Concerning the problems that Teaching & Learning Based Optimization (TLBO) algorithm has slow convergence rate and low accuracy, and it is easy to be trapped into local optimum in solving high-dimensional problems, an Improved TLBO algorithm with Brain Storming Optimization (ITLBOBSO) was proposed. In this algorithm, a new “learning”operator was designed and applied to replace the origin “learning” in the TLBO. In the iteration process of the population, the “teaching” operator was executed by the current individual. Then, two individuals were selected randomly from the population, and brain storming learning was executed by the better one of the above and the current individual to improve the state of the current individual. Cauchy mutation and a random parameter associated with the iterations were introduced in the formula of this operator to improve the exploration ability in early stage and the exploitation ability for new solutions in later stage of the algorithm. In a series of simulation experimentations, compared with TLBO, the proposed algorithm has large improvements of solution accuracy, robustness and convergence speed on 11 benchmark functions. The experimental results on two constrained engineering optimization problems show that compared to TLBO algorithm, ITLBOBSO reduces the total cost by 4 percentage points, which proves the effectiveness of the proposed mechanism on overcoming the weakness of TLBO algorithm. The proposed algorithm is suitable for solving high dimensional continuous optimization problems.

The Teaching-Learning-Based Optimization (TLBO) algorithm in function optimization problems has some weakness, such as falling into the local optimum value,converging slowly in the later period and acquiring solution inaccurately. To overcome these shortcomings, an improved TLBO algorithm with dynamic self-adaptive learning and dynamic random searching was proposed. Firstly, a linear increment dynamic variation coefficient was introduced into the teaching process to adjust the value of knowledge to individual learning in the iterative optimization process. Secondly, in order to improve the precision of solution, teacher individual executed dynamic random searching to exploit the solution space around the best individual. The experiments were conducted on 14 classic testing functions, and the experimental results show that the proposed algorithm is much better than standard TLBO at not only the accuracy of solutions but also for the convergence speed. It is suitable to solve the high-dimensional function optimization problem.

Concerning the problem that Fireworks Explosion Optimization (FEO) algorithm is easy to be premature and has low solution precision, an elite Opposition-Based Learning (OBL) was proposed. In every iteration, OBL was executed by the current best individual to generate an opposition search populations in its dynamic search boundaries, thus the search space of the algorithm was guided to approximate the optimum space. This mechanism is helpful to improve the balance and exploring ability of the FEO. For keeping the diversity of population, the sudden jump probability of the individual to the current best individual was calculated, and based on it, the roulette mechanism was adopted to choose the individual which entered into the child population. The experimental simulation on five classical benchmark functions show that, compared with the related algorithm, the improved algorithm has higher convergence rate and accuracy for numerical optimization, and it is suitable to solve the high dimensional optimization problem.

To solve the path test data generation automatically in software testing, a new scheme on searching solution space based on Artificial Fish Swarm (AFS) algorithm was proposed. To improve the ability of original AFS, chaotic searching was introduced to reform AFS' local searching ability and precision of solution. Once AFS finished an iteration process, chaos algorithm was executed with global best solution. At the same time, some partial individuals with bad state were washed out. Then, according to the optimization individual contracting the searching space, some new individuals were generated randomly. Two kinds of triangle program were tested and the results show that the improved AFS has faster convergence and higher calculation accuracy.