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Abstract Recently, a new negative learning variant of ant colony optimization (ACO) has been used to successfully tackle a range of combinatorial optimization problems. For providing stronger evidence of the general applicability of negative learning ACO, we investigate how it can be adapted to solve the Maximum Satisfiability problem (MaxSAT). The structure of MaxSAT is different from the problems considered to date and there exists only a few ACO approaches for MaxSAT. In this paper, we describe three negative learning ACO variants. They differ in the way in which sub-instances are solved at each algorithm iteration to provide negative feedback to the main ACO algorithm. In addition to using IBM ILOG CPLEX, two of these variants use existing MaxSAT solvers for this purpose. The experimental results show that the proposed negative learning ACO variants significantly outperform the baseline ACO as well as IBM ILOG CPLEX and the two MaxSAT solvers. This result is of special interest because it shows that negative learning ACO can be used to improve over the results of existing solvers by internally using them to solve smaller sub-instances.

Ignition timing and output voltage must be re-tuned when the engine used different types of fuel. This research aimed to investigate the effectiveness of CDI and ignition coil upgrade, both separately and combined, when various types of fuel was used by the engine. This research was done on a 160 cc four-stroke engine of a Honda motorcycle with compression ratio 9:1. The types of fuel used by the engine in this research was RON 88, RON 90, and RON 95 gasoline fuels. The engine’s torque was measured on an inertial type motorcycle chassis dynamometer. The fuel consumption data was obtained by on road test, the value of the data was calculated by dividing the distance being traveled with the volume of fuel being consumed. The result of this research showed that when RON 88 gasoline was used, the influence of aftermarket CDI and aftermarket ignition coil gave almost comparable impacts to the increase of torque. The combination of aftermarket CDI and aftermarket ignition coil was found to influence the largest torque increase percentage of 5.3% when RON 90 gasoline was used. When RON 95 gasoline was used, the aftermarket ignition coil influenced the largest torque increase percentage of 4.3% at lower engine speed and 7.4% at higher engine speed. The aftermarket CDI and aftermarket ignition coil, whether used separately or combined, always gave worse impact to fuel consumption when RON 88 and RON 90 gasoline was used. The opposite is true when RON 95 was used.

Ant colony optimization is a metaheuristic that is mainly used for solving hard combinatorial optimization problems. The distinctive feature of ant colony optimization is a learning mechanism that is based on learning from positive examples. This is also the case in other learning-based metaheuristics such as evolutionary algorithms and particle swarm optimization. Examples from nature, however, indicate that negative learning—in addition to positive learning—can beneficially be used for certain purposes. Several research papers have explored this topic over the last decades in the context of ant colony optimization, mostly with limited success. In this work we present and study an alternative mechanism making use of mathematical programming for the incorporation of negative learning in ant colony optimization. Moreover, we compare our proposal to some well-known existing negative learning approaches from the related literature. Our study considers two classical combinatorial optimization problems: the minimum dominating set problem and the multi dimensional knapsack problem. In both cases we are able to show that our approach significantly improves over standard ant colony optimization and over the competing negative learning mechanisms from the literature.

Airfoil performance data is regarded as highly valuable data in its wide area of application since it will help the designer, for examples; to predict the influence of rear spoiler mounting to the maneuverability and top speed of racing car, the thrust and fuel consumption needed for an aircraft to take off and cruise, or the power delivered by a wind turbine rotor at certain wind speed. The performance of airfoil, which is measured with its lift and drag coefficient, is greatly affected by its geometry and operating condition. The influence of various geometric and operational parameters to the airfoil performance is needed to be studied experimentally and the airfoil performance data obtained can be added to enrich the airfoil performance data base. The experiment was done by testing airfoil models by lift force direct measurements in a sub-sonic wind tunnel. The type of airfoil used in this experiment is the symmetric airfoil with maximum thickness location as the geometric parameter and angle of attack with wind speed as the operational parameter being investigated for their influence to the lift and drag coefficient. The maximum thickness location was varied in 30%, 40%, and 50% of chord length measured from leading edge. The angle of attack was varied from 0o to 20o with 4o increments. The wind speed was varied from 10 m/s to 20 m/s with 2 m/s increments. Parameters being measured were the free stream velocity, and the lift force. The result of the research shows that the best airfoil performance with lift coefficient 3,16 is achieved by the airfoil that has maximum thickness location in 40% of its chord length measured from its leading edge and operates at 20o angle of attack and 8,9 m/s free stream velocity.