Multi-objective Ant Colony Algorithm Research Articles

Multi-objective optimization for energy-efficient flow shop scheduling problem with blocking and collision-free transportation constraints

In this paper, the energy-efficient flow shop scheduling problem with blocking and collision-free transportation constraints (EFSP-BCFT) with sequence dependent setup times (SDST), scheduling of automated guided vehicles (AGV), transportation speed control and battery management is investigated. An enhanced multi-objective ant colony algorithm (EMOACA) is developed to minimize makespan and total energy consumption simultaneously. Several enhancement techniques including a novel low-high resolution search strategy, an effective AGV dispatching heuristic information and critical-path-based energy reduction improvement procedure are proposed. Extensive experiments spanning various scenarios of the problem are conducted and computational results demonstrate the effectiveness of the proposed method and enhancements to obtain high quality solutions compared to standard and state- of-the-art metaheuristics. The results also showcase the significance of considering transportation speed control, battery management and AGV idle power consumption in the modelling of the problem where better system performance can be achieved both in terms of shorter schedules and smoother transportation flow.

Multi-Objective Optimal Design of μ–Controller for Active Magnetic Bearing in High-Speed Motor

In this paper, a control strategy based on the inverse system decoupling method and μ-synthesis is proposed to control vibration in a rigid rotor system with active magnetic bearings that are built into high-speed motors. First, the decoupling method is used to decouple the four-degrees-of-freedom state equation of the electromagnetic bearing rigid rotor system; the strongly coupled and nonlinear rotor system is thus decoupled into four independent subsystems, and the eigenvalues of the subsystems are then configured. The uncertain parametric perturbation method is used to model the subsystem, and the multi-objective ant colony algorithm is then used to optimize the sensitivity function and the pole positions to obtain the optimal μ-controller. The closed-loop system thus has the fastest possible response, the strongest internal stability, and the best disturbance rejection capability. Then, the unbalanced force compensation algorithm is used to compensate for the high-frequency eccentric vibration; this algorithm can attenuate the unbalanced eccentric vibration of the rotor to the greatest extent and improve the robust stability of the rotor system. Finally, simulations and experiments show that the proposed control strategy can allow the rotor to be suspended stably and suppress its low-frequency and high-frequency vibrations effectively, providing excellent internal and external stability.

Emergency logistics network optimization with time window assignment

During natural disasters or accidents, an emergency logistics network aims to ensure the distribution of relief supplies to victims in time and efficiently. When the coronavirus disease 2019 (COVID-19) emerged, the government closed the outbreak areas to control the risk of transmission. The closed areas were divided into high-risk and middle-/low-risk areas, and travel restrictions were enforced in the different risk areas. The distribution of daily essential supplies to residents in the closed areas became a major challenge for the government. This study introduces a new variant of the vehicle routing problem with travel restrictions in closed areas called the two-echelon emergency vehicle routing problem with time window assignment (2E-EVRPTWA). 2E-EVRPTWA involves transporting goods from distribution centers (DCs) to satellites in high-risk areas in the first echelon and delivering goods from DCs or satellites to customers in the second echelon. Vehicle sharing and time window assignment (TWA) strategies are applied to optimize the transportation resource configuration and improve the operational efficiency of the emergency logistics network. A tri-objective mathematical model for 2E-EVRPTWA is also constructed to minimize the total operating cost, total delivery time, and number of vehicles. A multi-objective adaptive large neighborhood search with split algorithm (MOALNS-SA) is proposed to obtain the Pareto optimal solution for 2E-EVRPTWA. The split algorithm (SA) calculates the objective values associated with each solution and assigns multiple trips to shared vehicles. A non-dominated sorting strategy is used to retain the optimal labels obtained with the SA algorithm and evaluate the quality of the multi-objective solution. The TWA strategy embedded in MOALNS-SA assigns appropriate candidate time windows to customers. The proposed MOALNS-SA produces results that are comparable with the CPLEX solver and those of the self-learning non-dominated sorting genetic algorithm-II, multi-objective ant colony algorithm, and multi-objective particle swarm optimization algorithm for 2E-EVRPTWA. A real-world COVID-19 case study from Chongqing City, China, is performed to test the performance of the proposed model and algorithm. This study helps the government and logistics enterprises design an efficient, collaborative, emergency logistics network, and promote the healthy and sustainable development of cities.

