Structural Optimization Using a Novel Genetic Algorithm for Rapid Convergence

A novel evolutionary algorithm based upon genetic algorithm is presented in this paper which is suitable for a general class of structural optimization problems. The algorithm is applicable for discrete and/or continuous type(s) of design variables. Proposed algorithm has been designed such that it converges rapidly to local optima whenever a local optimum solution is nearby. In each generation, the algorithm selects a chromosome from the population that represents a design close to a local optimum. Further, a new set of chromosomes called Single Digit Chromosome (SDC) are generated having all zero bits except for one. Chromosomes are selected from the population and their binary addition and subtraction are performed with the SDCs. Through a number of test examples it is shown that the proposed algorithm is much robust and reliable as compared to the traditional genetic algorithm.