Ant Colony Optimization Metaheuristic Research Articles

Metaheruistic Optimization Based Ensemble Machine Learning Model for Designing Detection Coil with Prediction of Electric Vehicle Charging Time

An efficient charging time forecasting reduces the travel disruption that drivers experience as a result of charging behavior. Despite the machine learning algorithm’s success in forecasting future outcomes in a range of applications (travel industry), estimating the charging time of an electric vehicle (EV) is relatively novel. It can help the end consumer plan their trip based on the estimation data and, hence, reduce the waste of electricity through idle charging. This increases the sustainability factor of the electric charging station. This necessitates further research into the machine learning algorithm’s ability to predict EV charging time. Foreign object recognition is an essential auxiliary function to improve the security and dependability of wireless charging for electric vehicles. A comparable model is used to create the object detection circuit in this instance. Within this research, the ensemble machine learning methods employed to estimate EV charging times included random forest, CatBoost, and XGBoost, with parameters being improved through the metaheuristic Ant Colony Optimization algorithm to obtain higher accuracy and robustness. It was demonstrated that the proposed Ensemble Machine Learning Ant Colony Optimization (EML_ACO) algorithm achieved 20.5% of R2, 19.3% of MAE, 21% of RMSE, and 23% of MAPE in the training process. In comparison, it achieves 12.4% of R2, 13.3% of MAE, 21% of RMSE, and 12.4% of MAPE during testing.

A Statistical Comparison of Metaheuristics for Unrelated Parallel Machine Scheduling Problems with Setup Times

Manufacturing scheduling aims to optimize one or more performance measures by allocating a set of resources to a set of jobs or tasks over a given period of time. It is an area that considers a very important decision-making process for manufacturing and production systems. In this paper, the unrelated parallel machine scheduling problem with machine-dependent and job-sequence-dependent setup times is addressed. This problem involves the scheduling of tasks on unrelated machines with setup times in order to minimize the makespan. The genetic algorithm is used to solve small and large instances of this problem when processing and setup times are balanced (Balanced problems), when processing times are dominant (Dominant P problems), and when setup times are dominant (Dominant S problems). For small instances, most of the values achieved the optimal makespan value, and, when compared to the metaheuristic ant colony optimization (ACOII) algorithm referred to in the literature, it was found that there were no significant differences between the two methods. However, in terms of large instances, there were significant differences between the optimal makespan obtained by the two methods, revealing overall better performance by the genetic algorithm for Dominant S and Dominant P problems.

Heterogeneous fleet management for one-way electric carsharing system with optional orders, vehicle relocation and on-demand recharging

As a part of the trend of shared transport, carsharing has received increasing attention over the recent years. We address the operation management problem of a heterogeneous electric vehicle fleet in a one-way carsharing system with relocation and on-demand recharging activities, to fulfill the optional rental orders that are known in advance. We present a mixed-integer linear programming formulation for the problem with the objective to maximize the overall profit, which can be solved to the optimum for instances with up to 50 rental orders by CPLEX within an hour. To deal with large-scale problems, we apply the Dantzig–Wolfe decomposition technique and propose an approach which hybridizes the ant colony optimization (ACO) metaheuristics into the column generation framework to quickly solve the pricing subproblems. The evaluation experiments using randomly generated instances of different scales have shown that, the proposed hybrid approach is both efficient and efficacious: (1) the gap between the solutions by the hybrid approach and the optimal solutions is below 5% in average for small instances; (2) for large instances with over 100 rental orders, the overall performance of the hybrid approach is significantly better than those of CPLEX and ACO metaheuristics, with rather short time consumption. Furthermore, a case study is conducted based on practical transportation data of Beijing, using the net profit and order fulfillment rate as performance criteria. Insightful findings have been revealed by sensitivity analysis.

Hybrid algorithm for the solution of the periodic vehicle routing problem with variable service frequency

This document addresses the problem of scheduling and routing a specific number of vehicles to visit a set of customers in specific time windows during a planning horizon. The vehicles have a homogeneous limited capacity and have their starting point and return in a warehouse or initial node, in addition, multiple variants of the classic VRP vehicle routing problem are considered, where computational complexity increases with the increase in the number of customers to visit, as a characteris-tic of an NP-hard problem. The solution method used consists of two connected phases, the first phase makes the allocation through a mixed-integer linear programming model, from which the visit program and its frequency in a determined plan-ning horizon are obtained. In the second phase, the customers are grouped through an unsupervised learning algorithm, the routing is carried out through an Ant Colony Optimization metaheuristic that includes local heu-ristics to make sure com-pliance with the restrictive factors. Finally, we test our algorithm by performance measures using instances of the literature and a comparative model, and we prove the effectiveness of the proposed algorithm.