Optimization of minimum crossing distance of transmission line based on multi objective ant colony algorithm

In the test of cross span distance of transmission line, the estimation of cross span distance is not accurate, which leads to the increase of subsequent test error. Therefore, a test method of transmission line cross span distance based on a multi-objective ant colony algorithm is proposed. Statistics of the actual information of different crossovers including trees and forest areas. Considering the transmission line as a flexible cable, the objective function for estimating the cross span distance of the transmission line is constructed according to the catenary model of the flexible cable model. On this basis, the multi-objective ant colony algorithm is used to solve the function, and the optimization results of transmission line crossing distance are output. The experimental results show that the method has high accuracy, small error, short time and reliability.

A scheduling and planning method for geological disasters

Upon the occurrence of a disaster, emergency supplies should arrive at disaster areas with different severities in the shortest time. Therefore, it is of pivotal importance to plan and schedule appropriate processes. This paper first elaborates on the principle of ordered arrival of emergency supplies. Then, it establishes a multi-objective multi-constraint emergency material scheduling (EMS) model based on the principle of ordered arrival. Subsequently, a hybrid ant colony optimization (HACO) is proposed to solve EMS. The computational results for 15 multi-depot benchmark instances are reported and the average error between HACO and the best known solution (BKS) is only 0.16% Then, real geological disasters from Hubei province in China are examined as a case study. Experimental results show that HACO performs better than non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective ant colony algorithm (MOACO) in the case study and benchmark instances.

Low‐Energy Secure Routing Protocol for WSNs Based on Multiobjective Ant Colony Optimization Algorithm

As one of the three pillars of information technology, wireless sensor networks (WSNs) have been widely used in environmental detection, healthcare, military surveillance, industrial data sampling, and many other fields due to their unparalleled advantages in deployment cost, network power consumption, and versatility. The advent of the 5G standard and the era of Industry 4.0 have brought new opportunities for the development of wireless sensor networks. However, due to the limited power capacity of the sensor nodes themselves, the harsh deployment environment will bring a great difficulty to the energy replenishment of the sensor nodes, so the energy limitation problem has become a major factor limiting its further development; how to improve the energy utilization efficiency of WSNs has become an urgent problem in the scientific and industrial communities. Based on this, this paper researches the routing technology of wireless sensor networks, from the perspective of improving network security, and reducing network energy consumption, based on the study of ant colony optimization algorithm, further studies the node trust evaluation mechanism, and carries out the following research work: (1) study the energy consumption model of wireless sensor networks; (2) basic ant colony algorithm improvement; (3) multiobjective ant colony algorithm based on wireless sensor routing algorithm optimization. In this study, the NS2 network simulator is used as a simulation tool to verify the performance of the research algorithm. Compared with existing routing algorithms, the simulation results show that the multiobjective ant colony optimization algorithm has better performance in evaluation indexes such as life cycle, node energy consumption, node survival time, and stability compared with the traditional algorithm and the dual cluster head ant colony optimization algorithm.

Multi objective ant colony algorithm for electrical wire routing

Multi objective ant colony algorithm for electrical wire routing

Multi objective ant colony algorithm for electrical wire routing

Ant colony optimisation algorithms have been applied to solve wide range of difficult combinatorial optimisation problems like routing problems, assigning problems, scheduling problems and revealed remarkable solutions. In this paper we present a novel approach of ant colony optimisation algorithm to solve the electrical cable routing problem. The study focuses on optimising wire lengths, number of bends and angles of bends. We have studied these objectives in cable routing and modified the ant colony system algorithm to get better solutions. Ants are directed to search for the optimal path between the starting and the ending points by avoiding the obstacles. While ants are navigating, they travel the paths with less number of bends and consider angles of the bends towards 90, 180, and 270 degrees. Normal walls are presented as a grid and doors, windows and other obstacles are represented as rectangles. The possible points to follow by ants are designed according to the BS 7671 (IET Wiring Regulations) standards. The results of the simulation prove with comparisons that this method is feasible and effective for optimising the electrical wire routing.

