Multi-objective Routing Optimization Research Articles

Energy Oriented Multi-Objective Route Optimization Using Donkey Smuggler Optimization in Cluster Based Wireless Sensor Networks

Energy Oriented Multi-Objective Route Optimization Using Donkey Smuggler Optimization in Cluster Based Wireless Sensor Networks

Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters

Unmanned aerial vehicle (UAV) clusters offer significant potential in civil, military, and commercial fields due to their flexibility and cooperative capabilities. However, characteristics such as dynamic topology and limited energy storage bring challenges to the design of routing protocols for UAV networks. This study leverages the Deep Double Q-Learning Network (DDQN) algorithm to optimize the traditional Greedy Perimeter Stateless Routing (GPSR) protocol, resulting in a multi-objective optimized GPSR routing protocol (DDQN-MTGPSR). By constructing a multi-objective routing optimization model through cross-layer data fusion, the proposed approach aims to enhance UAV network communication performance comprehensively. In addition, this study develops the above DDQN-MTGPSR intelligent routing algorithm based on the NS-3 platform and uses an artificial intelligence framework. In order to verify the effectiveness of the DDQN-MTGPSR algorithm, it is simulated and compared with the traditional ad hoc routing protocols, and the experimental results show that compared with the GPSR protocol, the DDQN-MTGPSR has achieved significant optimization in the key metrics such as the average end-to-end delay, packet delivery rate, node average residual energy variance and percentage of node average residual energy. In high dynamic scenarios, the above indicators were optimized by 20.05%, 12.72%, 0.47%, and 50.15%, respectively, while optimizing 36.31%, 26.26%, 8.709%, and 69.3% in large-scale scenarios, respectively.

Multiobjective Routing Optimization to Support IoUT Applications in UWSNs

Multiobjective Routing Optimization to Support IoUT Applications in UWSNs

Route Optimization of Hazardous Material (Hazmat) Transportation with Risk Equity and Time-Varying Risk

This paper strives for route optimization of hazardous material (hazmat) for given probabilities and consequences. We aim to not only develop a model to determine the route with the least total risk and cost, but also pursue risk equity from three perspectives of government, enterprises, and society in the route optimization. Specifically, this paper reduces the differences of population exposure risk among road segments by using the threshold setting method and decreases the differences of environmental risk by minimizing the risk compensation cost caused by the additional environmental risk exceeding its average for each road segment. In addition, this paper analyzes the impact of the time-varying characteristics of risk and risk equity and proposes a multi-objective route optimization model which considers the minimization of total risk, cost, and risk differences under time-varying conditions. Finally, the ε-constraint method and an improved genetic algorithm are used to optimize the model, which is applied to a case study involving hazardous materials transportation route optimization in Shanghai, China. The resulting analyses indicate that risk equity can be improved by setting a proper threshold to acquire feasible paths and by minimizing the compensation cost to control the differences of environmental risk. Considering variance of time in the route optimization may lead to a route with lower risk and better risk equity.

Multimodal transportation routing optimization based on multi-objective Q-learning under time uncertainty

Multimodal transportation is a modern way of cargo transportation. With the increasing demand for cargo transportation, higher requirements are being placed on multimodal transportation multi-objective routing optimization. In multimodal transportation multi-objective routing optimization, in response to the limitations of classical algorithms in solving large-scale problems with multiple nodes and modes of transport, the limitations of directed transportation networks in the application, and the uncertainty of transport time, this paper proposes an optimization framework based on multi-objective weighted sum Q-learning, combined with the proposed undirected multiple-node network, and characterizes the uncertainty of time with a positively skewed distribution. The undirected multiple-node transportation network can better simulate cargo transportation and characterize transfer information, facilitate the modification of origin and destination, and avoid suboptimal solutions due to the manual setting of wrong route directions. The network is combined with weighted sum Q-learning to solve multimodal transportation multi-objective routing optimization problems faster and better. When modeling the uncertainty of transport time, a positively skewed distribution is used. The three objectives of transport cost, carbon emission cost, and transport time were studied and compared with PSO, GA, AFO, NSGA-II, and MOPSO. The experimental results show that compared with PSO, GA, and AFO using a directed transportation network, the proposed method has a significant improvement in optimization results and running time, and the running time is shortened by 26 times. The proposed method can better solve the boundary of the Pareto front and dominate the partial solutions of NSGA-II and MOPSO. The effect of time uncertainty on the performance of the algorithm is more significant in transport orders with high time weight. With the increase in uncertainty, the reliability of the route decreases. The effectiveness of the proposed method is verified.

