Hybridization of Bat and Genetic Algorithm to Solve N-Queens Problem

In this paper, a hybrid of Bat-Inspired Algorithm (BA) and Genetic Algorithm (GA) is proposed to solve N-queens problem. The proposed algorithm executes the behavior of microbats with changing pulse rates of emissions and loudness to final all the possible solutions in the initialization and moving phases. This dataset applied two metaheuristic algorithms (BA and GA) and the hybrid to solve N-queens problem by finding all the possible solutions in the instance with the input sizes of area 8*8, 20*20, 50*50, 100*100 and 500*500 on a chessboard. To find the optimal solution, consistently, ten run have been set with 100 iterations for all the input sizes. The hybrid algorithm obtained substantially better results than BA and GA because both algorithms were inferior in discovering the optimal solutions than the proposed randomization method. It also has been discovered that BA outperformed GA because it requires a reduced amount of steps in determining the solutions

PSSA : parallel stretched simulated annealing

We consider the problem of finding all the global (and some local) minimizers of a given nonlinear optimization function (a class of problems also known as multi-local programming problems), using a novel approach based on Parallel Computing. The approach, named Parallel Stretched Simulated Annealing (PSSA), combines simulated annealing with stretching function technique, in a parallel execution environment. Our PSSA software allows to increase the resolution of the search domains (thus facilitating the discovery of new solutions) while keeping the search time bounded. The software was tested with a set of well known problems and some numerical results are presented