A Multioffspring Genetic Algorithm Based on Sorting Grouping Selection and Combination Pairing Crossover

A multi-offspring genetic algorithm based on sorting grouping selection and combination pairing crossover was proposed in this paper. First, individuals the population were sorted according to their objective function values, remaining divided into several groups after eliminating worst. Then, operation, which has advantage of a simplified calculation that can be easily performed, implemented. Second, operator designed. Individuals from different selected for new combinations. In combination, if condition met, transposons operation made. Otherwise, offspring copies used, increase opportunity generating better by producing multi-offspring. Finally, formed adopting basic bit mutation operator, elitism policy strategy competition. Moreover, catastrophe mechanism been introduced improved avoid premature convergence. The test results functions CEC 2017 suites shown paper search performance, stability faster convergence global optimal solution. These thus verified effectiveness feasibility Applying location optimization problem agricultural product logistics facilities, it is an effective method solve optimization.