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Modern diesel engines are charged with the difficult problem of balancing emissions and efficiency. For this work, a variant of the artificial bee colony (ABC) algorithm was applied for the first time to the experimental optimization of diesel engine combustion and emissions. In this study, the employed and onlooker bee phases were modified to balance both the exploration and exploitation of the algorithm. The improved algorithm was successfully trialed against particle swarm optimization (PSO), genetic algorithm (GA), and a recently proposed PSO-GA hybrid with three standard benchmark functions. For the engine experiments, six variables were changed throughout the optimization process, including exhaust gas recirculation (EGR) rate, intake temperature, quantity and timing of pilot fuel injections, main injection timing, and fuel pressure. Low sulfur diesel fuel was used for all the tests. In total, 65 engine runs were completed in order to reduce a five-dimensional objective function. In order to reduce nitrogen oxide (NOx) emissions while keeping particulate matter (PM) below 0.09 g/kW h, solutions call for 43% exhaust gas recirculation, with a late main fuel injection near top-dead center. Results show that early pilot injections can be used with high exhaust gas recirculation to improve the combustion process without a large nitrogen oxide penalty when main injection is timed near top-dead center. The emission reductions in this work show the improved ABC algorithm presented here to be an effective new tool in engine optimization.

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New engine hardware and injection strategies allow modern engines to meet stringent emissions regulations but can require extensive engine testing to identify optimum operating points. Swarm intelligence algorithms, which do not require knowledge of the search space gradient, can provide a short cut in finding optimum operating parameters in reduced experimental time than a traditional design of experiments study. In this paper, a modified artificial bee colony (ABC) algorithm and a cooperative particle swarm optimization (CPSO) algorithm are applied to triple and quadruple injection routines. The optimization was applied directly to the operation of a four-cylinder turbocharged production diesel engine operating at a high exhaust gas recirculation rate and medium load. Six and eight variable optimizations were carried out for triple and quadruple injection schedules, respectively. In experimental testing, the cooperative particle swarm optimizer significantly reduced soot and carbon monoxide emissions in only 84 engine tests when using a pilot-pilot-main-post schedule. The modified artificial bee colony algorithm took 176 engine tests to optimize a pilot-main-post triple-injection schedule and was not applied to the four-injection routine.