Cold Chain Logistics Path Optimization via Improved Multi-Objective Ant Colony Algorithm

In order to improve the performance and change the current situation of the cost minimization model widely used in the cold chain logistics distribution process, a multi-objective optimization model based on cost, carbon emissions and customer satisfaction is proposed. Considering the characteristic of this proposed optimization model, we design an improved ant colony algorithm with a multi-objective heuristic function to solve it, termed as ACOMO. Experimental results show that the proposed ACOMO can effectively solve the vehicle routing problem of the multi-objective optimization model, and outperforms the classic ant colony algorithms, resulting in more Pareto optimal solutions. It offers an environmentally friendly distribution solution for the problem. Specifically, the distribution path obtained by the improved ant colony algorithm manages to achieve the above multiple goals, including reduction of distribution costs and carbon emissions, and improvement of customer satisfaction. In addition, compared with a single-target model that only provides one single distribution route to cost minimization, multi-objective optimization can provide a variety of distribution route options for logistics companies in practice. Finally, through the sensitivity analysis of temperature changes and cargo damage coefficients, the proposed system successfully provides reference for the optimization of the path of cold chain logistics enterprises, and promotes logistics enterprises to effectively arrange their work and to be more socially responsible.

Multi-objective optimization of auto-body fixture layout based on an ant colony algorithm

Fixtures are extensively used in many industries such as the car industry, to locate and constrain the sheet part during the assembly stage. Fixture layout affects on deformation of sheet parts. Therefore, fixture layout optimization is crucial to the accuracy and quality of products. In addition, the number of clamps that uses in the fixture is another important factor that must be considered in fixture design. This article presents a novel fixture layout optimization method by combining multi-objective ant colony algorithm (M-ACO) and the finite element method. The proposed method optimizes the fixture layout and the number of clamps simultaneously as a multi-objective problem. An approximation of Pareto frontier is acquired by the proposed method. The fixture layout for the side reinforcement of a car is optimized using the proposed method. The results show that the proposed approach performs effectively to optimize the auto-body fixture layout.

Target Disassembly Sequencing and Scheme Evaluation for CNC Machine Tools Using Improved Multiobjective Ant Colony Algorithm and Fuzzy Integral

Disassembly planning aims to perform the optimal disassembly sequence given a used or obsolete product in terms of cost and environmental impact. This paper presents a new multiobjective programming model for the target disassembly sequencing. It proposes an improved multiobjective ant colony algorithm to derive optimal target disassembly sequences. This work also establishes some indices on disassembly scheme evaluation and a fuzzy integral method to evaluate the obtained disassembly scheme. A CNC machine tool example is given to illustrate the proposed models and the effectiveness of the proposed algorithm. Both theoretical and simulation results demonstrate that the proposed approach can perform the quantitative analysis of a disassembly process effectively. Such results can help decision makers select the best plans and sequences when executing a disassembly process of a product.

A Multi-Objective Optimization Algorithm for Truss Structures

For the optimization of multi-objective discrete variable structure optimization in civil engineering, a new improved multi-objective ant colony optimization algorithm for truss structures has been proposed in this paper. The new improved algorithm established two sets, including sets of feasible solutions and non-feasible solutions, and “the repeated solutions” was replaced with “the special solutions” to acquire the Pareto optimal front-end of the multi-objective problems. This algorithm can not only make the multi-objective ant colony algorithm better solve multi-objective problems under discrete variables, but also realize the successful application of multi-objective ant colony algorithm in truss structure. The present paper proposes a new improved multi-objective ant colony optimization algorithm for truss structures, which can provide a good performance in program design, arithmetic speed and generality of the proposed method. It is also a simple and practical, and suitable for projects in the future.

