Multi-objective Ant Colony Optimization Algorithm Research Articles

Optimization of Fresh Food Logistics Routes for Heterogeneous Fleets in Segmented Transshipment Mode

To address the challenges of environmental impact and distribution efficiency in fresh food logistics, a segmented transshipment model involving the coordinated operation of gasoline and electric vehicles is proposed. The model minimizes total distribution costs by considering transportation, refrigeration, product damage, carbon emissions, and penalties for time window violations. The k-means++ clustering algorithm is used to determine transshipment points, while an improved adaptive multi-objective ant colony optimization algorithm (IAMACO) is employed to optimize the delivery routes for the heterogeneous fleet. The case study results show that compared to the traditional model, the segmented transshipment mode reduces the total distribution costs, carbon emissions, and time window penalty costs by 22.13%, 28.32%, and 41.08%, respectively, providing a viable solution for fresh food logistics companies to achieve sustainable and efficient growth.

Multirobot Assembly Sequence Planning of a Large Space Telescope

As space structures trend toward larger scales and modularization, their on-orbit assembly presents a significant challenge. Assembly sequence planning is crucial in minimizing vibration and enhancing the efficiency of on-orbit construction. This paper explores the multirobot assembly sequence planning algorithm for a large space telescope. A dynamic model is firstly developed to analyze the vibration of segmented mirror pieces and the main satellite during assembly, taking into account the flexibility of connections at interfaces. The dynamic model is able to provide criteria for time efficiency and satellite attitude disturbances. Considering constraints during the assembly process, a modified multiobjective ant colony optimization algorithm capable of planning multirobot assembly sequences is implemented. The simulation results demonstrate that the proposed method not only facilitates parallel planning of multirobot assembly sequences but also leads to a reduction in assembly time and attitude disturbances of the main satellite. The results of assembly sequence planning provide dependable guidance for the subsequent planning levels, such as task planning and trajectory planning, of the on-orbit assembly task for large space telescopes.

An Artificial-Intelligence-Based Renewable Energy Prediction Program for Demand-Side Management in Smart Grids

Technology advancements have enabled the capture of Renewable Energy Sources (RES) on a massive scale. Smart Grids (SGs) that combine conventional and RES are predicted as a sustainable method of power generation. Moreover, environmental conditions impact all RES, causing changes in the amount of electricity produced by these sources. Furthermore, availability is dependent on daily or annual cycles. Although smart meters allow real-time demand prediction, precise models that predict the electricity produced by RES are also required. Prediction Models (PMs) accurately guarantee grid stability, efficient scheduling, and energy management. For example, the SG must be smoothly transformed into the conventional energy source for that time and guarantee that the electricity generated meets the predicted demand if the model predicts a period of Renewable Energy (RE) loss. The literature also suggests scheduling methods for demand-supply matching and different learning-based PMs for sources of RE using open data sources. This paper developed a model that accurately replicates a microgrid, predicts demand and supply, seamlessly schedules power delivery to meet demand, and gives actionable insights into the SG system’s operation. Furthermore, this work develops the Demand Response Program (DRP) using improved incentive-based payment as cost suggestion packages. The test results are valued in different cases for optimizing operating costs through the multi-objective ant colony optimization algorithm (MOACO) with and without the input of the DRP.

Color Formulation of Cotton Fabrics Using Multi-Objective Ant Colony Optimization

ABSTRACT The formulation of colors is one of the most important aspects of colorimetry. For dyeing and finishing industries, mastering this step is a major problem to be solved. In this paper, a multi-objective ant colony optimization algorithm is developed to optimize the reproduction of the desired shades. The multi-objective function of the proposed algorithm minimizes simultaneously the CMC color differences and the metamerism index between the standard and the reproduced colors. Two ranges of dyes were used to dye cotton fabrics. The elaborated multi-objective algorithm showed good results with small values of color differences and metamerism index. All CMC color differences values were less or at the limit of the acceptability threshold of 1, and about 75% of samples exhibited minimum metamerism and are considered as a good match.

