A Discrete Particle Swarm Optimization Algorithm for Archipelago Berth Allocation Problem

Peng, Jianliang; Li, Renjian

doi:10.4304/jcp.9.7.1648-1656

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Peng et al researched the archipelago berth allocation problem . They thought draft, crane and restriction for different ships in the lane [9].…”

Section: Introductionmentioning

confidence: 99%

The Vessel Schedule Optimization Based on Particle Swarm Optimization Algorithm

Zhang

2015

Proceedings of the 5th International Conference on Computer Engineering and Networks — PoS(CENet2015)

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Marine transportation quality is largely affected by the port service level; however, the contextual inquiry shows that the vessel schedule is now a stage of manual schedule. The situation when the vessels gather inbound or outbound may cause port congestion, especially in some adverse weather. Based on the analysis of vessel schedule in the port, this paper proposes a mathematic model for vessel schedule in one-way fairway. In order to calculate the model, we design a code based on the particle position and use discrete particle swarm optimization (DPSO) algorithm to get the schedule order. The simulated test verifies that the algorithm is completely convergent in 150 generation and the maximum waiting time is 117 minutes. The result demonstrates that it can reduce the waiting time when compared with the "First-Come, First-Served" rules.

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“…Peng et al researched the archipelago berth allocation problem . They thought draft, crane and restriction for different ships in the lane [9].…”

Section: Introductionmentioning

confidence: 99%

The Vessel Schedule Optimization Based on Particle Swarm Optimization Algorithm

Zhang

2015

Proceedings of the 5th International Conference on Computer Engineering and Networks — PoS(CENet2015)

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Improved discrete particle swarm optimization using Bee Algorithm and multi-parent crossover method (Case study: Allocation problem and benchmark functions)

Zibaei,

Mesgari

2024

Preprint

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Compared to other techniques, particle swarm optimization is more frequently utilized because of its ease of use and low variability. However, it is complicated to find the best possible solution in the search space in large-scale optimization problems. Moreover, changing algorithm variables does not influence algorithm convergence much. The PSO algorithm can be combined with other algorithms. It can use their advantages and operators to solve this problem. Therefore, this paper proposes the onlooker multi-parent crossover discrete particle swarm optimization (OMPCDPSO). To improve the efficiency of the DPSO algorithm, we utilized multi-parent crossover on the best solutions. We performed an independent and intensive neighborhood search using the onlooker bees of the bee algorithm. The algorithm uses onlooker bees and crossover. They do local search (exploitation) and global search (exploration). Each of these searches is among the best solutions (employed bees). The proposed algorithm was tested on the allocation problem, which is an NP-hard optimization problem. Also, we used two types of simulated data. They were used to test the scalability and complexity of the better algorithm. Also, fourteen 2D test functions and thirteen 30D test functions were used. They also used twenty IEEE CEC2005 benchmark functions to test the efficiency of OMPCDPSO. Also, to test OMPCDPSO's performance, we compared it to four new binary optimization algorithms and three classic ones. The results show that the OMPCDPSO version had high capability. It performed better than other algorithms. The developed algorithm in this research (OMCDPSO) in 36 test functions out of 47 (76.60%) is better than other algorithms. The OMPCDPSO algorithm used many parts of the best solution. It put them in the multi-parent crossover and neighborhood search with onlookers. This made it better than DPSO. The Onlooker bees and multi-parent operators significantly impact the algorithm's performance.

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A Discrete Particle Swarm Optimization Algorithm for Archipelago Berth Allocation Problem

Cited by 2 publications

References 17 publications

The Vessel Schedule Optimization Based on Particle Swarm Optimization Algorithm

The Vessel Schedule Optimization Based on Particle Swarm Optimization Algorithm

Improved discrete particle swarm optimization using Bee Algorithm and multi-parent crossover method (Case study: Allocation problem and benchmark functions)

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