COMBINING GENETIC AND DETERMINISTIC ALGORITHMS FOR LOCATING ACTUATORSON SPACE STRUCTURES

Genetic algorithms are a powerful tool for the solution of combinatori al problems such as the actuator placement problem, However, these alg orithms require a large number of analyses with the attendant high com putational costs, Therefore, it is useful to tune the operators and pa rameters of the algorithm on problems with inexpensive analyses that a re similar to computationally more expensive problems, An easy-to-calc ulate measure of actuator effectiveness is employed to evaluate severa l genetic algorithms for a problem of placing actuators at 8 of 1507 p ossible locations, Even with the best of the algorithms and with optim um mutation rates, tens of thousands of analyses are required for obta ining near-optimum locations, A hybrid procedure is proposed that firs t estimates near-optimum locations with a deterministic algorithm and then seeds these locations in the initial population of a genetic algo rithm, A simulated annealing technique is also applied as a mutation o perator for tile genetic:algorithm. The hybrid procedure reduces thee cost of the genetic optimization by an order of magnitude.