Dependency structure matrix, genetic algorithms, and effective recombination

In many different fields, researchers are often confronted by problems arising from complex systems. Simple heuristics or even enumeration works quite well on small and easy problems; however, to efficiently solve large and difficult problems, proper decomposition is the key. In this paper, investigating and analyzing interactions between components of complex systems shed some light on problem decomposition. By recognizing three bare-bones interactions-modularity, hierarchy, and overlap, facet-wise models arc developed to dissect and inspect problem decomposition in the context of genetic algorithms. The proposed genetic algorithm design utilizes a matrix representation of an interaction graph to analyze and explicitly decompose the problem. The results from this paper should benefit research both technically and scientifically. Technically, this paper develops an automated dependency structure matrix clustering technique and utilizes it to design a model-building genetic algorithm that learns and delivers the problem structure. Scientifically, the explicit interaction model describes the problem structure very well and helps researchers gain important insights through the explicitness of the procedure.This work was sponsored by Taiwan National Science Council under grant NSC97- 2218-E-002-020-MY3, U.S. Air Force Office of Scientific Research, Air Force Material Command, USAF, under grants FA9550-06-1-0370 and FA9550-06-1-0096, U.S. National Science Foundation under CAREER grant ECS-0547013, ITR grant DMR-03-25939 at Materials Computation Center, grant ISS-02-09199 at US National Center for Supercomputing Applications, UIUC, and the Portuguese Foundation for Science and Technology under grants SFRH/BD/16980/2004 and PTDC/EIA/67776/2006

Novel particle swarm optimization algorithms with applications to healthcare data analysis

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Optimization problem is a fundamental research topic which has been receiving increasing interest according to its application potential in almost all real-world systems including engineering systems, large-scaled complex networks, healthcare management systems and so on. A large number of heuristic algorithms have been developed with the purpose of effectively solving the optimization problems during the past few decades. Served as a powerful family of heuristic algorithms, the particle swarm optimization (PSO) algorithm has been successfully employed in a variety of practical applications in dealing with optimization problems. The PSO algorithm has exhibited more competitive performance than many popular evolutionary computation approaches because of its easy implementation, fast convergence and comprehensive ability of converging to a satisfactory solution. Nevertheless, there is still much room to improve the PSO algorithm in terms of both the convergence rate and the population diversity. To summarize, there are three challenging problems in developing new variant PSO algorithms with hope to further improve the convergence rate of the PSO algorithm and maintain the population diversity: 1) how to adjust the control parameters of the PSO algorithm; 2) how to achieve the balance between the local search and the global search during the evolution process; and 3) how to guarantee the search ability of the particles and avoid premature convergence. In this thesis, we address the above mentioned challenging problems and aim to design effective variant PSO algorithms with applications in intelligent data analysis. It should be pointed out that all the developed PSO algorithms in this thesis have been evaluated by comparing with some currently popular variant PSO algorithms. • With the aim to improve the convergence rate of the optimizer, an adaptive weighting PSO algorithm is put forward where a sigmoid-function-based weighting strategy is introduced to adjust the acceleration coefficients. With this weighting strategy, the distances from the particle to the global best position and from the particle to its personal best position are both taken into consideration, thereby having the distinguishing feature of enhancing the convergence rate. • As with other evolutionary computation approaches, the modification of parameters is an efficient method for improving the search ability of the algorithm. We present a randomised PSO algorithm where Gaussian white noise with adjustable intensity is utilized to randomly perturb the acceleration coefficients in order to explore and exploit the problem space thoroughly. • To further develop a novel PSO algorithm with promising search ability, we propose a randomly occurring distributedly delayed particle swarm optimization (RODDPSO) algorithm which demonstrates competitive performance in seeking the optimal solution. The randomly occurring distributed time delays not only contribute to a thorough exploration of the search space but also achieve a proper balance between the local exploitation and the global exploration. • To fully investigate the application potential of the developed PSO algorithms, we apply the RODDPSO algorithm to intelligent data analysis (including data clustering and classification problems). We optimize the initial cluster centroids of the K-means clustering algorithm and the hyperparameters of the deep neural network by using the RODDPSO algorithm. The developed PRODDPSO-based algorithms are successfully employed in patients’ triage categorization and patient attendance disposal problems with satisfactory performanc

Using semi-independent variables to enhance optimization search

In this study, the concept of a semi-independent variable (SIV) problem representation is investigated that embodies a set of expected or desired relationships among the original variables, with the goal of increasing search effectiveness and efficiency. The proposed approach intends to eliminate the generation of infeasible solutions associated with the known relationships among the variables and cutting the search space, thereby potentially improving a search algorithm's convergence rate and narrowing down the search space. However, this advantage does not come for free. The issue is the multiplicity of SIV formulations and their varying degree of complexity, especially with respect to variable interaction. In this paper, we propose the use of automatic variable interaction analysis methods to compare and contrast different SIV formulations. The performance of the proposed approach is demonstrated by implementing it within a number of classical and evolutionary optimization algorithms (namely, interior-point algorithm, simulated annealing, particle swarm optimization, genetic algorithm and differential evolution) in the application to several practical engineering problems. The case study results clearly show that the population-based algorithms can significantly benefit from the proposed SIV formulation resulting in better solutions with fewer function evaluations than in the original approach. The results also indicate that an automatic variable interaction analysis is capable of estimating the difficulty of the resultant SIV formulations prior to any optimization attempt

Using semi-independent variables to enhance optimization search

In this study, the concept of a semi-independent variable (SIV) problem representation is investigated that embodies a set of expected or desired relationships among the original variables, with the goal of increasing search effectiveness and efficiency. The proposed approach intends to eliminate the generation of infeasible solutions associated with the known relationships among the variables and cutting the search space, thereby potentially improving a search algorithm's convergence rate and narrowing down the search space. However, this advantage does not come for free. The issue is the multiplicity of SIV formulations and their varying degree of complexity, especially with respect to variable interaction. In this paper, we propose the use of automatic variable interaction analysis methods to compare and contrast different SIV formulations. The performance of the proposed approach is demonstrated by implementing it within a number of classical and evolutionary optimization algorithms (namely, interior-point algorithm, simulated annealing, particle swarm optimization, genetic algorithm and differential evolution) in the application to several practical engineering problems. The case study results clearly show that the population-based algorithms can significantly benefit from the proposed SIV formulation resulting in better solutions with fewer function evaluations than in the original approach. The results also indicate that an automatic variable interaction analysis is capable of estimating the difficulty of the resultant SIV formulations prior to any optimization attempt