Multi-Objective Weather Routing Algorithm for Ships: The Perspective of Shipping Company’s Navigation Strategy

Ship weather routing has always been an important issue in the research field of navigation, and many scholars have been devoted to this research for a long time. To study the route strategies of different shipping companies, this paper proposes an improved multi-objective ant colony optimization (IMACO) algorithm based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It can comprehensively consider ship navigation risk and fuel consumption cost under complex sea conditions. First, the grid method is used to model the marine environment. Then, we calculate the fuel consumption and the ship navigation risk of each grid and use the TOPSIS method to evaluate these two indicators for each grid. The results show that due to the different strictness of navigation management requirements of different companies, different routes are selected in the same sea area at the same time. Compared with the single-objective ant colony optimization (SACO) algorithm, the algorithm proposed in this paper can more comprehensively and effectively solve the problem of route strategy selection of shipping companies, which has great practical significance for ship operations management.

Multiobjective Evolutionary Interpretable Type-2 Fuzzy Systems With Structure and Parameter Learning for Hexapod Robot Control

This article proposes a data-driven multiobjective evolutionary interval type-2 fuzzy-logic system (IT2FLS) learning approach that considers both system performance and rule interpretability. One characteristic of the evolutionary learning framework is that the IT2FLS structure is incrementally generated instead of searching from an initially huge grid-type rule base, which significantly reduces the parameter search space. Another one is that a new constrained objective function is proposed to improve the distinguishability of the generated interval type-2 fuzzy sets (FSs) without restricting them to be evenly distributed. Because of the tradeoff between system performance and interpretability, a new multiobjective ant colony optimization (ACO) algorithm is proposed to optimize the IT2FLS parameters and improve optimization performance. The evolutionary IT2FLS learning approach is applied to control a real wall-following hexapod robot. The approach shows the advantages of model-free design and interpretability and robustness to noise in the evolved type-2 fuzzy rules. Simulations with performance comparisons and experiments in controlling a real robot show the advantages of the learning approach.

An ant colony optimization algorithm with evolutionary experience-guided pheromone updating strategies for multi-objective optimization

Since the multi-objective ant colony optimization algorithm consumes a massive cost of time and computation resources, improving its convergence performance is essential. This paper proposes a historical experience-guided pheromone updating approach to improve the efficiency and enhance the optimization quality, which uses a learning automata to choose suitable pheromone updating methods adaptively according to the searching history. The learning automata performs different kinds of pheromone updating schemes, including a novel intragroup evolutionary information-guided strategy and two widely accepted strategies. A comparative experiment tests the proposed algorithm on multi-objective benchmark traveling salesman problems with a high-dimensional search space, as well as the primer design problem, which is practical in the biology area. The experimental results indicate that the proposed algorithm has a competitive performance on both the benchmark problems and the practical problem.

A Novel Self-Adaptive Mixed-Variable Multiobjective Ant Colony Optimization Algorithm in Mobile Edge Computing

Mobile edge computing (MEC) provides physical resources closer to end users, becoming a good complement to cloud computing. The booming MEC brings many multiobjective optimization problems. The paper proposes a multiobjective optimization (MOO) algorithm called SAMOACOMV, which provides a new choice for solving MOO problems of MEC. We improve the ACOMV algorithm that is only suitable for solving mixed-variable single-objective optimization (SOO) problems and propose a MOACOMV algorithm suitable for solving mixed-variable MOO problems. And aiming at the dependence of MOACOMV algorithm performance on parameter setting, we proposed the SAMOACOMV algorithm using a self-adaptive parameter setting scheme. Furthermore, the paper also designs some mixed-variable MOO benchmark problems for the purpose to test and compare the performance of the SAMOACOMV algorithm. The experiments indicate that the SAMOACOMV algorithm has excellent comprehensive performance and is an ideal choice for solving mixed-variable MOO problems.

