PROJECT TITLE :

Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey

ABSTRACT:

Meta-heuristic algorithms are usually inspired by natural phenomena, as well as the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Thanks to their nice potential in solving tough optimization problems, meta-heuristic algorithms have found their means into automobile engine style. There are completely different optimization issues arising in several areas of car engine management as well as calibration, management system, fault diagnosis, and modeling. During this paper we have a tendency to review the state-of-the-art applications of various meta-heuristic algorithms in engine management systems. The review covers a wide selection of research, as well as the applying of meta-heuristic algorithms in engine calibration, optimizing engine management systems, engine fault diagnosis, and optimizing completely different elements of engines and modeling. The meta-heuristic algorithms reviewed during this paper embrace evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system.


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