Application of improved GA method in multi-objective programming for water pollution control systems

Simplex Genetic Algorithm (SGA) is a highly efficient stochastic search algorithm, which emulates biological evolution process. The algorithm adopts the theory of survival of the fittest, requires only simple numerical calculations, processes parameters self-fitting optimization under evolution rules and obtains Pareto optimum solutions at the end. But SGA has its inherent limitations. premature and low research efficiency are hard to conquer in practice. Thus in this paper, an improved Genetic Algorithm method is proposed to solve the multi-objective programming for water pollution control systems. The algorithm adopts decimal system coding theory, variable weighting comprehensive distance evaluation model and multiple-chromosomes crossover mechanism, which will facilitate the solving process and get satisfied optimal results. The paper also uses it to solve an example and proves the effectiveness of the algorithm.