Hybrid Nearest-Neighbor Ant Colony Optimization Algorithm for Enhancing Load Balancing Task Management

Many computer problems that arise from real-world circumstances are NP-hard, while, in the worst case, these problems are generally assumed to be intractable. Existing distributed computing systems are commonly used for a range of large-scale complex problems, adding advantages to many areas of research. Dynamic load balancing is feasible in distributed computing systems since it is a significant key to maintaining stability of heterogeneous distributed computing systems (HDCS). The challenge of load balancing is an objective function of optimization with exponential complexity of solutions. The problem of dynamic load balancing raises with the scale of the HDCS and it is hard to tackle effectively. The solution to this unsolvable issue is being explored under a particular algorithm paradigm. A new codification strategy, namely hybrid nearest-neighbor ant colony optimization (ACO-NN), which, based on the metaheuristic ant colony optimization (ACO) and an approximate nearest-neighbor (NN) approaches, has been developed to establish a dynamic load balancing algorithm for distributed systems. Several experiments have been conducted to explore the efficiency of this stochastic iterative load balancing algorithm; it is tested with task and nodes accessibility and proved to be effective with diverse performance metrics.

High Frequency and Dynamic Pairs Trading with Ant Colony Optimization

In recent years, there has been an explosion of research in metaheuristics, which provides efficient solutions that are close to optimal with lower computing times. Applying metaheuristics to finance is reasonable given that many financial decisions must be made within very short time frames, minutes or even seconds such as in the case of High Frequency Trading. In this paper, an algorithm based on Ant Colony Optimization metaheuristics is proposed to dynamically optimize the decision thresholds provided by the Pairs Trading investment strategy.The proposed algorithm is called the Ant Colony Optimization of Pairs Trading (ACO-PT) and is optimized by moving training-trading windows.The model is applied to Forex data at a high frequency, consisting of 38 Foreign Exchanges with a frequency of 15 min from September 22, 2017 until July 6, 2018. It is shown that ACO-PT can be used in deep markets efficiently and is capable of obtaining daily returns of 0.1204 $$\%$$ and a Sharpe ratio of 0.6520, which translates into an improvement over the base case for fixed thresholds of 13.21 $$\%$$ . We conclude statistically that the variation of the algorithm that showed the best performance was also the simplest variation and, therefore, the fastest.

Ant colony optimization, genetic algorithm and hybrid metaheuristics: A new solution for parallel machines scheduling with sequence-dependent set-up times

The parallel machine scheduling problem (PMSP) is one of the most difficult classes of problem. Due to the complexity of the problem, obtaining optimal solution for the problems with large size is very time consuming and sometimes, computationally infeasible. So, heuristic algorithms that provide near-optimal solutions are more practical and useful. The present study aims to propose a hybrid metaheuristic approach for solving the problem of unrelated parallel machine scheduling, in which, the machine and the job sequence dependent setup times are considered. A Mixed-Integer Programming (MIP) model is formulated for the unrelated PMSP with sequence dependent setup times. The solution approach is robust, fast, and simply structured. The hybridization of Genetic Algorithm (GA) with Ant Colony Optimization (ACO) algorithm is the key innovative aspect of the approach. This hybridization is made in order to accelerate the search process to near-optimal solution. After computational and statistical analysis, the two proposed algorithms are used to compare with the proposed hybrid algorithm to highlight its advantages in terms of generality and quality for short and large instances. The results show that the proposed hybrid algorithm has a very good performance as regards the instance size and provides the acceptable results.

Simultaneous Scheduling of Machines and AGVs in FMS Through Ant Colony Optimization Algorithm

High amount of flexibility and quick response times have become essential features of modern manufacturing systems where customers are demanding a variety of products with reduced product life cycles. Flexible manufacturing system (FMS) is the right choice to achieve these challenging tasks. The performance of FMS is dependent on the selection of scheduling policy of the manufacturing system. In Traditional scheduling problems machines are as considered alone. But material handling equipment’s are also valuable resources in FMS. The scheduling of AGVs is needed to be optimized and harmonized with machine operations. Scheduling in FMS is a well-known NP-hard problem due to considerations of material handling and machine scheduling. Many researchers addressed machine and AGVs individually. In this work an attempt is made to schedule both the machines and AGVs simultaneously. For solving these problems-a new metaheuristic Ant Colony Optimization (ACO) algorithm is proposed.

