Abstract
An adaptive Harris hawks optimization (HHO) algorithm is proposed to solve the non-linearly constrained optimization problems. The algorithm, namely, instinctive reaction strategy based on HHO (IRSHHO), combines the instinctive reaction strategy (IRS) with the HHO algorithm. In IRSHHO, the increment of step length decreases with the increase in the iteration. Moreover, the energy of the prey is also considered to speed up the convergence in each iteration. The performance of the IRSHHO is investigated on five benchmark numerical examples. The results of IRSHHO provide very competitive results compared to other well-known algorithms. Furthermore, the IRSHHO is applied to optimize the auto drum fashioned brake problem. Results show that the IRSHHO can obtain the optimal solution with the braking efficiency factor that is increased by 27.872% from the initial design with the proposed IRSHHO method.
Highlights
Many optimization problems in scientific research belong to nonlinear combinatorial optimization problems, which are always composed of objective functions, decision variables, and nonlinear constrained
To decrease the computing cost, an improved Harris hawks optimization (HHO), called instinctive reaction strategy based on HHO (IRSHHO), is proposed in this study
This paper has proposed an improved adaptive HHO, namely, instinctive reaction strategy based on HHO (IRSHHO), for solving the drum fashioned brakes optimization problems with some nonlinear constraints
Summary
Many optimization problems in scientific research belong to nonlinear combinatorial optimization problems, which are always composed of objective functions, decision variables, and nonlinear constrained. Referring to the “no free lunch” theorem, we cannot theoretically consider an algorithm as a general-purpose universally best optimizer.13 To handle this issue, many researchers have proposed some new optimization algorithms based on natural phenomena, e.g., Charged System Search (CSS), Gray Wolf Optimizer (GWO), Dragonfly Algorithm (DA), Search Group Algorithm (SGA), and Harris hawks optimization (HHO).. The energy of the prey decreases in the HHO algorithm, which happens in the transition from the exploration to exploitation phase. When the ∣E∣ ≥ 0.5, this strategy is named soft besiege, and the positions can be updated as follows: X(t + 1) = ΔX(t) − E∣JXrabbit(t) − X(t)∣,. The last strategy is ∣E∣ ≥ 0.5 and r < 0.5, the prey has not enough energy to escape from the hawks.
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