Evolutionary Computation for Intelligent Transportation in Smart Cities: A Survey [Review Article]

Chen, Zong-Gan; Zhan, Zhi-Hui; Zhang, Jun

doi:10.1109/mci.2022.3155330

Cited by 56 publications

(16 citation statements)

References 189 publications

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“…Constraint (5) shows decision variables x vb of the BAP model. The assigned berthing time ABT v of Equation (1) for each vessel to be served at berth b (b ∈ B) is calculated by…”

Section: B Objective Formulationmentioning

confidence: 99%

“…In the first receding horizon (i.e., K = 1), the time interval of each allocation window is the same. We therefore set W 1 1 = W 0 in step 1. The W 0 in every receding horizon is the same, and we simply use W 0 to represent all the W K t (t > 1) herein and in Algorithm 2.…”

Section: End If 16 End For 17 If Min(p Atmentioning

confidence: 99%

“…In this section, the impact of parameters N RHC and W 1 1 on the performance of AACS is investigated. We set parameter N RHC from 1 to 6 with a step length of 1.…”

Section: E Analysis Of Vrhc Parametersmentioning

confidence: 99%

“…T HE globalization of the world economy has stimulated the rapid increase in import and export volumes of cargo and resulted in the development of the international logistics industry. Maritime transport is a vital link in the global logistics supply chain [1]. The United Nations Conference on Trade and Development indicates that approximately 80% of goods of international trade are carried by sea, and this figure is even higher among developing countries [2].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Jiang

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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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.

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“…Constraint (5) shows decision variables x vb of the BAP model. The assigned berthing time ABT v of Equation (1) for each vessel to be served at berth b (b ∈ B) is calculated by…”

Section: B Objective Formulationmentioning

confidence: 99%

Section: End If 16 End For 17 If Min(p Atmentioning

confidence: 99%

“…In this section, the impact of parameters N RHC and W 1 1 on the performance of AACS is investigated. We set parameter N RHC from 1 to 6 with a step length of 1.…”

Section: E Analysis Of Vrhc Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wang

Jiang

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…, is a kind of evolutionary algorithm (EA) that solves optimization problems by iterations of mutation, crossover, and selection [3]- [8]. So far, DE has been successfully applied to a wide range of complex optimization problems [9]- [11], such as multi-objective optimization [12], many-objective optimization [13], constrained optimization [14] [15], multimodal optimization [16]- [19], dynamic optimization [20][21], expensive optimization [22] [23], and multitask optimization [24] [25].…”

Section: Introduction Ifferential Evolution (De) Proposed By Storn An...mentioning

confidence: 99%

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Wang

Jian

Zhan

et al. 2023

IEEE Trans. Evol. Computat.

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Large-scale optimization problems (LSOPs) are challenging because the algorithm is difficult in balancing too many dimensions and in escaping from trapped bottleneck dimensions. To improve solutions, this paper introduces targeted modification to the certain values in the bottleneck dimensions. Analogous to gene targeting (GT) in biotechnology, we experiment on targeting the specific genes in candidate solution to improve its trait in differential evolution (DE). We propose a simple and efficient method, called GT-based DE (GTDE), to solve LSOPs. In the algorithm design, a simple GT-based modification is developed to perform on the best individual, comprising probabilistically targeting the location of bottleneck dimensions, constructing a homologous targeting vector, and inserting the targeting vector into the best individual. In this way, all the bottleneck dimensions of the best individual can be probabilistically targeted and modified to break the bottleneck and to provide global guidance for more optimal evolution. Note that the GT is only performed on the globally best individual and is just carried out as a simple operator that is added to the standard DE. Experimental studies compare the GTDE with some other state-of-the-art large-scale optimization algorithms, including the winners of CEC2010, CEC2012, CEC2013, and CEC2018 competitions on large-scale optimization. The results show that the GTDE is efficient and performs better than or at least comparable to the others in solving LSOPs.Manuscript received….revised….accepted….

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Evolutionary Computation for Berth Allocation Problems: A Survey

Xu,

Jiang,

Zhang

et al. 2023

Lecture Notes in Computer Science

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Evolutionary Computation for Intelligent Transportation in Smart Cities: A Survey [Review Article]

Cited by 56 publications

References 189 publications

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

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

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Evolutionary Computation for Berth Allocation Problems: A Survey

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