The nondominated sorting genetic algorithm with elitism (NSGA-II) is widely used due to its good performance on solving multiobjective optimization problems. In each iteration of NSGA-II, truncation selection is performed based on the rank and crowding distance of each solution. There are, however, drawbacks in this process. These drawbacks to some extent cause overlapping solutions in the popu...

Robotic systems, whether physical or virtual, must balance multiple objectives to operate effectively. Beyond performance metrics such as speed and turning radius, efficiency of movement, stability, and other objectives contribute to the overall functionality of a system. Optimizing multiple objectives requires algorithms that explore and balance improvements in each. In this paper, we evaluate...

In this paper, a simple but efficient Non-dominated Sorting Genetic Algorithm (NSGA) II based technique is proposed for optimizing the Degree of Hybridization (DOH) in parallel passenger hybrid cars. The authors’ objective is to improve performance, maximize fuel economy and at the same time, minimize mass and emissions as much as possible, by optimal selection of DOH. The NSGA-II, which is a m...

The crossover operator has always been regarded as the primary search operator in genetic algorithm (GA) because it exploits the available information from the population about the search space. Moreover, it is one of the components to consider for improving the behavior of the GA. To improve performance of GA multi parent crossover operators have been used. Multi parent crossover operators inv...

in this paper, we study different methods of solving joint redundancy-availability optimization for series-parallel systems with multi-state components. we analyzed various effective factors on system availability in order to determine the optimum number and version of components in each sub-system and consider the effects of improving failure rates of each component in each sub-system and impr...

We are interested in this paper in solving a multiobjective hybrid flowshop scheduling problem (HFS). The problem has different parameters and constraints such as release dates, due dates and sequence dependent setup times. Two different objectives should be optimized at once: the makespan and the total tardiness to be minimized. To solve the problem, we have developed two versions of a new dec...

The evolutionary approach in the design optimisation of MEMS is a novel and promising research area. The problem is of a multi-objective nature; hence, multi-objective evolutionary algorithms (MOEA) are used. The literature shows that two main classes of MOEA have been used in MEMS evolutionary design Optimisation, NSGA-II and MOGA-II. However, no one has provided a justification for using eith...

In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non-Parallel MOGAs) may fail to solve such intractable problem in a reason...

A three stage production system is considered in this paper. There are two stages to fabricate and ready the parts and an assembly stage to assembly the parts and complete the products in this system. Suppose that a number of products of different kinds are ordered. Each product is assembled with a set of several parts. At first the parts are produced in the first stage with parallel machines a...