The gaining returns in line with risks is always a major concern for market play-ers. This study compared the selection of stock portfolios based on the strategy of buying and retaining winning stocks and the purchase strategy based on the level of investment risks. In this study, the two-step optimization algorithms NSGA-II and SPEA-II were used to optimize the stock portfolios. In order to de...

An improved harmony search algorithm for constrained multi-objective optimization problems is proposed in this paper. Inspired by Particle Swarm Optimization, an inductor particle is introduced to speed up the convergence rate of the CMOHS. Two populations are adopted to increase the opportunity of finding the optimal solutions. Numerical experiments are divided into two parts: the first one co...

An improved harmony search algorithm for constrained multi-objective optimization problems is proposed in this paper. Inspired by Particle Swarm Optimization, an inductor particle is introduced to speed up the convergence rate of the CMOHS. Two populations are adopted to increase the opportunity of finding the optimal solutions. Numerical experiments are divided into two parts: the first one co...

Railway electrification has attracted substantial interest in recent years as a key part of the global effort to achieve transport decarbonization. To improve energy efficiency train operations, particular is optimization speed trajectories. However, most studies formulate problem single-objective model and do not take into account mass uncertainty associated with passenger load variations. Thi...

To meet the increasing computational demands, geographically distributed resources need to be logically coupled to make them work as a unified resource. In analyzing the performance of such distributed heterogeneous computing systems scheduling a set of tasks to the available set of resources for execution is highly important. Task scheduling being an NP-complete problem, use of metaheuristics ...

Classical evolutionary multi-objective optimization algorithms aim at finding an approximation of the entire set of Pareto optimal solutions. By considering the preferences of a decision maker within evolutionary multi-objective optimization algorithms, it is possible to focus the search only on those parts of the Pareto front that satisfy his/her preferences. In this paper, an extended prefere...

nowadays, the subject of vision metrology network design is local enhancement of the existing network. in the other words, it has changed from first to third order design concept. to improve the network, locally, some new camera stations should be added to the network in drawback areas. the accuracy of weak points is enhanced by the new images, if the related vision constraints are satisfied si...

Context: The Next Release Problem involves determining the set of requirements to implement in the next release of a software project. When the problem was first formulated in 2001, Integer Linear Programming, an exact method, was found to be impractical because of large execution times. Since then, the problem has mainly been addressed by employing metaheuristic techniques. Objective: In this ...

NSGA ( [5]) is a popular non-domination based genetic algorithm for multiobjective optimization. It is a very effective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter σshare. A modified version, NSGAII ( [3]) was developed, which has a better sorting algorithm , incorporates elitism...

In this paper, we have studied a multiobjective hybrid flowshop scheduling problem where n independent jobs should be executed in a hybrid assembly line. The aim of our work is to optimize the makespan and the total tardiness of the whole production. A simulation based optimization algorithm is proposed here to solve this problem. It is a combination of the simulation software ARENA and the FLC...