Multi-Objective Routing Optimization in Electric and Flying Vehicles: A Genetic Algorithm Perspective

The advent of electric and flying vehicles (EnFVs) has brought significant advancements to the transportation industry, offering improved sustainability, reduced congestion, and enhanced mobility. However, the efficient routing of messages in EnFVs presents unique challenges that demand specialized algorithms to address their specific constraints and objectives. This study analyzes several case studies that investigate the effectiveness of genetic algorithms (GAs) in optimizing routing for EnFVs. The major contributions of this research lie in demonstrating the capability of GAs to handle complex optimization problems with multiple objectives, enabling the simultaneous consideration of factors like energy efficiency, travel time, and vehicle utilization. Moreover, GAs offer a flexible and adaptive approach to finding near-optimal solutions in dynamic transportation systems, making them suitable for real-world EnFV networks. While GAs show promise, there are also limitations, such as computational complexity, difficulty in capturing real-world constraints, and potential sub-optimal solutions. Addressing these challenges, the study highlights several future research directions, including the integration of real-time data and dynamic routing updates, hybrid approaches with other optimization techniques, consideration of uncertainty and risk management, scalability for large-scale routing problems, and enhancing energy efficiency and sustainability in routing. By exploring these avenues, researchers can further improve the efficiency and effectiveness of routing algorithms for EnFVs, paving the way for their seamless integration into modern transportation systems.

Heuristics for Multi-Vehicle Routing Problem Considering Human-Robot Interactions

Autonomous mobile robots (AMRs) are being used extensively in civilian and military applications for applications such as underground mining, nuclear plant operations, planetary exploration, intelligence, surveillance and reconnaissance (ISR) missions and manned-unmanned teaming. We consider a multi-objective, multiple-vehicle routing problem in which teams of manned ground vehicles (MGVs) and AMRs are deployed respectively in a leader-follower framework to execute missions with differing requirements for MGVs and AMRs while considering human-robot interactions (HRI). HRI studies highlight the costs of managing a team of follower AMRs by a leader MGV. This paper aims to compute feasible visit sequences, replenishments, team compositions and number of MGV-AMR teams deployed such that the requirements for MGVs and AMRs for the missions are met and the routing, replenishment, HRI and team deployment costs are at minimum. The problem is first modeled as a a mixed-integer linear program (MILP) that can be solved to optimality by off-the-shelf commercial solvers for small-sized instances. For larger instances, a variable neighborhood search algorithm is offered to compute near optimal solutions and address the challenges that arise when solving the combinatorial multi-objective routing optimization problem. Finally, computational experiments that corroborate the effectiveness of the proposed algorithms are presented.

Multi-objective route optimization of urban cold chain distribution using electric and diesel powered vehicles

Increasingly serious urban road congestion and environmental pollution have led some cities to impose restrictions such as limiting certain types of vehicles and the areas in which they can operate and introducing subsidies and other incentives for electric refrigerated vehicles. As a result, both internal combustion engine (ICE) refrigerated vehicles and electric refrigerated vehicles will be used together in urban cold chain distribution for a long time into the future. Most current studies are concerned with mixed vehicle type allocation or mixed vehicle routing problems. Although there are some studies that combine the two, there has been little research into the effects of low carbon emission and multiple objective optimization on distribution and routing for a mixture of vehicle types. This paper describes the creation of an optimized decision-making cold chain distribution model that includes different types of distribution vehicles and different route optimization criteria that represent the three enterprise objectives of minimal cost, minimum carbon emissions and minimum distance. The decision criteria are influenced by conflicting benefits offered by ICE and electric refrigerated vehicles as well as government subsidies for electric refrigerated vehicles and carbon emission trading by logistics enterprises. A chaotic particle swarm optimization algorithm was used to analyze a cold chain distribution system in Wuhan, Hubei Province, China. The results show that: (1) The choice of enterprise distribution models and the vehicles and routes determined are closely related to enterprise goals. To minimize total cost, ICE refrigerated trucks with less load capacity and electric refrigerated trucks with greater load capacity are preferred. To minimize carbon emissions, ICE refrigerated trucks and electric refrigerated trucks with less load capacity are preferred. To minimize total distance, ICE refrigerated trucks and electric refrigerated trucks with greater load capacity are preferred. (2) As the value of government subsidies increases, the total load capacity of electric refrigerated vehicles also increases, the distribution routes change, and total carbon emissions and total distribution costs decrease. (3) When carbon emission trading prices increase, the number of electric refrigerated vehicles gradually increases, the distribution path changes, the total distribution costs increase, and total carbon emissions decrease. Some suggestions are made for government to determine subsidies for electric refrigerated vehicles and carbon emission trading policies that will guide the choice of distribution vehicles and affect the criteria used for route optimization by cold chain logistics enterprises.

