AN IMPROVED RANDOM-SEARCH ALGORITHM FOR NONLINEAR OPTIMIZATION

An adaptive random search optimization algorithm is presented, which is found to be very efficient in dealing with non-linear constrained and unconstrained problems. The major differences relatively to previously reported algorithms are that variable, parameter dependent compression vectors are employed in the contraction of the search regions and that shifting strategies incorporating "wrong-way" moves are enforced. The algorithm was tested with 24 severe functions published in the literature. Results were compared with those obtained employing another random search method and with published work. The proposed algorithm is shown to be more robust and more efficient in what concerns the overcoming of difficulties associated with local optima, the sensitivity to search intervals and associated compression factors, the starting solution vector and the dependency upon the random number sequence. The results obtained reveal the adequacy of the algorithm for the optimization of a broad range of problems encountered in chemical engineering practice.