despite the growing use of evolutionary multi-objective optimization algorithms in different categories of science, these algorithms as a powerful tool in portfolio optimization and specially solving multi-objective portfolio optimization problem is still in its early stages. in this paper, moeas have been used for solving multi-objective portfolio optimization problem in tehran stock market. f...

in this study the performance and emissions characteristics of a heavy-duty, direct injection, compression ignition (ci) engine which is specialized in agriculture, have been investigated experimentally. for this aim, the influence of injection timing, load, engine speed on power, brake specific fuel consumption (bsfc), peak pressure (pp), nitrogen oxides (nox), carbon dioxide (co2), carbon mon...

Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of EAF-based steel-making process based on multi-objective using evolutionary computing machine learning. Using large amount real-world historical data containing 6423 consecutive EAF heats collected from melt shop in an established steel plant work not on...

During last decades, developing multi-objective evolutionary algorithms for optimization problems has found considerable attention. Flexible job shop scheduling problem, as an important scheduling optimization problem, has found this attention too. However, most of the multi-objective algorithms that are developed for this problem use nonprofessional approaches. In another words, most of them c...