Multi-Objective Routing Optimization for 6G Communication Networks Using a Quantum Approximate Optimization Algorithm.

Sixth-generation wireless (6G) technology has been focused on in the wireless research community. Global coverage, massive spectrum usage, complex new applications, and strong security are among the new paradigms introduced by 6G. However, realizing such features may require computation capabilities transcending those of present (classical) computers. Large technology companies are already exploring quantum computers, which could be adopted as potential technological enablers for 6G. This is a promising avenue to explore because quantum computers exploit the properties of quantum states to perform certain computations significantly faster than classical computers. This paper focuses on routing optimization in wireless mesh networks using quantum computers, explicitly applying the quantum approximate optimization algorithm (QAOA). Single-objective and multi-objective examples are presented as robust candidates for the application of quantum machine learning. Moreover, a discussion about quantum supremacy estimation for this problem is provided.

QoS Routing Algorithm for OBS Networks Based on a Multi-Objective Genetic Algorithm

To optimize the QoS of optical burst switching networks, a QoS routing optimization algorithm based on a multi-objective genetic algorithm is proposed. A Bayesian network model is used to locate the fault of optical burst switching network and obtain the fault location of the transmission link of optical burst switching network; In this position, the routing optimization algorithm based on a multi-objective genetic algorithm transforms the multi constrained network quality of service routing optimization problem into a constrained multi-objective routing optimization problem. Under multiple constraints, the best path of optical burst switching network service is obtained to realize the optical burst switching network quality of service routing optimization. The results show that after applying the proposed algorithm, the average delay of video, text and picture transmission in an optical burst switching network is less than 400ms. The proposed algorithm can improve the packet delivery rate of information transmission in an optical burst switching network, reduce the transmission delay, blocking probability and use cost of an optical burst switching network, and optimize the service quality of an optical burst switching network.

A model of multi-objective route optimization for a vessel in drifting ice

The paper suggests a three-dimensional graph model for a spatio-temporal representation of a water area with drifting ice objects. The article considers multi-objective route optimization with both additive and minimax criteria. The algorithmic solution of multi-objective optimization is proposed for a set of objective functions, which correspond to the following route parameters: the route length, risks along the route and the number of course direction changes. The method presented in the paper allows finding a set of Pareto-optimal solutions.The paper suggests the algorithm for arranging three-dimensional graph in relation to motion laws of ice objects existing in the considered water area. For illustrating the method, the article provides a computational example of path optimization. The paper also includes the discussion on the parameters influencing computational complexity of practical implementation of the developed method as well as on the approach in general.The developed model can be implemented in designing marine transportation systems and navigation assisting in ice infested water areas.

