Multi-objective Path Research Articles

Finding all Pareto optimal paths by simulating ripple relay race in multi-objective networks

This paper proposes a novel nature-inspired method, so-called ripple-spreading algorithm (RSA) for multi-objective path optimization problem (MOPOP). Unlike most existing methods mainly capable of finding partial or approximated Pareto front, this paper focuses on calculating the complete Pareto front. This is achieved by taking advantage of the optimality principle in natural ripple-spreading phenomenon. Basically, the proposed RSA carries out a one-off ripple relay race in the route network, and then the complete Pareto front will be identified with guaranteed optimality by backtracking those Pareto non-dominated ripples (PNDRs) which reached the destination node. Theoretical analyses and comprehensive experiments show that all complete Pareto fronts of a one-to-all MOPOP can also be found in just a single run of ripple relay race, and the reported method can be further extended to calculate all Pareto optimal paths in dynamical networks, which have rarely been touched by existing MOPOP methods. Since many real-world application problems can be converted into MOPOP, the reported method has a great potential of applications.

Multi- and many-objective path-relinking: A taxonomy and decomposition approach

This paper proposes a decomposition-based path-relinking method for multi- and many-objective combinatorial optimization problems. The proposed approach, referred to as MOPR/D, considers the localization of solutions in the objective space and can handle this information to make choices regarding intermediate solutions in the path-relinking trajectory. Because it does not require aggregation functions, it does not suffer from a tendency to introduce search bias to specific regions of the Pareto front. The proposed approach was compared with seven path-relinking techniques from the literature on three differently structured combinatorial optimization problems: 0/1 multidimensional knapsack, quadratic assignment, and spanning tree. Experiments were conducted on sets of benchmark instances with up to five objectives. This study also reviewed multi-objective path generation strategies, investigated their behaviors, and a taxonomy is proposed for standardizing and classifying them.

Design of Dynamic Programming Model for Multi-Objective Cold Chain Logistics Deployment Path Based on Meme Algorithm

Design of Dynamic Programming Model for Multi-Objective Cold Chain Logistics Deployment Path Based on Meme Algorithm

Multi-objective Path Planning Based on K-D Fusion Algorithm

How to arrange sales staff to visit offline stores reasonably is a critical task in the Fast Moving Consumer Goods (FMCG) industry. Based on the K-means and Dijkstra algorithms (K-D fusion algorithm), this paper proposed an algorithm to automatically allocate offline stores for sales staff and optimize the visiting path, thereby improving management efficiency. A new initial cluster center selection approach was proposed for the K-means algorithm to select its initial clustering center with the consideration of outlier points. The sales staff’s visiting path to the store was planned by the Dijkstra algorithm. The performance of our K-D fusion algorithm was evaluated in terms of grouping rationality and path planning optimization. Experimental results show that our algorithm can comprehensively consider several factors such as the number of stores, store types, and geographical locations, and distribute the workload more evenly to all the sales staff. In addition, it can also optimize the visiting path for sales staff, which can effectively improve the efficiency of sales staff to visit offline stores.

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.

Multiobjective Path Optimization for Arc Welding Robot Based on DMOEA/D-ET Algorithm and Proxy Model

In the arc welding process, a reasonable welding path can improve welding efficiency and quality, especially when a large number of welding seams exist. Hence, this article introduces an intelligent path optimization strategy to optimize the sequence of welding seams. The shortest path length, power consumption, and welding deformation are considered optimization objectives, and welding deformation is studied based on the proxy model to improve optimization efficiency. Then, the improved discrete multiobjective decomposition algorithm based on event-triggering strategy (DMOEA/D-ET) is proposed. Here, the grid method and decomposition multiobjective algorithm (MOEA/D) are used to improve search performance. The adaptive neighborhood strategy is used to improve the quality and distribution of noninferior solutions. After the comparison with NSGA-II, NSGA-III, and CGMOPSO algorithms, the DMOEA/D-ET algorithm shows better performance on both convergence and diversity. Finally, the proposed strategy is used for welding robot path planning, and the simulation results show its effectiveness.

