Research On Harris Hawk Optimization Algorithm And Its Engineering Application

Optimization problem is to find the best solution from many alternatives under certain constraints,so that the selected objective function can reach the optimal problem.The meta-heuristic optimization algorithm is a common method to solve the global optimization problem.It mainly realizes the solution of the optimal solution by simulating the natural and human intelligence.Because of its low computing cost,search speed and efficiency is favored.Harris Hawk Optimization(HHO)algorithm is a new population-based,naturally-inspired optimization paradigm,which is proposed to imitate the real performance of the bird predator of Harris Hawk in capturing prey,and has strong search and optimization ability.(1)In order to enhance its search mechanism and speed of convergence,an new improved HHO algorithm based on the inverse cumulative function operator of Cauchy distribution and Tangent flight operator was proposed.Two operators are added as factors to control step size,and the walking path of Cauchy inverse cumulative integral function further enhances the search stability of the algorithm.Tangential flight operator has the function of balancing development and exploration,which enhances the convergence ability of the algorithm.The addition of the two makes the algorithm can search the local space effectively.In order to verify the performance of the proposed algorithm,the 30 benchmark functions of the 2017 Institute of Electrical and Electronic Engineers(IEEE)Conference on Evolutionary Computation(CEC2017)and two practical engineering design problems are adopted to carry out the simulation experiments.On the other hand,the Covariance Matrix Adaptation Evolutionary Strategies(CMA-ES),Arithmetic Optimization Algorithm(AOA),Butterfly Optimization Algorithm(BOA),Bat Algorithm(BA),Whale Optimization Algorithm(WOA),Sine Cosine Algorithm(SCA),and the proposed HHO algorithms were used for comparison experiments.Simulation results show that the proposed the Cauchy-distribution and Tangent-Flight Harris Hawk Optimization(CTHHO)algorithm has strong optimization capability.(2)In order to find Pareto optimal solution set uniformly distributed along all objectives,a Hybrid Multi-Objective Harris Hawk Optimization Algorithm(H-MOHHO)was proposed based on elite non-dominated sorting and grid indexing mechanism.The optimal Pareto solution set is obtained by combining the two terms.Firstly,the non-dominant ranking mechanism based on elite was used to assign rank to the population and select the high quality solution set,and then the archived grid index mechanism with updating mechanism was used to select the final solution set.Such a hybrid structure makes the objective function obtain the optimal Pareto solution set while maintaining the diversity of the population,which improves the effectiveness of solving multi-objective optimization problems.In order to verify the performance of the proposed H-MOHHO algorithm,22 test functions and 4multi-objective engineering problems are used for simulation,and four performance indexes are compared with Multi-Objective Particle Swarm Optimization(MOPSO),Non-dominated Sorting Genetic Algorithm II(NSGA-II),Multi-Objective Ant Lion Optimizer(MOALO),Multi-Objective Salp Swarm Algorithm(MSSA)and Multi-Objective Dragonfly Algorithm(MODA).Experimental results show that the proposed H-MOHHO algorithm has better competitiveness and applicability.(3)Wind energy is a clean energy widely promoted all over the world in recent years.An important stage of obtaining electricity from wind turbines is wind farm layout optimization(WFLO),whose optimization goal is to obtain the optimal location of all wind turbines in order to maximize the total power output with the minimum energy cost.This thesis proposes a wind farm layout optimization strategy based on the Harris Hawk Optimization Algorithm with Variable Weight Coefficients(ABHHO)and Jansen’s wake model.In order to improve the search efficiency and convergence speed of the algorithm,two weight coefficients(beta and alpha)are proposed to improve the HHO algorithm.Beta reduces the search step size and enhances the search efficiency by making it more detailed in global search.Alpha interferes with energy by oscillating,making energy fluctuate and jump out of the local optimal.Firstly,the proposed ABHHO algorithm and HHO,AOA,BOA,WOA,BA,SCA and Reptile Search Algorithm(RSA)were used to conduct Consumer Electronics Control 2017 Test Function(CEC2017)function optimization simulation experiments to verify the effectiveness of the proposed algorithm.Aiming at the WFLO problem based on the Jansen’s wake model,two different initial wind speeds were used to test the performance,considering the influence of single wake,multiple wake,and two energy estimates with or without considering the partially shielded wake area.Nine algorithms,including HHO,WOA,SCA,Particle Swarm Optimization(PSO),Grass-Hopper Optimization Algorithm(GOA),Differential Evolution(DE),Crow Search Algorithm(CSA),Real-Code Ant Colony Optimization(ACOR)and Teaching-Learning Based Optimization(TLBO),were selected to conduct simulation experiments with the proposed ABHHO algorithm.The performance of the proposed ABHHO algorithm was compared with that of the wind farm layout output power and cost of value.The results show that the ABHHO algorithm can obtain better results.