Multiobjective routing optimization of mobile charging vehicles for UAV power supply guarantees

A novel power supply guarantee model based on mobile charging vehicles (MCVs) is developed for solving the battery capacity constraint problem of unmanned aerial vehicles (UAVs) in performing long-term and large-scale missions. In this model, the charging vehicles travel to charging points located under the routes of the UAVs to be charged wirelessly, maximizing the assurance of completing reconnaissance missions. This paper focuses on the scheduling and routing of charging vehicles and achieves multisystem cooperation by constructing two submodels, namely, a UAV reconnaissance routing model and an MCV charging routing model. Furthermore, an improved coevolutionary framework for constrained multiobjective optimization problems with a generalized opposition-based learning strategy (OL-CCMO) is proposed to optimize the two objectives of power supply guarantee investment cost and reconnaissance mission time window deviation. Three scenarios of UAV reconnaissance missions with different levels of complexity are built in the case studies, in which the charging vehicle routes are optimized with multiple algorithms, demonstrating the superiority and applicability of the proposed algorithm by comparison.

Improved Wolf Pack Algorithm for Solving a Class of Multi-Objective Route Optimization Problem

This paper considers a class of Multi-objective path planning and solving algorithms. The choice of step size is a very important factor that affects the iterative algorithm. This paper improves the classical Wolf Pack Algorithm (WPA) by designing an adaptive step size. This new improved wolf pack algorithm (IWPA) improves the classical wolf colony algorithm by designing an adaptive step size. It effectively solves the problem that the WPA may fall into a endless loop. The simulation results by MATLAB show that the IWPA has a good effectiveness than the WPA to solve multi-objective route optimization.

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

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.

Hierarchical Bayesian approach for improving weights for solving multi-objective route optimization problem

The weighted sum method is a simple and widely used technique that scalarizes multiple conflicting objectives into a single objective function. It suffers from the problem of determining the appropriate weights corresponding to the objectives. This paper proposes a novel Hierarchical Bayesian model based on multinomial distribution and Dirichlet prior to refine the weights for solving such multi-objective route optimization problems. The model and methodologies revolve around data obtained from a small-scale pilot survey. The method aims at improving the existing methods of weight determination in the field of Intelligent Transport Systems as data driven choice of weights through appropriate probabilistic modelling ensures, on an average, much reliable results than non-probabilistic techniques. Application of this model and methodologies to simulated as well as real data sets revealed quite encouraging performances with respect to stabilizing the estimates of weights. Generation of weights using the proposed Bayesian methodology can be used to develop a bona-fide Bayesian posterior distribution for the optima, thus properly and coherently quantifying the uncertainty about the optima.

Simulation of Sports Venue Based on Ant Colony Algorithm and Artificial Intelligence

In order to improve the congestion of the evacuation plan and further improve the evacuation efficiency, this paper proposes the priority Pareto partial order relation and the vector pheromone routing method based on the priority Pareto partial order relation. Numerical experiments show that compared with the hierarchical multiobjective evacuation path optimization algorithm based on the hierarchical network, the fragmented multiobjective evacuation path optimization algorithm proposed in this paper effectively improves the evacuation efficiency of the evacuation plan and the convergence of the noninferior plan set. However, the congestion condition of the noninferior evacuation plan obtained by the fragmented multiobjective evacuation route optimization algorithm is worse than the congestion condition of the noninferior evacuation plan obtained by the hierarchical multiobjective evacuation route optimization algorithm. The multiple factors that affect the routing process considered in the probability transfer function used in the traditional ant colony algorithm routing method must be independent of each other. However, in actual route selection, multiple factors that affect route selection are not necessarily independent of each other. In order to fully consider the various factors that affect the routing, this paper adopts the vector pheromone routing method based on the traditional Pareto partial order relationship instead of the traditional ant colony algorithm. The model mainly improves the original pheromone distribution and volatilization coefficient of the ant colony, speeds up the convergence speed and accuracy of the algorithm, and obtains ideal candidate solutions. The method is applied to the location of sports facilities and has achieved good results. The experimental results show that the improved ant colony algorithm model designed in this paper is suitable for solving the problem of urban sports facilities location in large‐scale space.

A multi-objective routing optimization model for ship intelligent navigation

In order to solve the problem of ship route planning in the autonomous navigation and decision-making of ships, based on the electronic chart display and information system (ECDIS), the meteorological conditions of ocean-going ships were analyzed and the grid method was used to establish an environmental model. The voyage time, fuel consumption and navigation safety were taken as optimization goals, and a multi-objective ship route optimization model was established, then the non-dominated sorting genetic algorithm with elite strategy (NSGA-II) was applied in the optimization route searching to realize the solution of the optimal ship route.