One-exact approximate Pareto sets

Papadimitriou and Yannakakis (Proceedings of the 41st annual IEEE symposium on the Foundations of Computer Science (FOCS), pp 86–92, 2000) show that the polynomial-time solvability of a certain auxiliary problem determines the class of multiobjective optimization problems that admit a polynomial-time computable (1+varepsilon , dots , 1+varepsilon )-approximate Pareto set (also called an varepsilon -Pareto set). Similarly, in this article, we characterize the class of multiobjective optimization problems having a polynomial-time computable approximate varepsilon -Pareto set that is exact in one objective by the efficient solvability of an appropriate auxiliary problem. This class includes important problems such as multiobjective shortest path and spanning tree, and the approximation guarantee we provide is, in general, best possible. Furthermore, for biobjective optimization problems from this class, we provide an algorithm that computes a one-exact varepsilon -Pareto set of cardinality at most twice the cardinality of a smallest such set and show that this factor of 2 is best possible. For three or more objective functions, however, we prove that no constant-factor approximation on the cardinality of the set can be obtained efficiently.

Production Logistics Management of Industrial Enterprises Based on Wavelet Neural Network

With an efficient production logistics system, intelligent manufacturers can reduce the investment in production, improve the stability and self-repair ability of production logistics, and strike a perfect balance between production scheduling and production logistics. This paper probes deep into the production logistics management (PLM) of industrial enterprises, and proposes a PLM model for such enterprises based on wavelet neural network (WNN). Firstly, the PLM system architecture of industrial enterprises was established, and the scheduling and task allocation principles were proposed for the collaboration of various subjects in the system. Based on curved time window, a multi-objective path planning and optimization model was established, under influencing factors like the dynamics of station demand and the maximum driving range of handling equipment. Simulation results show that the proposed model is effective in optimizing the path for industrial production logistics. The research results provide theoretical supports to the real-time optimization of PLM and rationalization of production scheduling in industrial enterprises.

Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using PSO–GWO optimization algorithm with evolutionary programming

As path planning is an NP-hard problem it can be solved by multi-objective algorithms. In this article, we propose a multi-objective path planning algorithm which consists of three steps: (1) the first step consists of optimizing a path by the hybridization of the Grey Wolf optimizer-particle swarm optimization algorithm, it minimizes the path distance and smooths the path. (2) the second step, all optimal and feasible points generated by PSO–GWO algorithm are integrated with Local Search technique to convert any infeasible point into feasible point solution, the last step (3) depends on collision avoidance and detection algorithm, where mobile robot detects the presence of an obstacle in its sensing circle and then avoid them using collision avoidance algorithm. The proposed method is further improved by adding the mutation operators by evolutionary, it further solves path safety, length, and smooths it further for a mobile robot. Different simulations have been performed under numerous environments to test the feasibility of the proposed algorithm and it is shown the algorithm produces a more feasible path with a short distance and thus proves that it overcomes the shortcomings of other conventional techniques.

Global path planning and multi-objective path control for unmanned surface vehicle based on modified particle swarm optimization (PSO) algorithm

This paper investigates the novel path navigation method for an unmanned surface vehicle (USV), which is divided into two-stage: global path planning and path control. In the first stage, combined with the travelling salesman problem (TSP), a global path is obtained by maximizing the profit per unit time in multiple task locations. In the second stage, a nonlinear multi-objective optimization model is formulated for the path control between two task locations. In addition, fixed and time-varying currents are also considered for USV motion, which aims to avoid collision and take full advantage of the direction of currents. To solve the problem quickly and accurately, a chaotic and sharing-learning particle swarm optimization (CSPSO) algorithm is developed to solve the extended TSP and the nonlinear multi-objective model. Simulation experiments validate that the proposed hierarchical navigation method, CSPSO algorithm, and collision avoidance rules are effective and justifiable.

A survey of welding robot intelligent path optimization

Abstract Welding robots are widely used for most welding works in manufacturing industries due to their flexible, efficient and accurate operation. At the same time, it can improve enterprise competitiveness, productivity, product quality, and reduce producing cost. For industrial welding robot task planning, manual planning is still widely used, and it is time-consuming when there are large-scale welding joints or welding seams. To meet this challenge, automatic and efficient path optimization technologies have been studied to realize effective robot path planning. For welding robot path optimization, path optimization problem description is presented first. Then, intelligent optimization algorithms for welding robot path optimization are introduced. Furthermore, obstacle avoidance strategies are studied. Besides, welding robot multi-objective path optimization is given based on the above optimization problem description and optimization algorithm analysis. Optimization results show the effectiveness of intelligent path optimization strategies. At last, the discussions on welding robot path optimization application and future research are analyzed briefly.

