Research on continuous berth allocation optimization based on improved multi-population genetic algorithm

Although discrete berths can reduce the complexity of berth allocation problems, they have the problem of low utilization rate of dock shoreline, while continuous berths can improve the utilization rate of dock shoreline. Traditional genetic algorithms are prone to fall into local optimal value when solving continuous berth allocation problems. The multi-population genetic search algorithm can increase the evolutionary diversity of the population by changing the control parameters of the genetic algorithm. On this basis, the tabu search algorithm is introduced in this paper to randomly select the outstanding individuals of the population for tabu search. The Improved multi-population genetic search algorithm (IMPGA) is designed to enhance the local search ability of the algorithm. This algorithm and the standard Simple genetic algorithm (SGA) were used to solve the problem of continuous berth allocation respectively. Numerical simulation experiments were designed under the conditions of the same length wharf berth shoreline and the same ship arrival data. The results show that: The improved multi-population genetic search algorithm can solve the problem of continuous berths allocation, and the algorithm has faster convergence speed and better optimization results than the common genetic algorithm.