Firework Algorithm For Multi-Objective Optimization Of A Multimodal Transportation Network Problem

Mnif, Mouna Gargouri; Bouamama, Sadok

doi:10.1016/j.procs.2017.08.189

Cited by 26 publications

(5 citation statements)

References 3 publications

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“…In Equation (23), value(e i ) is the fitness of the elite individual e i and value(pop best ) is the maximum fitness in pop.…”

Section: • Elite Opposition-based Learning Operatormentioning

confidence: 99%

“…In recent years, the fireworks algorithm (FWA) has also been widely applied for multi-objective optimization [19][20][21][22]. Mnif et al [23] used a multi-objective fireworks algorithm to solve the Pareto-optimal solutions of the optimal path planning problem in complex traffic systems and then selected a compromise solution from them. Ali et al [24] established a mathematical model for capacity planning in 5G networks and proposed a discrete fireworks algorithm to solve it.…”

Section: Introductionmentioning

confidence: 99%

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An FW–GA Hybrid Algorithm Combined with Clustering for UAV Forest Fire Reconnaissance Task Assignment

Tian

Hou

2023

Mathematics

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The assignment of tasks for unmanned aerial vehicles (UAVs) during forest fire reconnaissance is a highly complex and large-scale problem. Current task allocation methods struggle to strike a balance between solution speed and effectiveness. In this paper, a two-phase centralized UAV task assignment model based on expectation maximization (EM) clustering and the multidimensional knapsack model (MKP) is proposed for the forest fire reconnaissance task assignment. The fire situation information is acquired using the sensors carried by satellites at first. Then, the EM algorithm based on the Gaussian mixture model (GMM) is applied to get the initial position of every UAV. In the end, the MKP is applied for UAV task assignment based on the initial positions of the UAVs. An improved genetic algorithm (GA) based on the fireworks algorithm (FWA) is proposed for faster iteration speed. A simulation was carried out against the background of forest fires in Liangshan Prefecture, Sichuan Province, and the simulation’s results demonstrate that the task assignment model can quickly and effectively address task allocation problems on a large scale. In addition, the FW–GA hybrid algorithm has great advantages over the traditional GA, particularly in solving time, iteration convergence speed, and solution effectiveness. It can reduce up to 556% of the iteration time and increase objective function value by 1.7% compared to the standard GA. Furthermore, compared to the GA–SA algorithm, its solving time is up to 60 times lower. This paper provides a new idea for future large-scale UAV task assignment problems.

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“…In Equation (23), value(e i ) is the fitness of the elite individual e i and value(pop best ) is the maximum fitness in pop.…”

Section: • Elite Opposition-based Learning Operatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An FW–GA Hybrid Algorithm Combined with Clustering for UAV Forest Fire Reconnaissance Task Assignment

Tian

Hou

2023

Mathematics

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“…Additionally, metaheuristic approaches have gained attention in dealing with NP-hard Problem in the context of multimodal transportation network route optimization with uncertainty elements. A new method of handling multi-objective based on Fire-Works Algorithm (FWA) was introduced by Mnif et al [21], which aimed to determine the shortest and efficient itinerary of satisfying a certain set of demands and operational constraints. Wang et al [30] adopted a Memetic Algorithm (MA) to solve the problem of the hub-and-spoke based road-rail intermodal transportation network design and obtained high-quality solutions.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

The route problem of multimodal transportation with timetable under uncertainty: multi-objective robust optimization model and heuristic approach

2021

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The uncertainty of transportation duration between nodes is an inherent characteristic and should be concerned in the routing optimization of the multimodal transportation network to guarantee the reliability of delivery time. The interval number is used to deal with the uncertainty of transportation duration, and the multi-objective robust optimization model is established which covers the transportation duration and the cost. To solve the combinatorial optimization problem of this study, Non-Dominated Sorting Genetic Algorithm-II (NSGA- II ) is designed, which integrates the (μ+λ) selection method elite retention and the external filing elite retention. Our findings verify the efficiency of the proposed approach by analyzing the diversity, distribution and convergence of the frontier solutions. Finally, near-optimal solutions are obtained with the proposed algorithm in the numerical example. The present study can provide decision reference for multimodal transportation carriers in making transportation plan under uncertainty.

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“…In addition to the above categories of meta-heuristics, Fire-works algorithm is proposed by Tan et al (2010) to simulate the scenario of fireworks explosion. Mnif and Bouamama (2017) introduced Fire-Works Algorithm (FWA) to solve the multi-objective multimodal transportation route optimization problem. Figure 1 is a summary of some classical and popular meta-heuristic algorithms applied to solve the shortest path and multimodal transport route optimization problems in recent years.…”

Section: Introductionmentioning

confidence: 99%

The route problem of multimodal transportation with timetable: stochastic multi-objective optimization model and data-driven simheuristic approach

Peng

Luo

Jiang

et al. 2021

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PurposeDistribution of long-haul goods could be managed via multimodal transportation networks where decision-maker has to consider these factors including the uncertainty of transportation time and cost, the timetable limitation of selected modes and the storage cost incurred in advance or delay arriving of the goods. Considering the above factors comprehensively, this paper establishes a multimodal multi-objective route optimization model which aims to minimize total transportation duration and cost. This study could be used as a reference for decision-maker to transportation plans.Design/methodology/approachMonte Carlo (MC) simulation is introduced to deal with transportation uncertainty and the NSGA-II algorithm with an external archival elite retention strategy is designed. An efficient transformation method based on data-drive to overcome the high time-consuming problem brought by MC simulation. Other contribution of this study is developed a scheme risk assessment method for the non-absolutely optimal Pareto frontier solution set obtained by the NSGA-II algorithm.FindingsNumerical examples verify the effectiveness of the proposed algorithm as it is able to find a high-quality solution and the risk assessment method proposed in this paper can provide support for the route decision.Originality/valueThe impact of timetable on transportation duration is analyzed and making a detailed description in the mathematical model. The uncertain transportation duration and cost are represented by random number that obeys a certain distribution and designed NSGA-II with MC simulation to solve the proposed problem. The data-driven strategy is adopted to reduce the computational time caused by the combination of evolutionary algorithm and MC simulation. The elite retention strategy with external archiving is created to improve the quality of solutions. A risk assessment approach is proposed for the solution scheme and in the numerical simulation experiment.

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Firework Algorithm For Multi-Objective Optimization Of A Multimodal Transportation Network Problem

Cited by 26 publications

References 3 publications

An FW–GA Hybrid Algorithm Combined with Clustering for UAV Forest Fire Reconnaissance Task Assignment

An FW–GA Hybrid Algorithm Combined with Clustering for UAV Forest Fire Reconnaissance Task Assignment

The route problem of multimodal transportation with timetable under uncertainty: multi-objective robust optimization model and heuristic approach

The route problem of multimodal transportation with timetable: stochastic multi-objective optimization model and data-driven simheuristic approach

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