Ant-Set: A Subset-Oriented Ant Colony Optimization Algorithm for the Set Covering Problem

This paper proposes an algorithm for the set covering problem based on the metaheuristic Ant Colony Optimization (ACO) called Ant-Set, which uses a lineoriented approach and a novelty pheromone manipulation based on the connections between components of the construction graph, while also applying a local search. The algorithm is compared with other ACO-based approaches. The results obtained show the effectiveness of the algorithm and the impact of the pheromone manipulation.

TABU Search Prioritized Ant Colony Metaheuristic Optimization based Dynamic Symbolic Genetic Technique for High Coverage Test Suite Generation

TABU Search Prioritized Ant Colony Metaheuristic Optimization based Dynamic Symbolic Genetic Technique for High Coverage Test Suite Generation

User mobility and Quality-of-Experience aware placement of Virtual Network Functions in 5G

Virtual Network Functions (VNFs) in cloud servers of Fifth Generation (5G) network systems are responsible for executing offloaded codes from mobile users. Placement of VNFs in the cloud is very complicated to get on-time execution service due to many reasons including users’ mobility and resource heterogeneity, which often cause VNF relocations from one data center to another. Minimizing service delay (i.e., maximizing user Quality-of-Experience) for the user applications and the number of VNF relocations are the two main design goals of VNF placement problem; however, they do oppose each other. In this paper, we have formulated the above problem as a Multi-objective Integer Linear Programming (MILP), which is proven to be an NP-hard one. The proposed optimization framework trades-off between the number of VNF relocations and user Quality-of-Experience. We then develop an Artificial Intelligence based meta-heuristic Ant Colony Optimization (ACO) algorithm to achieve sub-optimal placement of VNFs within polynomial time. The performance analysis results, carried out in Cloudsim, depict that the proposed system outperforms the state-of-the-art works significantly in terms of user satisfaction and VNF relocation overhead.

An Evaluation on Game Theory Problem of RSSI Localization Technique in Wireless Sensor Networks

The most interesting and challenging research areas in WSNs are routing protocol based on RSSI localization technique in wireless sensor networks. Get-up-and-go safeguarding is the most important experiment for WSNs and make the most of the energy efficiently during routing is an essential requirement and is a demanding task for all other research areas in WSNs. Enhancing the lifespan of the network be contingent on game theory based on RSSI localization technique in wireless sensor networks are the foremost purposes in Machiavellian WSNs since the course-plotting up for theory based on RSSI sensor nodes are battery operated and cannot be replenished or recharged frequently. Here game theory based on RSSI localization for increasing the Wireless Sensor Network life-time using Ant Colony Optimization metaheuristics.

Mining and IoT-based Vehicle Ad-hoc NETwork: Industry opportunities and innovation

In recent years, many mining companies have begun to deploy new emerging technologies to increase their productivity and reduce costs without compromising the safety of staff. The mining industry is going through an intense period of change, making mining companies’ ability to innovate and improve indispensable. Emerging technologies are set to change the way miners operate over the next decade. Internet of Things (IoT) is one of them, leading the way in bringing about positive change in the mining industry. This work is in line with IoT-based mobile Ad Hoc networks for mobile vehicles, commonly referred to as VANET (Vehicle Ad-hoc NETwork). VANET communication has recently become an increasingly popular research topic in the area of wireless networking as well as the automobile industries. A routing protocol based on ant colony optimization metaheuristic is proposed. Modeling of this protocol was adopted in a mining environment and then implemented by simulation using OPNET Modeler. The performance of the proposed routing protocol is compared with other well-known routing protocol used in ad-hoc networks.

