Bacteria Foraging Optimization Algorithm And Its Application In Load Dispatch Problems

Economic load distribution of power system is the foundation of safe and economic operation of power system.The valve point effect of actual power generation system makes the economic load distribution abstract into a single-objective optimization problem with high dimensional,nonlinear and multi-constraint.The rapid development of low-carbon economy,environmental economic distribution has become a research hotspot.Considering environmental cost target based on economic dispatch,it is a typical complex nonlinear optimization problem with multiple objectives,high dimensions and strong constraints.It is difficult to use traditional optimization methods for mathematical modeling.In recent years,intelligent algorithm has been widely used in solving this kind of problem.The bacterial foraging algorithm is a new kind of intelligent algorithm,which has become a hot topic in recent years because of its parallel searching ability and simple and easy optimization operation.However,the basic bacterial foraging algorithm has some defects in operator operation,which makes it easy to fall into the local minimum and lost the diversity of population.So the complex optimization problems cannot be effectively solved.Aiming at the problem of power load distribution,this paper focuses on the research and application of bacterial foraging algorithm,and the main research work and achievements are as follows:1.A bacterial foraging algorithm based on biological evolution is proposed to solve the problem that BFO algorithm is easy to fall into local minimum and to lose the population diversity.From the perspective of biological evolution,the chemotactic step of bacterial individuals is regarded as an evolutionary process that adapts itself with the update of bacterial location and the change of environment.Evolutionary strategies are used to find the optimal chemotactic step of bacterial individuals.In the reproductive operator,differential evolution is used to reproduce the offspring.It increases the diversity of the population and improves the convergence performance of the algorithm.In order to avoid blind migration and improve the precision and global optimization ability of the algorithm,an adaptive migration operator is designed.The effectiveness of the algorithm is verified with the standard test function.The results show that the probability of finding the average optimal solution is more than 80%,which is more than 20% higher than the basic bacterial foraging algorithm.It shows that the algorithm can obtain better global searching ability.Compared with the performance of other algorithms,the results show that the algorithm has better stability,better global search ability and faster convergence speed in solving the optimization problem of high-dimensional complex functions.2.In order to realize the information interaction between individuals and groups,the systematic characteristics of bacterial foraging algorithm are analyzed based on the system concept of biological population.A bacterial foraging model based on feedback control is constructed.And a feedback control bacterial foraging algorithm based on PID control strategy was designed.It makes the individual adjust their position constantly with the change of the system output information of the group,and finally makes the group move towards the target region continuously.In order to save the cost of calculation,on this basis,the determination of reproductive operation is adjusted according to the number of chemotaxis,and the premature convergence of reproductive operation can be effectively improved.The simulation results of the standard function show that the algorithm can improve the convergence speed and balance the relationship between global optimization and local optimization.3.In order to improve the adaptability and selection ability of bacteria to the environment,the concept of intelligent agent is introduced into the individual.The structure of intelligent agent bacteria is constructed,and its intelligent action strategy is designed.With the help of random neighbor bacteria and optimal bacteria,the bacteria constantly modify their own position.With the gradual diffusion of the neighborhood,this kind of individual behavior enables the bacterial population to perceive the changes of the environment to adjust the appropriate step size and position.The bacterial foraging algorithm based on agent is proposed.Through the standard function test,the results show that the the probability of finding the average optimal solution is 90%,and the time is the least.By comparison with other intelligent algorithms,the algorithm can find the optimal value of the theory,the stability of the algorithm is good,and the result of processing high-dimensional complex functions is satisfactory.4.The idea of NSGA-II non-dominant sorting strategy is introduced into the BFO algorithm to construct a multi-target bacterial foraging optimization algorithm.The non-inferior solution set of Parato is constructed according to the crowding distance.In order to improve the global optimization and convergence speed,the bacterial foraging algorithm based on feedback control is used to get the adaptive update locations of the bacteria individual in non-inferior bacterials,and the population diversity is obtained by the multiply operation based on differential evolution.The current local optimal solution is obtained by neighborhood search and external archiving is implemented by elite retention strategy.Finally,through the standard test function experiment,the diversity evaluation index of multi-target bacterial foraging algorithm is at least 1 order of magnitude higher than the algorithm in the literature,which improves the diversity of the optimal solution of Pareto and makes the distribution of the optimal frontier of Pareto more uniform.5.In order to solve the problem of power system load distribution,each power units is regarded as bacteria individual to carry on the parameters of the code.The inequality constraint boundary condition is used as the limit range of the initial population.Equality condition is treated as a penalty functions.Fitness function is designed with the objective function considering the effect of threshold point.The improved bacterial foraging algorithm is used to solve the economic load problem.The feasibility and effectiveness of the improved BFO algorithm are verified by taking 6 buses 3 power system as an example.The multi-objective bacterial foraging algorithm based on NSGA-II is used to solve the environmental economic load distribution problem.The feasibility and effectiveness of multi-objective bacterial foraging algorithm are verified by the example of IEEE30 bus 6power systems.