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Seeker Optimization Algorithm And Its Applications

Posted on:2010-03-21Degree:DoctorType:Dissertation
Country:ChinaCandidate:C H DaiFull Text:PDF
GTID:1118360278458740Subject:Electrical system control and information technology
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Optimization has been widely used in many fields such as scientific research, engineering practice and economic management, etc. The aim is to create an objective function and find a solution for minimizing or maximizing it. With the extension of human knowledge and activities, optimization technique and algorithm has become the target of increasing interest due to the increasing demands for improved optimization performance in many complex systems where the involved objective functions are non-linear, multi-modal, and even cannot be expressed in explicit mathematical forms and their derivatives cannot be easily computed. In many research fields, pursuing an effective optimization method has become one of the main objectives for the scientific researchers.The wonderful and constructive self-organization behavior usually emerges from the individuals of social animals who follow some simple rules and communicate with each other and their environments. In the past 20 years, these behaviors of social animals have been attracting more and more attention of researchers from which swarm intelligence computation was proposed. Swarm intelligence is an algorithm or a device and illumined by the social behavior of gregarious insects and other animals, which is designed for solving distributed problems. Optimization tasks are often encountered in many areas of human life, and the search for a solution to a problem is one of the basic behaviors to all mankind. The dissertation focuses on learning from the advanced social animal, human, and simulating their behaviors for solving optimization problems. The main contributions given in this dissertation are as follows.(1) A novel cloud-based adaptive genetic algorithm (CAGA) was proposed. The rule for the probability of crossover (mutation) can be described by natural linguistic variables as follows: the probabilities of the individuals with hyper-average fitnesses decrease with the fitness increasing while the probabilities of the individuals with sub-average fitnesses are set at a fixed maximum value. Then, a normal cloud model is introduced to model the rule and adaptively give the probabilities of crossover and mutation. Because cloud model has the properties of randomness and stable tendency, the first property is able to help GA avoid a local optimum, and the second property can improve its convergence speed. The benchmark function optimization and TSP problems proved the effectiveness of CAGA.(2) A novel cloud-based evolutionary algorithm (CEA) was proposed. Based on the intermediate value theorem of the continuous function and the simulation of human focusing search, CEA is implemented using cloud models as crossover and mutation operators. Unlike GA with the property of "absence of memory", CEA searches the optimal solutions around the current generations until to converge to the optimum for as few generations as possible. Owning to the stable tendency of cloud model, CEA does not easily get lost and is able to locate the region in which the global optimum exists. On the other hand, the randomness of cloud model can maintain the diversity of the population and make CEA enough robust not to get stuck at a local optimum. Benchmark function optimization and digital FIR filter design proved the effectiveness of CEA.(3) A novel stochastic search algorithm called as seeker optimization algorithm (SOA) was proposed. In SOA, optimization is viewed as a search of search population in the search space. After the detailed study of human searching behaviors, SOA is established with search group as population and the position of a seeker as a candidate solution. In the algorithm, the choice of search direction is based on the empirical gradient by evaluating the response to the position changes, and the decision of step length is based on uncertainty reasoning by using a simple Fuzzy rule. Then, according to the given search direction and step length, the position update is conducted so as to implement the solution evolution.(4) It is proved that SOA is a new swarm intelligence algorithm. Further theoretical analysis on the validity of SOA was conducted. The difference and sameness between SOA and other intelligent optimization algorithms were presented. The influence of the parameters on the performance was studied. Then, SOA was applied to the benchmark functions of CEC05 and presented the better optimization outputs for some functions. The results of the experiments proved the effectiveness of SOA.(5) A SOA-based training algorithm was proposed for the evolution of weight values and structure. Since the MLP's performance is sensitive to network architecture, to achieve a proper balance in ANN's network complexity and generalization capability, two approaches are used in this study, namely: structure evolution and the use of the mean squared error with regularization performance function (MSEREG). Hence, the individual in SOA consists of three parts: the link switch bits, the connection weight values and the regularization parameter. The benchmark problems from pattern classification and function approximation were used to evaluate the new algorithm.(6) A SOA-based evolutionary method is proposed for digital IIR filter design. In this study, IIR filters are designed for the system identification purpose. In this case, the parameters of the IIR filter are successively adjusted by SOA until the error between the outputs of the filter and the system is minimized. Several widely used design examples were used to exhibit the effectiveness of the proposed method.(7) A new optimized model of proton exchange membrane fuel cell (PEMFC) was proposed by using the proposed SOA. In this case, SOA was used to automatically tune the parameters of the polarization curve model of a PEMFC to minimize the mean square error between the optimized model and the previously sampled experimental data. The simulation results have shown that SOA is suitable for modeling a PEMFC.(8) The proposed SOA was applied to reactive power optimization. The aim is to minimize the active power loss in the transmission network by tuning the generator voltage, the transformer tap and the shunt capacitor/inductor and simultaneously keeping the load-bus voltage and the generator reactive power within the suitable limits to maintain the voltage quality. The new method is tested on IEEE-57 bus power systems, and the experimental results showed that SOA is suitable for reactive power optimization.
Keywords/Search Tags:Swarm intelligence, intelligent optimization algorithms, cloud-based adaptive genetic algorithm, cloud evolutionary algorithm, human searching behabiors, seeker optimization algorithm, benchmark function optimization, artificial neural network training
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