Ren, Yuan (2008-12). Adaptive Evolutionary Monte Carlo for Heuristic Optimization: With Applications to Sensor Placement Problems. Doctoral Dissertation.
Thesis
This dissertation presents an algorithm to solve optimization problems with "black-box" objective functions, i.e., functions that can only be evaluated by running a computer program. Such optimization problems often arise in engineering applications, for example, the design of sensor placement. Due to the complexity in engineering systems, the objective functions usually have multiple local optima and depend on a huge number of decision variables. These difficulties make many existing methods less effective. The proposed algorithm is called adaptive evolutionary Monte Carlo (AEMC), and it combines sampling-based and metamodel-based search methods. AEMC incorporates strengths from both methods and compensates limitations of each individual method. Specifically, the AEMC algorithm combines a tree-based predictive model with an evolutionary Monte Carlo sampling procedure for the purpose of heuristic optimization. AEMC is able to escape local optima due to the random sampling component, and it improves the quality of solutions quickly by using information learned from the tree-based model. AEMC is also an adaptive Markov chain Monte Carlo (MCMC) algorithm, and is in fact the rst adaptive MCMC algorithm that simulates multiple Markov chains in parallel. The ergodicity property of the AEMC algorithm is studied. It is proven that the distribution of samples obtained by AEMC converges asymptotically to the "target" distribution determined by the objective function. This means that AEMC has a larger probability of collecting samples from regions containing the global optimum than from other regions, which implies that AEMC will reach the global optimum given enough run time. The AEMC algorithm falls into the category of heuristic optimization algorithms, and is applicable to the problems that can be solved by other heuristic methods, such as genetic algorithm. Advantages of AEMC are demonstrated by applying it to a sensor placement problem in a manufacturing process, as well as to a suite of standard test functions. It is shown that AEMC is able to enhance optimization effectiveness and efficiency as compared to a few alternative strategies, including genetic algorithm, Markov chain Monte Carlo algorithms, and meta-model based methods. The effectiveness of AEMC for sampling purposes is also shown by applying it to a mixture Gaussian distribution.
This dissertation presents an algorithm to solve optimization problems with "black-box" objective functions, i.e., functions that can only be evaluated by running a computer program. Such optimization problems often arise in engineering applications, for example, the design of sensor placement. Due to the complexity in engineering systems, the objective functions usually have multiple local optima and depend on a huge number of decision variables. These difficulties make many existing methods less effective. The proposed algorithm is called adaptive evolutionary Monte Carlo (AEMC), and it combines sampling-based and metamodel-based search methods. AEMC incorporates strengths from both methods and compensates limitations of each individual method. Specifically, the AEMC algorithm combines a tree-based predictive model with an evolutionary Monte Carlo sampling procedure for the purpose of heuristic optimization. AEMC is able to escape local optima due to the random sampling component, and it improves the quality of solutions quickly by using information learned from the tree-based model. AEMC is also an adaptive Markov chain Monte Carlo (MCMC) algorithm, and is in fact the rst adaptive MCMC algorithm that simulates multiple Markov chains in parallel. The ergodicity property of the AEMC algorithm is studied. It is proven that the distribution of samples obtained by AEMC converges asymptotically to the "target" distribution determined by the objective function. This means that AEMC has a larger probability of collecting samples from regions containing the global optimum than from other regions, which implies that AEMC will reach the global optimum given enough run time. The AEMC algorithm falls into the category of heuristic optimization algorithms, and is applicable to the problems that can be solved by other heuristic methods, such as genetic algorithm. Advantages of AEMC are demonstrated by applying it to a sensor placement problem in a manufacturing process, as well as to a suite of standard test functions. It is shown that AEMC is able to enhance optimization effectiveness and efficiency as compared to a few alternative strategies, including genetic algorithm, Markov chain Monte Carlo algorithms, and meta-model based methods. The effectiveness of AEMC for sampling purposes is also shown by applying it to a mixture Gaussian distribution.