Optimal System Design of In-Situ Bioremediation Using Parallel Recombinative Simulated Annealing

We present a simulation/optimization model combining optimization with BIOPLUME II simulation for optimizing in-situ bioremediation system design. In-situ bioremediation of contaminated groundwater has become widely accepted because of its cost-effective ability to achieve satisfactory cleanup. We use parallel recombinative simulated annealing to search for an optimal design and apply the BIOPLUME II model to simulate aquifer hydraulics and bioremediation. Parallel recombinative simulated annealing is a general-purpose optimization approach that has the good convergence of simulated annealing and the efficient parallelization of a genetic algorithm. This is the first time that parallel recombinative simulated annealing has been applied to groundwater management. The design goal of the in-situ bioremediation system is to minimize system installation and operation cost. System design decision variables are pumping well locations and pumping rates. The problem formulation is mixed-integer and nonlinear. The system design must satisfy constraints on pumping rates, hydraulic heads, contaminant concentration at the plume source and at downstream monitoring wells. For the posed problem, the parallel recombinative simulated annealing obtains an optimal solution that minimizes system cost, reduces contaminant concentration and prevents plume migration

An efficient algorithm for nonlinear integer programming

M.Sc., Faculty of Sciences, University of the Witwatersrand, 2011Abstract This dissertation is concerned with discrete global optimization of nonlinear problems. These problems are constrained and unconstrained and are not easily solvable since there exists multiplicity of local and global minima. In this dissertation, we study the current methods for solving such problems and highlight their ine ciencies. We introduce a new local search procedure. We study the rapidly-exploring random tree (RRT) method, found mostly in the research area of robotics. We then design two global optimization algorithms based on RRT. RRT has never been used in the eld of global optimization. We exploit its attractive properties to develop two new algorithms for solving the discrete nonlinear optimization problems. The rst method is called RRT-Optimizer and is denoted as RRTOpt. RRTOpt is then modi ed to include probabilistic elements within the RRT. We have denoted this method by RRTOptv1. Results are generated for both methods and numerical comparisons are made with a number of recent methods

An adaptive hybrid genetic-annealing approach for solving the map problem on belief networks

Genetic algorithms (GAs) and simulated annealing (SA) are two important search methods that have been used successfully in solving difficult problems such as combinatorial optimization problems. Genetic algorithms are capable of wide exploration of the search space, while simulated annealing is capable of fine tuning a good solution. Combining both techniques may result in achieving the benefits of both and improving the quality of the solutions obtained. Several attempts have been made to hybridize GAs and SA. One such attempt was to augment a standard GA with simulated annealing as a genetic operator. SA in that case acted as a directed or intelligent mutation operator as opposed to the random, undirected mutation operator of GAs. Although using this technique showed some advantages over GA used alone, one problem was to find fixed global annealing parameters that work for all solutions and all stages in the search process. Failing to find optimum annealing parameters affects the quality of the solution obtained and may degrade performance. In this research, we try to overcome this weakness by introducing an adaptive hybrid GA - SA algorithm, in which simulated annealing acts as a special case of mutation. However, the annealing operator used in this technique is adaptive in the sense that the annealing parameters are evolved and optimized according to the requirements of the search process. Adaptation is expected to help guide the search towards optimum solutions with minimum effort of parameter optimization. The algorithm is tested in solving an important NP-hard problem, which is the MAP (Maximum a-Posteriori) assignment problem on BBNs (Bayesian Belief Networks). The algorithm is also augmented with some problem specific information used to design a new GA crossover operator. The results obtained from testing the algorithm on several BBN graphs with large numbers of nodes and different network structures indicate that the adaptive hybrid algorithm provides an improvement of solution quality over that obtained by GA used alone and GA augmented with standard non-adaptive simulated annealing. Its effect, however, is more profound for problems with large numbers of nodes, which are difficult for GA alone to solve