Applying ontology learning and multi-objective ant colony optimization method for focused crawling to meteorological disasters domain knowledge

The focused crawler based on semantic analysis is a research hotspot in the field of information retrieval. The domain ontology is generally applied to construct the topic model of the focused crawler. In order to overcome the limitations of builders' knowledge reserve and subjective consciousness in the process of constructing artificially ontology, a semi-automatic construction method of domain ontology based on ontology learning technology combining the latent Dirichlet allocation and the Apriori algorithm is proposed in this article. When evaluating the relevance between a hyperlink and a specific topic, the joint evaluation method considering both the web text and the link structure is usually used. However, the traditional weighted sum method is difficult to reasonably determine the optimal weights of these evaluating indicators. To solve this problem, a multi-objective optimization model for link evaluation and a subsequent multi-objective ant colony optimization algorithm (MOACO) are proposed. In the MOACO, a method of the nearest farthest candidate solution (NFCS) is combined with the fast non-dominated sorting to select a set of Pareto-optimal hyperlinks and guide the crawlers’ search directions. The experimental results of the focused crawling on the domain knowledge of typhoon disasters and rainstorm disasters prove that the ability of the proposed focused crawlers to retrieve topic-relevant webpages.

An efficient multi-objective ant colony optimization for task allocation of heterogeneous unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have become powerful tools in modern military combat. How to properly allocate the tasks of heterogeneous UAVs in a combat is a fundamental and challenging problem. In this paper, we formulate the cooperative task allocation of heterogeneous UAVs as a constrained multi-objective optimization problem. To efficiently resolve the formulated problem, we further propose a multi-objective ant colony optimization (MOACO) algorithm with a new pheromone updating mechanism and four newly defined heuristic information. Simulation results on test cases with different scales and characteristics have shown that the proposed methods can perform better than several recently published algorithms, in terms of convergence speed, solution quality and solution diversity.

A multi-tiered vehicle routing problem with global cross-docking

A new “rich” variation on the multi-objective vehicle routing problem (VRP), called the multi-tiered vehicle routing problem with global cross-docking (MTVRPGC), is introduced in this paper. With respect to previously studied VRPs, the MVRPTGC includes the following novel features: (i) segregation of facilities into different tiers that distinguish them in terms of different processing and storage capabilities, (ii) cross-docking at a pre-specified subset of facilities in the network (a feature referred to as global cross-docking), and (iii) the possibility of spill-over into subsequent planning periods of demand for facility visitation. The problem originated from a real-life application concerning the collection and delivery of pathology specimens in the transportation network of a pathology health-care service provider. Other industrial applications may, however, benefit from this type of VRP, such as mail sorting. A mixed integer linear programming (MILP) model for this VRP is proposed, and tested computationally in respect of seventeen small hypothetical test instances. A multi-objective ant colony optimisation (MACO) algorithm for solving larger real-world instances of the MTVRPGC is also proposed. The solutions returned by the MACO algorithm are compared with those achieved by the MILP in respect to sixteen instances and also compared to actual collection and delivery routes of a real pathology healthcare service provider operating in South Africa and it is found that adopting the routes suggested by the algorithm results in substantial improvements of all the objectives pursued relative to the status quo.

A Hybrid Multi-Objective Bat Algorithm for Solving Cloud Computing Resource Scheduling Problems

To improve the service quality of cloud computing, and aiming at the characteristics of resource scheduling optimization problems, this paper proposes a hybrid multi-objective bat algorithm. To prevent the algorithm from falling into a local minimum, the bat population is classified. The back-propagation algorithm based on the mean square error and the conjugate gradient method is used to increase the loudness in the search direction and the pulse emission rate. In addition, the random walk based on lévy flight is also used to improve the optimal solution, thereby improving the algorithm’s global search capability. The simulation results prove that the multi-objective bat algorithm proposed in this paper is superior to the multi-objective ant colony optimization algorithm, genetic algorithm, particle swarm algorithm, and cuckoo search algorithm in terms of makespan, degree of imbalance, and throughput. The cost is also slightly better than the multi-objective ant colony optimization algorithm and the multi-objective genetic algorithm.

