Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a multi-objective genetic algorithm (MOGA) based on immune and entropy principle to solve the multiobjective FJSP. In this improved MOGA, the fitness scheme based on Pareto-optimality is applied, and the immune and entr...

Testing of multi-threaded programs is a demanding work due to the many possible thread interleavings one should examine. The noise injection technique helps to increase the number of thread interleavings examined during repeated test executions provided that a suitable setting of noise injection heuristics is used. The problem of finding such a setting, i.e., the so called test and noise config...

Emerging network applications require the delivery of packets from one or more senders to a group of receivers. For this reason QoS Multicast Routing Algorithm is a key problem for the researchers. This paper shows a QoS Multicast Genetic Algorithm (GA) that is applied to a multi-layer wireless system composed of a GEO DVB-RCS Satellite and a set of High Altitude Platforms (HAPs). This architec...

The dyeing of textile materials is the most critical process in cloth production because of the strict technological requirements. In addition to the technical aspect, there have been increasing concerns over how to minimize the negative environmental impact of the dyeing industry. The emissions of pollutants are mainly caused by frequent cleaning operations which are necessary for initializing...

The high computational cost of population based optimization methods, such as multiobjective genetic algorithms (MOGAs), has been preventing applications of these methods to realistic engineering design problems. The main challenge is to devise methods that can significantly reduce the number of simulation (objective/constraint functions) calls. We present a new multi-objective design optimizat...

Evolutionary algorithms are considered more efficient for optimal system design because they can provide higher opportunity for obtaining the global optimal solution. This paper introduces a method for construct and train Recurrent Neural Networks (RNN) by means of Multi-Objective Genetic Algorithms (MOGA). The use of a multi-objective evolutionary algorithm allows the definition of many object...

Obtaining a fullest possible representation of solutions to a multiobjective optimization problem has been a major concern in Multi-Objective Genetic Algorithms (MOGAs). This is because a MOGA, due to its very nature, can only produce a discrete representation of Pareto solutions to a multiobjective optimization problem that usually tend to group into clusters. This paper presents a new MOGA, o...

Optimizing current conveyors (CC) through the multi objective genetic algorithm (MOGA) is presented in this brief. Performances of a positive second generation CC are optimized, namely the high cutoff frequency and the Xport parasitic resistance. The optimized CC is used as a building block to design a high performance current mode filter. Spice simulation results are presented to show good rea...

The NBI-NLPQLP optimization method is tested on several multi-objective optimization problems. Its performance is compared to that of MOGA-II: since NBI-NLPQLP is based on the classical gradientbased NLPQLP, it is fast and accurate, but not as robust, in comparison with the genetic algorithm. Furthermore a discontinuous Pareto frontier can give rise to problems in the NBI’s convergence. In orde...

In a just-in-time (JIT) system, kanban number and size represent the inventory level of work-in-process (WIP) or purchasing parts. It is an important issue to determine the feasible kanban number and size. In this research, an integrated multiple-objective genetic algorithm (MOGA) based system is developed to determine the Pareto-optimal kanban number and size, and is applied in a JIT-oriented ...