Engineering optimization method of Harris hawks algorithm based on multi-strategy enhancement

Abstract

The invention discloses an engineering optimization method of a Harris hawks algorithm based on multi-strategy enhancement. The engineering optimization method comprises the following steps: S1, constructing an optimization objective function of an actual engineering problem; S2, initializing parameters; S3, initializing the population position of the Harris hawks; calculating the fitness value ofeach Harris hawk in the initialized population according to the target function; S5, dynamically simulating the motion state of the prey and the execution state of the population so as to find the best execution strategy for each Harris hawk; s6, performing an exploration stage; s7, performing a development stage; s8, updating the position of the whole population through the steps S5 and S6, andenabling the updated new population to pass through an objective function and a constraint condition; s9, judging whether the maximum number of iterations is reached or not. The method has the following advantages and effects that a better solution can be found in an actual engineering optimization problem, and the precision of the solution of the constraint-containing engineering optimization problem is improved within limited time.

Description

technical field [0001] The invention relates to an engineering optimization method based on multi-strategy enhanced Harris eagle algorithm. Background technique [0002] The swarm intelligence algorithm is a non-deterministic optimization algorithm that simulates the social behavior and predation behavior of different biological groups in nature. Unlike the deterministic optimal algorithm, the swarm intelligence algorithm obtains an approximate solution to the problem. But compared with the deterministic algorithm, the swarm intelligence algorithm does not need the mathematical information of the optimization problem. At the same time, when dealing with large-scale optimization problems, compared with the deterministic algorithm, it can quickly obtain the approximate solution of the optimal solution. Real problems offer possible solutions. As real problems become more and more complex and people have higher and higher requirements for solution time, swarm intelligence optim...

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Problems solved by technology

[0004] However, when the Harris Hawk Optimization Algorithm (HHO) deals with complex optimization problems with a large number of local optimal solutions, it is easy to fall into local optimum. The algorithm's exploration ability needs to be strengthened, and its conve

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