A Hybrid Genetic Algorithm with Fuzzy Logic for Optimization Heat Loss in Spherical Reactor

Works purpose is to ascertain the suitability of the fuzzy-genetic algorithm (FGA) methodology, introduced by two of the authors in previous papers [1, 2], for the optimization of mechanical complex components subject to several criteria and constraints. A genetic algorithm (GA) is hybridized with an arti�cial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired in the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The procedure is applied to mechanical engineering problems available in the literature and compared to other alternative techniques. A genetic algorithm (GA) is hybridized with an arti�cial immune system (AIS) as an alternative to tackle optimization problems in engineering. The AIS is inspired in the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The procedure is applied to mechanical engineering problems available in the literature and compared to other alternative techniques. FGA optimization results are arranged by an "ad hoc" MDL program, written by one of the authors, which automatically draws the 3Dimodel in Micro Station environment of the designer can directly visualize or plot a true scale picture of the solution for the problem considered.