A new multi-objective mathematical model for a Citrus supply chain network design: Metaheuristic algorithms

Nowadays, the citrus supply chain has been motivated by both industrial practitioners and researchers due to several real-world applications. This study considers a four-echelon citrus supply chain, consisting of gardeners, distribution centers, citrus storage, and fruit market. A Mixed Integer Non-Linear Programming (MINLP) model is formulated, which seeks to minimize the total cost and maximize the profit of the Citrus supply chain network. Due to the complexity of the model when considering large-scale samples, two well-known meta-heuristic algorithms such as Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithms have been utilized. Additionally, a new multi-objective ACO algorithm based on a set of non-dominated solutions form the Pareto frontier developed to solve the mathematical model. An extensive comparison based on different measurements analyzed to find a performance solution for the developed problem in the three sizes (small, medium, and large-scale). Finally, the various outcomes of numerical experiments indicate that the MOACO algorithm is more reliable than other algorithms.

Application of Improved Multi-Objective Ant Colony Optimization Algorithm in Ship Weather Routing

This paper presents a novel intelligent and effective method based on an improved ant colony optimization (ACO) algorithm to solve the multi-objective ship weather routing optimization problem, considering the navigation safety, fuel consumption, and sailing time. Here the improvement of the ACO algorithm is mainly reflected in two aspects. First, to make the classical ACO algorithm more suitable for long-distance ship weather routing and plan a smoother route, the basic parameters of the algorithm are improved, and new control factors are introduced. Second, to improve the situation of too few Pareto non-dominated solutions generated by the algorithm for solving multi-objective problems, the related operations of crossover, recombination, and mutation in the genetic algorithm are introduced in the improved ACO algorithm. The final simulation results prove the effectiveness of the improved algorithm in solving multi-objective weather routing optimization problems. In addition, the black-box model method was used to study the ship fuel consumption during a voyage; the model was constructed based on an artificial neural network. The parameters of the neural network model were refined repeatedly through the historical navigation data of the test ship, and then the trained black-box model was used to predict the future fuel consumption of the test ship. Compared with other fuel consumption calculation methods, the black-box model method showed higher accuracy and applicability.

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.

Efficient Task Scheduling in Cloud Computing using Multi-objective Hybrid Ant Colony Optimization Algorithm for Energy Efficiency

The efficiency of Internet services is determined by the Cloud computing process. Various challenges in computing are being faced, such as security, the efficient allocation of resources, which in turn results in the waste of resources. Researchers have explored a number of approaches over the past decade to overcome these challenges. The main objective of this research is to explore the task scheduling of cloud computing using multi-objective hybrid Ant Colony Optimization (ACO) with Bacterial Foraging (ACOBF) behavior. ACOBF technique maximized resource utilization (Service Provider Profit) and also reduced Makespan and user wait times Job request. ACOBF classifies the user job request in three classes based on the sensitivity of the protocol associated with each request, Schedule Job request in each class based on job request deadline and create a Virtual Machine (VM) cluster to minimize energy consumption. Based on comprehensive experimentation, the simulated results show that the performance of ACOBF outperforms the benchmarked techniques in terms of convergence, diversity of solutions and stability.

