Genetic network parameter estimation using single and multi-objective particle swarm optimization

dc.contributor.authorMorcos, Karim M.
dc.description.abstractMulti-objective optimization problems deal with finding a set of candidate optimal solutions to be presented to the decision maker. In industry, this could be the problem of finding alternative car designs given the usually conflicting objectives of performance, safety, environmental friendliness, ease of maintenance, price among others. Despite the significance of this problem, most of the non-evolutionary algorithms which are widely used cannot find a set of diverse and nearly optimal solutions due to the huge size of the search space. At the same time, the solution set produced by most of the currently used evolutionary algorithms lacks diversity. The present study investigates a new optimization method to solve multi-objective problems based on the widely used swarm-intelligence approach, Particle Swarm Optimization (PSO). Compared to other approaches, the proposed algorithm converges relatively fast while maintaining a diverse set of solutions. The investigated algorithm, Partially Informed Fuzzy-Dominance (PIFD) based PSO uses a dynamic network topology and fuzzy dominance to guide the swarm of dominated solutions. The proposed algorithm in this study has been tested on four benchmark problems and other real-world applications to ensure proper functionality and assess overall performance. The multi-objective gene regulatory network (GRN) problem entails the minimization of the coefficient of variation of modified photothermal units (MPTUs) across multiple sites along with the total sum of similarity background between ecotypes. The results throughout the current research study show that the investigated algorithm attains outstanding performance regarding optimization aspects, and exhibits rapid convergence and diversity.en_US
dc.description.advisorSanjoy Dasen_US
dc.description.advisorStephen M. Welchen_US
dc.description.degreeMaster of Scienceen_US
dc.description.departmentDepartment of Electrical and Computer Engineeringen_US
dc.publisherKansas State Universityen
dc.subjectParticle Swarm Optimizationen_US
dc.subjectGenetic networken_US
dc.subjectMulti-objective optimizationen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectEvolutionary algorithmsen_US
dc.subjectGenetic algorithmsen_US
dc.subject.umiArtificial Intelligence (0800)en_US
dc.subject.umiBioinformatics (0715)en_US
dc.subject.umiBiology, Plant Physiology (0817)en_US
dc.subject.umiComputer Engineering (0464)en_US
dc.subject.umiEngineering (0537)en_US
dc.subject.umiInformation Science (0723)en_US
dc.subject.umiPlant Pathology (0480)en_US
dc.subject.umiPlant Sciences (0479)en_US
dc.titleGenetic network parameter estimation using single and multi-objective particle swarm optimizationen_US


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