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments. The problem is solved by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The proposed path planning algorithm mimics the real world by adding the actual size of the mobile robot to that of the obstacles and formulating the problem as a moving point in the free-space. The proposed algorithm consists of three modules. The first module forms an optimized path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimizes distance and follows path smoothness criteria. The second module detects any infeasible points generated by the proposed hybrid PSO-MFB Algorithm by a novel Local Search (LS) algorithm integrated with the hybrid PSO-MFB algorithm to be converted into feasible solutions. The third module features obstacle detection and avoidance (ODA), which is triggered when the mobile robot detects obstacles within its sensing region, allowing it to avoid collision with obstacles. The simulation results indicate that this method generates an optimal feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Moreover, compared to recent path planning techniques, simulation results show that the proposed hybrid PSO-MFB algorithm is highly competitive in terms of path optimality.

Multi-Objective Path Planning for Autonomous Robots Using Reconfigurable Analog VLSI

This paper presents a Multi-Objective path planning approach using reconfigurable Analog-Very-Large-Scale-Integrated (AVLSI) circuits. It is significant because it is the first example of floating-gate based analog resistive grid circuits used for Multi-Objective path planning. The two path planning objectives are 1) minimizing path length and 2) minimizing path cost. Three hardware experimental results are presented that implement the approach using a Field Programmable Analog Array (FPAA) circuit. First, an example demonstrates a simple proof-of-concept. Second, an example shows how the FPAA solution compares to an entire solution set for a specific Start and Goal path planning problem. Third, an example shows how the FPAA solution compares to two edge-cases. The edge-cases are the two ideals: ideal lowest cost path, and ideal shortest distance path. Based on these foundational proof-of-concept hardware results, larger environment grids than are currently implementable on the FPAA hardware were simulated to predict performance if a custom FPAA application specific integrated circuit (ASIC) was built for this Multi-Objective path planning purpose. Finally, analysis is presented to address this method's computational complexity.

Study of multi-objective path planning method for vehicles.

To address the problems associated with single-objective path planning, herein, we propose a multi-objective path planning method that takes into account emissions, fuel consumption, and time as travel demands. First, a fuel consumption and emission estimation model, suitable for coupling with a dynamic transportation network, is established, and then, the multi-objective path planning model is presented. In addition, the analytic hierarchy process (AHP) and gray relational analysis are used to derive relative priorities of each sub-objective according to driver preference. The above two models are combined with an improved Dijkstra algorithm to perform multi-objective path planning based on dynamic time-dependent road conditions. Finally, the method is verified using real-world vehicle experiments on actual roads. Compared to single-objective path planning, the multi-objective planning method meets travel needs and can save time and fuel, thereby providing greater environmental protection while still considering driver preferences.

An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders.

Underwater gliders are energy-efficient vehicles that rely on changes in buoyancy in order to convert up and down movement into forward displacement. These vehicles are conceived as multi-sensor platforms, and can be used to collect ocean data for long periods in wide range areas. This endurance is achieved at the cost of low speed, which requires extensive planning to ensure vehicle safety and mission success, particularly when dealing with strong ocean currents. As gliders are often involved on missions that pursue multiple objectives (track events, reach a target point, avoid obstacles, sample specified areas, save energy), path planning requires a way to deal with several constraints at the same time; this makes glider path planning a multi-objective (MO) optimization problem. In this work, we analyse the usage of the non-dominated sorting genetic algorithm II (NSGA-II) to tackle a MO glider path planning application on a complex environment integrating 3D and time varying ocean currents. Multiple experiments using a glider kinematic simulator coupled with NSGA-II, combining different control parameters were carried out, to find the best parameter configuration that provided suitable paths for the desired mission. Ultimately, the system described in this work was able to optimize multi-objective trajectories, providing non dominated solutions. Such a planning tool could be of great interest in real mission planning, to assist glider pilots in selecting the most convenient paths for the vehicle, taking into account ocean forecasts and particular characteristics of the deployment location.