Comparative study between exact and metaheuristic approaches for virtual machine placement process as knapsack problem

In cloud computing, the virtual machine placement is a critical process which aims to identify the most appropriate physical machine to host the virtual machine. It has a significant impact on the performance, resource usage and energy consumption of the datacenters. In order to reduce the number of active physical machines in a datacenter, several virtual machine placement schemes have already been designed and proposed. This study investigates how do four different methods compare to each other in terms of accuracy and efficiency for solving the virtual machine placement as a knapsack problem. A new approach has been adopted which focuses on maximizing the use of a server’s central processing unit resource considering a certain capacity threshold. The compared methods are classified; two belong to the category of the exact methods, i.e., branch and bound and dynamic programming, while the other two represent the approximate approach, i.e., genetic algorithm and ant colony optimization algorithm. Experimental results show that the metaheuristic ant colony optimization algorithm outperforms the other three algorithms in terms of efficiency.

Meta-heuristic Ant Colony Optimization Based Unequal Clustering for Wireless Sensor Network

Sensor nodes are randomly deployed to perform specific area monitoring in geographical region and temporal space. The network connectivity maintenance is a major requirement for accurate event detection with minimum energy consumption. To minimize the energy consumption, various clustering algorithms have been evolved in research studies. But, they failed to consider the other performance parameters such as quality of service constraints and the performance level. The initialization of nodes nearer to the base station (BS) as relay nodes reduces the number of relay node participation and increases the performance. This paper proposes the novel ant colony meta-heuristic based unequal clustering for the novel cluster head (CH) selection. The data fusion from the CH node to the intermediate node called Rendezvous node reduces the message transmissions and hence the energy consumed by the nodes is minimum. The neighbor finding phase and the link maintenance through the Meta-Heuristic Ant Colony Optimization approach selects the optimal path between the nodes which increases the packets delivered to the destination. The population initialization requires more time at this stage. Hence, the Haversine distance is estimated among the nodes which also reduces the dimensionality of the message transmission among the nodes. The prediction of optimal path and the CH selection using Ant Colony Optimization Meta-Heuristic and unequal clustering reduces the energy consumption effectively. The comparative analysis of proposed Meta-Heuristic Ant Colony Optimization based Unequal Clustering with the existing unequal clustering approaches on the basis of various performance parameters such as Packet Delivery Ratio, number of packets sent to the BS, energy consumption, residual energy and the percentage of dead nodes shows the effectiveness of proposed work in WSN applications.

Ant colony algorithm for automotive safety integrity level allocation

ISO 26262, the new automotive functional safety standard, aims to foster the design and development of safe products by ensuring that the risks posed by hazardous components are reduced to a residual level. Therefore, the standard defines and uses the concept of Automotive Safety Integrity Levels (ASILs) that classify the strictness of safety requirements to be assigned to the failure modes of the system based on the hazard they may cause. ASIL allocation can be described as a hard optimization problem focused on finding the optimal ASIL allocation that maximizes the safety requirements and minimizes cost. However, finding this optimal allocation among a set of possible allocations can represent a difficult task in large systems that contain a large number of components, which subsequently increases the search space. In this paper, we introduce a novel approach that uses the nature-inspired meta-heuristic Ant Colony Optimization (ACO) algorithm to solve the ASIL allocation problem and makes use of strategies that reduce the solution space. The problem was formulated as a construction graph, which the ants use to construct possible ASIL allocations. The search space reduction is accelerated considerably by both the effective performance of the ACO and the convergence of the algorithm on the optimal solution. This approach has been evaluated by applying it to a hybrid braking system and a steer-by-wire system. The results show a significant improvement over genetic-based, penguins search-based and tabu search-based approaches.

Optimización basada en Colonia de Hormigas aplicada al problema de Planificación de Celdas en redes de radio para sistemas de telefonía móvil


 
 
 
 
 
 En este trabajo se presenta una propuesta computacional para la solución del problema de Planificación de Celdas. La importancia de dicho problema en el área de las Telecomunicaciones lo impone como un referente en la búsqueda de nuevos métodos de optimización. Por la complejidad del problema, en este trabajo se utiliza una relajación discreta del mismo y se propone un modelo matemático para la aplicación de la Meta-heurística Optimización basada en Colonia de Hormigas (ACO). Para el análisis de los resultados se seleccionaron 5 instancias del problema de diferentes tamaños y se aplicó el algoritmo Sistema de Hormigas (AS). Lo resultados muestran que la propuesta explora de manera eficiente el espacio de búsqueda, encontrando la solución óptima a cada instancia con un costo computacional relativamente bajo. Estos resultados son comparados con 3 alternativas evolutivas de referencia internacional que han sido aplicadas a las mismas instancias de estudio, constatándose una mejora significativa por parte de nuestra propuesta. 
 