A multi-objective ant colony optimization with decomposition for community detection in complex networks

Community detection in complex networks plays an important role in mining and analyzing the structure and function of networks. However, traditional algorithms for community detection-based graph partition and hierarchical clustering usually have to face expensive computational costs or require some specific conditions when dealing with complex networks. Recently, community detection based on intelligent optimization attracts more and more attention because of its good effectiveness. In this paper, a new multi-objective ant colony optimization with decomposition (MACOD) for community detection in complex networks is proposed. Firstly, a new framework of multi-objective ant colony algorithm specialized initially for the complex network clustering is developed, in which two-objective optimization problem can be decomposed into a series of subproblems and each ant is responsible for one single objective subproblem and it targets a particular point in the Pareto front. Secondly, a problem-specific individual encoding strategy based on graph is proposed. Moreover, a new efficient local search mechanism is designed in order to improve the stability of the algorithm. The proposed MACOD has been compared with four other state of the art algorithms on two benchmark networks and seven real-world networks including three large-scale networks. Experimental results show that MACOD performs competitively for the community detection problems.

An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling

Energy-aware scheduling in manufacturing operations with time-of-use electricity tariffs is a challenging problem. Sustainable manufacturing is gaining significant momentum: companies are not only improving their product quality, but also optimizing the production processes to improve energy consumption (i.e., minimizing energy cost) in order to manage environmental challenges which contribute to global climate change. The purpose of this paper is to study a preemptive scheduling problem on a single-machine to minimize the total completion time and total energy cost under time-of-use electricity tariffs, which is a mixed-integer multiobjective mathematical programming model. To solve these objectives, we develop several new holistic ant colony optimization algorithms. The proposed model is solved via several methods including weighted sum method (WSM) using CPLEX, and multiobjective ant colony optimization based on a dominance ranking (ACO-DR) or based on a dominance ranking procedure and crowding distance comparison (ACO-DRC) or based on a heuristic approach to obtain an approximate Pareto-front and also provide information on when to start and resume each job for any solution on the Pareto-front. We provide detailed experimental results evaluating the performance of the proposed algorithms. In a case study, we demonstrate how the results of the multiobjective model could be utilized in decision making using the multiobjective optimization on the basis of ratio analysis (MOORA) method. This proposed model and heuristics allows decision makers to operate in challenging-data enabled environments in industrial internet of things ecosystem, and assists in optimizing production planning to improve energy cost.

Multi-Objective Ant Colony Optimization Algorithm Based on Discrete Variables

Based on multi-objective optimization of discrete variable, a new multi-objective ant colony optimization algorithm based on discrete variable has been proposed in this paper. The improved multi-objective ant colony algorithm established two databases on the basis of P-ACO algorithm, including sets of feasible solutions and non-feasible solutions, and “the repeated solutions” was replaced with “the special solutions” to acquire the Pareto optimal front-end of the multi-objective problems. This algorithm is not only a better application in solving multi-objective problem in the discrete variables system, but also can be applied to the truss structure optimization successfully. The present paper proposes a multi-objective ant colony optimization algorithm based on discrete variable, which can provide a good performance in program design, arithmetic speed and generality of the proposed method. It is also simple and practical, and suitable for projects in the future.

A multi-objective krill herd algorithm for virtual machine placement in cloud computing

In cloud computing, the major task is to make efficient use of resources. The growth in cloud environment opens door for vast research. Placement of virtual machine is a main problem in cloud environment. The ideal placement of virtual machine leads to power effectiveness and asset usage in distributed cloud computing. A multi-objective krill herd technique is proposed in this paper for placement of virtual machine. The objective is to effectively acquire an arrangement of non-dominated solutions that the entire limit add up to asset wastage and power utilization. The work is analysed with the examples from the literature. The experimental outcomes are compared with the existing multi-objective genetic procedure and multi-objective ant colony algorithm. Also, the results are compared with the 2 algorithms, First Fit Decreasing algorithm and Simplified Ant Colony algorithm. The experimental results shows that the proposed method is more efficient when compared to other existing methods.

