Design of a novel hyper-heuristic algorithm for solving engineering problems

This thesis proposed a new optimization algorithm based on the hyper-heuristic structure. The proposed optimization algorithm is called Hyper-Heuristic Fitness-Distance Balance Success-History Based Adaptive Differential Evolution (HH-FDB-SHADE). The hyper selection framework and a low-level heuristic (LLH) pool are the two main structure of the hyper-heuristic algorithms. The hyper selection framework selects the most suitable heuristic method among the low-level heuristic pool and this selection method was chosen to be the FDB in the proposed algorithm. Moreover, ten different heuristic methods which are obtained from two crossover methods and five mutation operators are chosen to create the LLH pool. The main reason for choosing FDB as the framework of the proposed algorithm is that it is a good method to balance exploration and exploitation capability. In order to test the success of HH-FDB-SHADE algorithm, CEC-17 and CEC-20 benchmark test suites in the literature are tested for different dimensional search spaces and the obtained results are compared with ten different LLH pool algorithms. The best ranked algorithm in solving CEC-17 and CEC-20 benchmark test suites is HH-FDB-SHADE according to the results of statistical analysis. In addition, the HH-FDB-SHADE algorithm is applied to two different engineering problems to reveal the performance of the improved algorithm more clearly and to prove its success in solving engineering problems. The first engineering problem is selected as the optimization of the control parameters of PID, PIDF, FOPID and PIDD2 in the Automatic Voltage Regulator (AVR) design problem. The results obtained from the AVR system are compared with five other effective meta-heuristic search algorithms in the literature such as Fitness-Distance Balance Lévy Flight Distribution, Differential Evolution, Harris Hawks Optimization, Barnacles Mating Optimization and Moth Flame Optimization algorithms. Also, the proposed algorithm is more effective and robust than the other five meta-heuristic algorithms in solving AVR design problems. As another engineering problem, the optimization of five different objective functions has been selected for the Alternating Current/Multi-Terminal Direct Current Integrated Optimal Power Flow (AC/MTDC OPF) problem. The results obtained with the proposed algorithm are compared with the Adaptive Guided Differential Evolution, Marine Predators Algorithm, Atom Search Optimization, Stochastic Fractal Search and Fitness-Distance Balance based Stochastic Fractal Search algorithms in the literature. According to the comparison result, it is observed that the proposed algorithm gives effective results compared to other algorithms for the AC/MTDC OPF problem.