Multi-strategy hybrid whale optimization algorithms for complex constrained optimization problems

A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low optimization precision.Firstly,the population is initialized by introducing the theory of good point set,which increases the randomness and diversity of the population and lays the foundation for the global optimization of the algorithm.Then,a novel linearly update equation of convergence factor is designed to coordinate the abilities of exploration and exploitation.At the same time,the global exploration and local exploitation capabilities are improved through the siege mechanism of Harris Hawks optimization algorithm.Finally,the simulation experiments are conducted on the 6 benchmark functions and Wilcoxon rank sum test to evaluate the optimization performance of the improved algorithm.The experimental results show that the proposed algorithm has more significant improvement in optimization accuracy,convergence speed and robustness than the comparison algorithm.

Improved HHO algorithm based on good point set and nonlinear convergence formula

Harris hawks optimization(HHO)algorithm is an efficient method of solving function optimization problems.However,it is still confronted with some limitations in terms of low precision,low convergence speed and stagnation to local optimum.To this end,an improved HHO(IHHO)algorithm based on good point set and nonlinear convergence formula is proposed.First,a good point set is used to initialize the positions of the population uniformly and randomly in the whole search area.Second,a nonlinear exponential convergence formula is designed to balance exploration stage and exploitation stage of IHHO algorithm,aiming to find all the areas containing the solutions more comprehensively and accurately.The proposed IHHO algorithm tests 17 functions and uses Wilcoxon test to verify the effectiveness.The results indicate that IHHO algorithm not only has faster convergence speed than other comparative algorithms,but also improves the accuracy of solution effectively and enhances its robustness under low dimensional and high dimensional conditions.