 
 
 
 

Resource Allocation via Max–Min Goodput Optimization for BIC-OFDMA Systems

In this paper, a novel resource allocation (RA) strategy is designed for the downlink of orthogonal frequency division multiple access networks employing practical modulation and coding under quality of service constraints and retransmission techniques. Compared with previous works, two basic concepts are combined together, namely: 1) taking the goodput (GP) as performance metric and 2) ensuring maximum fairness among users. Thus, the resulting RA maximizes the GP of the worst users, optimizing subcarrier allocation (SA), per-subcarrier power allocation (PA), and adaptation of modulation and coding (AMC) of the active users, yielding a nonlinear nonconvex mixed optimization problem (OP). The intrinsic demanding difficulty of the OP is tackled by iteratively and optimally solving the AMC, PA, and SA subproblems, devoting special care to the difficult nonlinear combinatorial SA-OP. First, the optimal (yet computationally complex) solution is found by applying the branch and bound method to the optimal SA solution found in the relaxed domain, and accordingly, it is taken as benchmark. Then, an innovative suboptimal yet efficient solution based on the metaheuristic ant colony optimization (ACO) framework is derived. The proposed RA strategy is corroborated by comprehensive simulations, showing improved performance even at the cost of affordable numerical complexity.

Prediction of the pH as indicator of porcine meat quality using Raman spectroscopy and metaheuristics

Up to now, there is no available sensor which can assess meat quality based on a single measuring process. Currently, the pH values measured 45min (pH45) as well as 24h (pH24) postmortem (p.m.) are used as quality indicators. The herein presented approach aims to tackle this problem by combining Raman spectroscopy with the ant colony optimization (ACO) metaheuristics that included a priori metabolic knowledge of pH-correlated Raman peaks in the modeling process. For the validation of this method, Raman spectra and pH values from 96 porcine hams recorded in a slaughterhouse 1–2hp.m. as well as 24hp.m. were used. By applying the ACO procedure, a segment of 25 wavenumbers out of a full Raman spectrum of 1024 wavenumbers was selected for the prediction of the pH value. For the segments, different fingerprint locations were selected according to the different metabolic information required to predict pH45 and pH24. From the pre-rigor spectra, the pH45 can be predicted with a root mean square error of cross-validation (RMSECV) of 0.13 pH units and the pH24 with an RMSECV of 0.21 pH units. Similar results were achieved with the Raman spectra measured after 24h. Hence, it can be concluded that the meat's quality markers pH45 and pH24 information is inherently present in both, the pre-rigor (1–2h) and the post-rigor (24h) Raman spectra. Additionally, the results of the pH decline prediction with a RMSECV of 0.21 pH units suggest that this information could be virtually present in any spectra taken within 24hp.m. This might be of interest for the meat production industry where assessing meat quality in a fast and non-invasive way is desired.

Biological complexity: ant colony meta-heuristic optimization algorithm for protein folding

Ant colony meta-heuristic optimization (ACO) is one of the few algorithms that can help to gain an atomic level insight into the conformation of protein folding states, intermediate weights and pheromones present along the protein folding pathway. These are analysed by nodes (amino acids), and these nodes depend upon the probability of next optimized node (amino acids). Nodes have conformational degrees of freedom as well as depend upon the natural factors and collective behaviour of biologically important molecules like temperature, volume, pressure and other ensembles. This biological quantum complexity can be resolved using ACO algorithm. Ants are visually blind and important behaviour of communication among individuals or colony of ant environment is based on chemicals (pheromones) deposited by the ants. Just like ants, proteins are also a group of colony; amino acids are node (amino acid) attached to each others with the help of bonds. This paper is aimed to determine the factors affecting protein folding pattern using ant colony algorithm. Protein occurs structurally in a compact form and determining the ways of protein folding is called NP hard (non-deterministic polynomial-time hard) problem. Using the ACO, we have developed an algorithm for protein folding. It is interesting to note that based on ants ability to find new shorter path between the nest and the food, proteins can also be optimized for shorter path between one node to another node and the folding pattern can be predicted for an unknown protein (ab initio). We have developed an application based on ACO in Perl language (PFEBRT) for determining optimized folding path of proteins.