Constraint genetic algorithm and its application in sintering proportioning

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Abstract
This paper puts forward a method for constrained optimization problems based on self-adaptive penalty function and improved genetic algorithm. In order to improve the speed of convergence and avoid premature convergence, a method based on good-point set theory has been proposed. By using good point set method for generating initial population, the initial population is uniformly distributed in the solution space. This paper Designs an elite reverse learning strategy, and proposes a mechanism to automatically adjust the crossover probability according to the individual advantages and disadvantages. The tests indicate that the proposed constrained genetic algorithm is efficient and feasible.

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