An Adaptive Ant Colony System Based on Variable Range Receding Horizon Control for Berth Allocation Problem

Abstract

The berth allocation problem (BAP) is an NP-hard problem in maritime traffic scheduling that significantly influences the operational efficiency of the container terminal. This paper formulates the BAP as a permutation-based combinatorial optimization problem and proposes an improved ant colony system (ACS) algorithm to solve it. The proposed ACS has three main contributions. First, an adaptive heuristic information (AHI) mechanism is proposed to help ACS handle the discrete and real-time difficulties of BAP. Second, to relieve the computational burden, a divide-and-conquer strategy based on variable-range receding horizon control (vRHC) is designed to divide the complete BAP into a set of sub-BAPs. Third, a partial solution memory (PSM) mechanism is proposed to accelerate the ACS convergence process in each receding horizon (i.e., each sub-BAP). The proposed algorithm is termed as adaptive ACS (AACS) with vRHC strategy and PSM mechanism. The performance of the AACS is comprehensively tested on a set of test cases with different scales. Experimental results show that the effectiveness and robustness of AACS are generally better than the compared state-of-the-art algorithms, including the well-performing adaptive evolutionary algorithm and ant colony optimization algorithm. Moreover, comprehensive investigations are conducted to evaluate the influences of the AHI mechanism, the vRHC strategy, and the PSM mechanism on the performance of the AACS algorithm.

A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization

“…Keeping this in mind, we will target our future work on the new inter-task similarity measurement mechanism. In addition, we will apply the BoKT framework to other evolutionary computation algorithms like particle swarm optimization [52]- [54] and ant colony optimization [55]- [57], and also extend the BoKTbased algorithms to help efficiently solve some challenging real-world optimization problems [58]- [60].…”

A survey on computational intelligence approaches for intelligent marine terminal operations

“…The author of [60] solves the DD‐BAP while proposing an adaptive ant colony system (AACS) by hybridizing three various methods: (i) adaptive heuristic information (AHI) is used to deal with real‐time difficulties of DD‐BAP, (ii) variable range receding horizon is employed to divide the complete space into small parts, and (iii) partial memory unit is developed to quicken the convergence speed of whole system (AACS). An exact method and two metaheuristics are also implemented for comparison purposes.…”

Research on application of coevolutionary ant colony optimization algorithm based on hybrid breeding strategy

“…Wu et al [3] added direction information guidance in the iterative process of the ant colony optimization algorithm, improved the state transition rules. Wang et al [4] introduced adaptive heuristic information into ant colony optimization algorithms to deal with the discrete and real-time difficulties in berth allocation problems. However, ACO also have some shortcomings, such as high time complexity and being prone to falling into local optima.…”