The multidimensional knapsack problem (MKP) is a well-known NP-hard optimization problem. Various meta-heuristic methods are dedicated to solve this problem in literature. Recently a new meta-heuristic algorithm, called artificial algae algorithm (AAA), was presented, which has been successfully applied to solve various continuous optimization problems. However, due to its continuous nature, AAA cannot settle the discrete problem straightforwardly such as MKP. In view of this, this paper proposes a binary artificial algae algorithm (BAAA) to efficiently solve MKP. This algorithm is composed of discrete process, repair operators and elite local search. In discrete process, two logistic functions with different coefficients of curve are studied to achieve good discrete process results. Repair operators are performed to make the solution feasible and increase the efficiency. Finally, elite local search is introduced to improve the quality of solutions. To demonstrate the efficiency of our proposed algorithm, simulations and evaluations are carried out with total of 94 benchmark problems and compared with other bio-inspired state-of-the-art algorithms in the recent years including MBPSO, BPSOTVAC, CBPSOTVAC, GADS, bAFSA, and IbAFSA. The results show the superiority of BAAA to many compared existing algorithms.

There is a region of pump-turbine operation, often called the S-shaped region, in which one unit rotational speed corresponds to three unit flows or torques. In this paper, the dynamic model of the pump-turbine in S-shaped regions is established by introducing the nonlinear piecewise function of relative parameters. Then, the global bifurcation diagrams of the pump-turbine are presented to analyze its dynamic characteristics in the S-shaped regions. Meanwhile, a stability criterion of runaway point is given based on the established theoretical model. The numerical experiments are conducted on the model and the results are in good agreement with

This research was focused on a comparative analysis of using LNG as a marine fuel with a conventional marine gas oil (MGO) from an environmental point of view. A case study was performed using a 50K bulk carrier engaged in domestic services in South Korea. Considering the energy exporting market for South Korea, the fuel supply chain was designed with the two largest suppliers: Middle East (LNG-Qatar/MGO-Saudi Arabia) and U.S. The life cycle of each fuel type was categorized into three stages: Well-to-Tank (WtT), Tank-to-Wake (TtW), and Well-to-Wake (WtW). With the process modelling, the environmental impact of each stage was analyzed based on the five environmental impact categorizes: Global Warming Potential (GWP), Acidification Potential (AP), Photochemical Potential (POCP), Eutrophication Potential (EP) and Particulate Matter (PM). Analysis results reveal that emission levels for the LNG cases are significantly lower than the MGO cases in all potential impact categories. Particularly, Case 1 (LNG import to Korea from Qatar) is identified as the best option as producing the lowest emission levels per 1.0 × 107 MJ of fuel consumption: 977 tonnages of CO2 equivalent (for GWP), 1.76 tonnages of SO2 equivalent (for AP), 1.18 tonnages of N equivalent (for EP), 4.28 tonnages of NMVOC equivalent (for POCP) and 26 kg of PM 2.5 equivalent (for PM). On the other hand, the results also point out that the selection of the fuel supply routes could be an important factor contributing to emission levels since longer distances for freight transportation result in more emissions. It is worth noting that the life cycle assessment can offer us better understanding of holistic emission levels contributed by marine fuels from the cradle to the grave, which are highly believed to remedy the shortcomings of current marine emission indicators.

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