Research On The Modified Bacteria Foraging Optimization Algorithm And Its Application

Optimization is involved in every aspect of production and practical-life activities as well as Mechanics. Especially, in the field of engineering mechanics, plenty problems come along with the inverse problems. Optimization algorithms are often used in the general way of solving inverse problems--establishing functional along with minimization process. The weakness of the traditional optimization algorithm is the limitations of lower computing efficiency and local searching techniques. Mechanics problems in the practical engineering is becoming more and more sophisticated and complicated which makes tradition algorithm incompatible with the demand of the accuracy and efficiency. Therefore, it is urgent to develop new intelligent algorithm that is more efficient than ever. In recent years, intelligent optimization method such as genetic algorithm, ant colony optimization, particle swarm optimization etc. are wildly applied in the inverse problems of mechanics. Bacterial Foraging Optimization (BFO) is one of the new intelligent optimization methods that are based on the simulation of the foraging of Escherichia coli. The advantage of BFO is the insensitivity of parameter choosing, robustness, parallel computing and easily global searching.Researches of BFO remain initiation, and during the superficial application the weakness of relatively low accuracy and rate of convergence is discovered. Especially coping with the multi-model function optimization problems, it is difficult for BFO to discover all the optimal solutions at once. Therefore, it is highly important to thoroughly apply the BFO into the living and production, analyze and improve the original algorithm of BFO. This thesis focus mainly on the melioration and application of BFO and the major works are listed below:(1) To remove the defects of prematurity and low rate of convergence,3modifications were added to the standard BFO:In the Chemotaxis process, change the step length of the bacterial with the bacterial sensitivities; During the process of swarming process, attach each bacterial the probability of adaptive migration, and generate a more comprehensive improved algorithm:Estimated Distribution Algorithm of BFO (EDA-BFO), which can ameliorate the computing efficiency and solution quality significantly and might provide an effective way of solving complex optimization problems. After applied to the test functions, EDA-BFO is proved to be feasible and effective, and can be coded to solve high-dimensional optimization problems in practical engineering.(2) As for the low accuracy of finding out all optimal solutions with multi-method functions, Niched Bacterial Foraging Optimization is performed. Niched techniques can relatively avoid bacteria's chemotaxis, which may lead to gathering in the non-global extreme point, maintain the divergence of the bacterial and thus improve the capability of global searching. With the simulated optimization experiment with typical test functions, NBFO can generate a better searching ability globally and higher rate of convergence. NBFO is able to tracing multiple optimal solutions with higher accuracy and rate of convergence; its overall performance is significantly enhanced than the standard algorithm.(3) For the weakness of low rate of convergence, and considering the genetic algorithm is able to search in large scale, a hybrid algorithm of GA and BFO (GA-BFO) is given in this thesis. Being applied to sophisticated high-dimensional test functions, both higher time-efficiency and solution accuracy is obtained.(4) Considering the global convergence rate of BFO has no competence with PSO, an improvement of handling PSO as a mutation operator is given to combine the two algorithms--PSO and BFO in order to give full play of the ability for searching locally with BFO and globally with PSO. The mixed algorithm (PSO-BFO) is able to make the best of both optimization method and is proved to be effective after testing on the practical functions for its reliability of global convergence and rate of convergence, which are obviously more prominent than standard BFO or PSO.(5) Due to the better global searching ability of EDA-BFO, as well as the merit of high accuracy, simple modeling, low complexity of computing and unable of sinking into local extreme point, the operation of reproduction and swarming process is inserted into the standard BP network training, then an ameliorated BP network modal (BFO-BP) is generated, with optimized network connection weight. BFO-BP is applied into the simulation research of a classical inverse problem in mechanic--locating damage of compound material and the result is compared with control. Results say:BFO-BP's location of damage is highly accurate than the other, therefore BFO-BP network model possess potential in the field of locating damage of unknown. (6) Chaos system parameter identification plays a major role in the control and synchronism of chaos system, which contains unknown or unmatched parameters. Research on this field is superficial with no effective method. This thesis put forward a specific algorithm of identifying parameters of dynamical systems based on BFO in order to examine the validity and feasibility of BFO in PID of dynamical system. In practical research, numerical simulation of Lorenz&Chen non-noise chaos system and noise chaos systems are studied. Results show that BFO algorithm can effectively distinguish the parameters in the chaos systems without noise; meanwhile, BFO also has a superior ability of accurate identification of parameters in the chaos systems with white noise than PSO and GA.