Optimal design of honeycomb sandwich structure cover with multi-objective ant colony algorithm

In order to improve the strength of the composite folding inflatable flexible wing cover of unmanned plane, the lay-up of the cover of honeycomb sandwich structure is optimized and designed by adopting multi-objective wolf colony algorithm. Aimed at the characteristic of discretization of the optimal variable of composite structure, multi-objective wolf colony algorithm with integer coding method was adopted and fitness function is proposed. Constraint condition was given by referring to the lay-up principle of composite. Multi-objective wolf colony algorithm based on population density was adopted for the optimization of algorithm module. Then the strategy of using the evaluation of estimate of the above simulation module to optimize the surface structure of the folding inflatable flexible wind was adopted. Non-dominated ranking and diversity of population density was introduced in algorithm MOWCA to maintain the operation, which effectively improved the front distribution diversity and convergence precision. The experimental result verifies the effectiveness of scheduling algorithm in multi-objective optimization.

Multi-objective process route optimization considering carbon emissions

Process route planning bridges the gap between design stage and manufacturing stage, which transforms workblanks into parts or products. The decisions of processing methods, machines, cutting tools, and sequence of process stages during process route planning have a significant impact on carbon emissions in manufacturing process. To reduce carbon emissions of process routes of parts and simultaneously consider economic and high-efficiency factors, a low-carbon multi-objective process route optimization method is proposed. Firstly, a constructed PBOM (process bill of material) based on machining features of parts is used to quantify carbon emissions of every processing step. Secondly, a multi-objective optimization model of process routes with the objectives of minimum carbon emissions, minimum processing cost, and minimum processing time is built based on the PBOM. Thirdly, a multi-objective ant colony algorithm is designed to solve the proposed model. Finally, a practical applicable bearing seat is taken as a case study to verify the rationality of the proposed method. Comparison results show that the proposed method can obtain the low-carbon, economic, and high-efficiency process routes for parts.

Application of Ant Colony Algorithm in Multi-objective Optimization Problems

In actual application and scientific research, multi-objective optimization is an extremely important research subject. In reality, many issues are related to the simultaneous optimization under multi-objective conditions. The research subject of multi-objective optimization is getting increasing attention. In order to better solve some nonlinear, restricted complex multi-objective optimization problems, based on the current studies of multi-objective optimization and evolutionary algorithm, this paper applies the ant colony algorithm to multi-objective optimization, and proves through experiments that multi-objective ant colony algorithm can converge the real Pareto front of the standard test function more quickly and accurately, and can also maintain the distributivity of the better solution.

Optimal design of linear sensor networks for process plants: A multi-objective ant colony optimization approach

This investigation is performed to study nonredundant linear sensor network design problems that simultaneously optimize three objectives, namely cost, precision and reliability. In this article, a novel ant colony algorithm is proposed to efficiently tackle this multi-objective combinatorial problem. Both heuristic information and phenomenon are described as multiple matrices and a dynamic random weighted strategy is introduced to compute selection probability. A linear independent relationship is presented to guide a feasible solution construction process. An external archive is used to improve the convergence speed. Moreover, the TOPSIS method is adopted to aid in multi-criteria decision making with respect to Pareto-optimal solutions. The proposed method is successfully applied to a multi-objective design problem of a steam metering network of a methanol production plant. The results illustrate that the proposed method could find a set of global Pareto-optimal solutions, which is helpful for understanding the relationship among cost, precision and reliability, and consequently is helpful for final decision support. Therefore, the integrated methodology of a multi-objective ant colony algorithm and a multi-criteria decision making technique provides a promising tool for the optimal design of sensor networks by simultaneously considering multiple conflicting objectives.