supply chain network (SCN) model is developed in which both dynamic facility locations and dynamic distributed quantities of materials and products are assumed. The problem is formulated mathematically using the mixed integer linear programming (MILP). The solution methodology adopted in this research is the hybrid genetic algorithm (hGA) comprising genetic algorithm (G...

Workflow scheduling is a key component behind the process for an optimal workflow enactment. It is a well-known NP-hard problem and is more challenging in the heterogeneous computing environment. The increasing complexity of the workflow applications is forcing researchers to explore hybrid approaches to solve the workflow scheduling problem. The performance of genetic algorithms can be enhance...

The design of efficient monitoring networks is very important to obtain a better understanding of environmental, ecological and epidemiological processes. In this paper we develop for the optimal design of monitoring networks a new hybrid genetic algorithm (HGA) which combines the standard genetic algorithm (GA) with a local search operator. We compare the performance of our HGA with two other ...