Multi-objective routing aware of mixed IoT traffic for low-cost wireless Backhauls

The futuristic wireless networks expects to provide adequate support for distinct kind of applications, their diverse requirements, and scenarios for future Internet systems, such as Internet of Things based on multimedia and sensor data, while figuring out low cost solutions to offload the mobile communication core. In this context, Low-cost Wireless Backhauls (LWBs) can be useful, since they are based on cheap WLAN technologies, such as Wireless Mesh Networks that provide capacity for future IoT applications based on mixed traffic. The routing is a fundamental process to provide communication in these multi-hop networks and multi-objective routing optimization algorithms based on Integer Linear Programming (ILP) models have been studied in the literature to address this problem, but there is a lack of solutions for mixed traffic. For this reason, we propose a novel ILP multi-objective approach, called Multi-objective routing Aware of miXed traffIc (MAXI), which employs three weighted objectives to guide the routing in WMNs with different applications and requirements. In addition, we provide a comparative analysis with other relevant approaches of routing using NS-3 to evaluation based on simulation, that takes into account different types and levels of interference (e.g. co-channel interference and external interference) focused on mixed IoT traffic for elderly healthcare scenario. Finally, we demonstrate the effectiveness of the proposed approach to support the requirements of each application through the appropriate combination of objective functions, mainly in dense scenarios with high level of interference.

Quantum-Aided Multi-Objective Routing Optimization Using Back-Tracing-Aided Dynamic Programming

Pareto optimality is capable of striking the optimal trade-off amongst the diverse conflicting QoS requirements of routing in wireless multihop networks. However, this comes at the cost of increased complexity owing to searching through the extended multi-objective search-space. We will demonstrate that the powerful quantum-assisted dynamic programming optimization framework is capable of circumventing this problem. In this context, the so-called Evolutionary Quantum Pareto Optimization (EQPO) algorithm has been proposed, which is capable of identifying most of the optimal routes at a near-polynomial complexity versus the number of nodes. As a benefit, we improve both the the EQPO algorithm by introducing a back-tracing process. We also demonstrate that the improved algorithm, namely the Back-Tracing-Aided EQPO (BTA-EQPO) algorithm, imposes a negligible complexity overhead, while substantially improving our performance metrics, namely the relative frequency of finding all Pareto-optimal solutions and the probability that the Pareto-optimal solutions are indeed part of the optimal Pareto front

MORP

The Wireless Power Transfer (WPT) system that enables in-motion charging (or wireless charging) for Electric Vehicles (EVs) has been introduced to resolve battery-related issues (such as long charging time, high cost, and short driving range) and increase the wide-acceptance of EVs. In this paper, we study the WPT system with the objectives of minimizing energy consumption, travel time, charging monetary cost on the way, and range anxiety for online EVs. Specifically, we propose the Multi-Objective Route Planner system (MORP) to guide EVs for the multi-objective routing. MORP incorporates two components: traffic state prediction and optimal route determination. For the traffic state prediction, we conducted analysis on a traffic dataset and observed spatial-temporal features of traffic patterns. Accordingly, we introduce the horizontal space-time Autoregressive Integrated Moving Average (ARIMA) model to predict vehicle counts (i.e., traffic volume) for locations with available historical traffic data. And, we use the spatial-temporal ordinary kriging method to predict vehicle counts for locations without historical traffic data. Based on vehicle counts, we use the non-parametric kernel regression method to predict velocity of road sections, which is used to predict travel time and then, energy consumption of a route of an EV with the help of the proposed energy consumption model. We also estimate charging monetary cost and EV related range anxiety based on unit energy cost, predicted travel time and energy consumption, and current onboard energy. We design four different cost functions (travel time, energy consumption, charging monetary cost, and range anxiety) of routing and formulate a multi-objective routing optimization problem. We use the predicted parameters as inputs of the optimization problem and find the optimal route using the adaptive epsilon constraint method. We evaluate our proposed MORP system in four different aspects (including traffic prediction, velocity prediction, energy consumption prediction, and EV routing). From the experimental studies, we find the effectiveness of the proposed MORP system in different aspects of the online EV routing system.