Multi-objective path optimization for arc welding robot based on discrete DN multi-objective particle swarm optimization

Rational weld seams sequence is important for improving welding productivity and quality. Hence, intelligent path optimization strategy is introduced to obtain optimized weld seams sequence in this article. The path length and total welding deformation are considered for multi-objective path planning. First, the optimization problem description is presented. At the same time, the path length and the total welding deformation of some sequences are calculated. Then, improved multi-objective particle swarm optimization algorithm is studied. In addition, the agent model is obtained based on the sample data and experiment design. At last, the proposed algorithm is applied to optimize the welding path length and total welding deformation. The simulation results show the effectiveness of the optimization strategy.

An improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments

One of the important issues in ubiquitous geographic information science (GIS) is designing user-centric path finding algorithms to meet user needs. Mostly, in a route planning problem, the user’s purpose is optimization of two or more objective functions simultaneously. Thus, the problem is a multi-objective problem. In the present study, having considered multi-objective optimization methods in path finding, we developed an algorithm for solving this problem using an improved multi-objective ant colony optimization (ACO) algorithm. Modifications are introduced for various components of the ant colony metaheuristics; specifically, for those associated with the ‘ant decision rule’. The proposed algorithm was tested on the studied network. The results demonstrate that the proposed approach has acceptable settings, repeatability and run time. In addition, one of the important research outputs is a pareto-front which allows the user to select the final path according to the desired priorities.

Multi-objective path planner for an agricultural mobile robot in a virtual greenhouse environment

Robotics in agriculture has experienced enormous development in the past decade. In order to accomplish numerous agricultural tasks, the order in which the crop is covered is particularly important in order to minimise travelling cost and to preserve soil conditions. However, it is difficult to determine an optimised sequential path for the robot which minimizes navigation costs, while ensuring a fully completed agricultural operation. This paper presents the implementation of a multi-objective algorithm to solve the path planning problem for pesticide spraying operation inside a greenhouse. Pesticide spraying problems found in greenhouses have been adapted from the vehicle routing problem found in operations research, where the infected plants represent the customers and the mobile robot represents the vehicles. Routes between the plants are generated using a Probabilistic Roadmap path planner based on the designed virtual environment. The virtual environment is designed based on the real greenhouse environment to visualise the agricultural operation inside the greenhouse. To determine the best routes for the mobile robot, the Non-dominated Sorting Genetic Algorithm using Reference Point Based (NSGA-III) is tested and applied to the system. The solution quality has been compared with the Non-dominated Sorting Genetic Algorithm (NSGA-II) using the C-metric indicator. Comparisons with NSGA-II using the C-metric indicator verify that NSGA-III offers a superior performance with a good quality result.

Finding equitable risk routes for hazmat shipments

This paper develops a model to analyse hazmat shipments routing in the context of hazmat transportation between the specified origin-destination (OD) pair. A novel aspect of this model is the consideration of risk equity using the standard deviation, an established computation to assess equity. To solve the model, a two-phase method is developed, in which the multi-objective shortest path algorithm is used to obtain the Pareto-optimal paths set, and get the routes using estimation of distribution algorithm after paths choice. We then present a test problem of hazmat shipment with consideration of risk equity and discuss computational results.

A benchmark test problem toolkit for multi-objective path optimization

Abstract Due to the complexity of multi-objective optimization problems (MOOPs) in general, it is crucial to test MOOP methods on some benchmark test problems. Many benchmark test problem toolkits have been developed for continuous parameter/numerical optimization, but fewer toolkits reported for discrete combinational optimization. This paper reports a benchmark test problem toolkit especially for multi-objective path optimization problem (MOPOP), which is a typical category of discrete combinational optimization. With the reported toolkit, the complete Pareto front of a generated test problem of MOPOP can be deduced and found out manually, and the problem scale and complexity are controllable and adjustable. Many methods for discrete combinational MOOPs often only output a partial or approximated Pareto front. With the reported benchmark test problem toolkit for MOPOP, we can now precisely tell how many true Pareto points are missed by a partial Pareto front, or how large the gap is between an approximated Pareto front and the complete one.