A parallel search genetic algorithm based on multiple peak values and multiple rules

In this paper, Hamming distance is used to control individual difference in the process of creating an original population, and a peak-depot is established to preserve information of different peak-points. Some new methods are also put forward to improve the optimization performance of a genetic algorithm (GA), such as the point-cast method and the neighborhood search strategy around peak-points. The methods are used to deal with genetic operation as well as cross-over and mutation, in order to obtain a global optimum solution and avoid the GAs premature convergence. By means of many control rules and a peak-depot, the new algorithm carries out an optimum search surrounding several peak-points. Along with the evolution of individuals of the population, the fitness of peak-points of peak-depot increases continually, and a global optimum solution can be obtained. The new algorithm searches around several peak-points, which increases the probability of obtaining the best global optimum solution. The results of some examples to test the modified GA indicate that what has been done makes the modified genetic algorithm effective in solving both linear optimization problems and non-linear optimization problems with restrictive functions