A holonic intelligent decision support system for urban project planning by ant colony optimization algorithm

Hitherto, urbanization reached unprecedented spreading, various problems in the field increase from day to day, and makes the urban phenomena more dynamic and more complex. Therefore, it is important to call in experts and provide all stuff to establish urban projects’ plans, which often need to be achieved in a brief time. Actually, decision-makers need more and more updated plans and even sustainable solutions to convey eventual urban changes with maintaining intrinsic features of urban areas, such as coverage, inter-dependency, and coherency. Due to decision-makers yearnings and the short time allocated to planners, urban project planning remains an exhausting task; it resorts to arbitrary choices to find a good match of projects according to the intended situations. On the other hand, it should take care of the available resources like funds, land, water, energy, underground, and raw materials, which ought to be rationally exploited, and preserved for future generations. In this paper, the proposed intelligent decision support system (IDSS) aims to find out the best urban plans that fit urban projects to appropriate areas. It also employs the holonic approach to model complex and large-scale urban systems, where agents of each level apply a new multi-objective ant colony optimization algorithm called BKPACS for the urban project planning problem, which is viewed as a bounded knapsack problem (BKP). To produce global optimal urban plans, the main algorithm called H-MACO coordinates between the different levels of this holonic system. The experimental results on a set of urban projects about a province of Algeria show good quality plans produced in less time.

A multi-objective decomposition-based ant colony optimisation algorithm with negative pheromone

ABSTRACT Existing ant colony algorithms only have one kind of pheromone. They use non-dominated solutions to update it while not making use of dominated solutions, which can provide valuable information for guiding the subsequent foraging process. To make full use of dominated solutions, we create a new kind of pheromone temporarily called a negative pheromone and propose a new ant colony optimisation algorithm called NMOACO/D, which combines MOEA/D-ACO with the negative pheromone. Many experiments have been carried out in this study to compare NMOACO/D with MOEA/D-ACO and other algorithms on several bi-objective travelling salesman problems. We demonstrate that NMOACO/D outperforms the MOEA/D-ACO and six different recently proposed related algorithms on all nine test instances. We also evaluate the effect of negative pheromone on the performance of the NMOACO/D. The results in this paper show that correctly making use of the information related to dominated solutions can further improve the ant colony algorithm performance.

Energy enhancement using Multiobjective Ant colony optimization with Double Q learning algorithm for IoT based cognitive radio networks

Internet of Things (IoT) is the efficient wireless communication in the modern era, energy efficiency is the primary issue that focuses mainly on the Cognitive network. Most of the CR networks are focusing on battery powdered to predominantly utilize the data dissipated in terms of spectrum sharing, dynamic spectrum access, routing and spectrum allocation. The clustering and data aggregation are the best efforts technique to enhance the energy modeling. Multiobjective Ant colony optimization (MOACO)and greedy based optimization proposed with Deep Reinforcement Learning with Double Q-learning algorithm. Most of the IoT bed models involve data aggregation and energy constrained devices with optimization techniques to enhance utilization. The cluster-based data utilization is proposed with the Q-learning algorithm and it enhances the inter cluster data aggregation. The network lifetime is improved with AI-based modeling with intra-network to enhance green communication. The simulation experiments showcase that the throughput, lifetime and jamming prediction is analyzed and enhances the energy using the MOACO, when compared to the artificial bee colony and genetic algorithm. The jamming activity at low, high moderate stages is analyzed using the AI and MOACO algorithms.

Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT

The Mobile networks deploy and offers a multiaspective approach for various resource allocation paradigms and the service based options in the computing segments with its implication in the Industrial Internet of Things (IIOT) and the virtual reality. The Mobile edge computing (MEC) paradigm runs the virtual source with the edge communication between data terminals and the execution in the core network with a high pressure load. The demand to meet all the customer requirements is a better way for planning the execution with the support of cognitive agent. The user data with its behavioral approach is clubbed together to fulfill the service type for IIOT. The swarm intelligence based and reinforcement learning techniques provide a neural caching for the memory within the task execution, the prediction provides the caching strategy and cache business that delay the execution. The factors affecting this delay are predicted with mobile edge computing resources and to assess the performance in the neighboring user equipment. The effectiveness builds a cognitive agent model to assess the resource allocation and the communication network is established to enhance the quality of service. The Reinforcement Learning techniques Multi Objective Ant Colony Optimization (MOACO) algorithms has been applied to deal with the accurate resource allocation between the end users in the way of creating the cost mapping tables creations and optimal allocation in MEC.