Ant Colony Optimization Approach Research Articles

Optimal allocation of wind power hybrid energy storage capacity based on ant colony optimization algorithm

Renewable energy sources, such as wind power, face challenges owing to their erratic construction and intermittent nature, leading to the emergence of energy storage strategies to achieve grid stability and reliable electricity supplies. Determination of the correct size of energy storage devices for wind power plants is complicated. In this study, the ant colony optimization (ACO) algorithm is proposed for the best distribution/sizing of wind-generated hybrid storage capacity. Ants’ foraging habits motivate the development of wind turbines with high dependability, high efficiency and low energy storage costs. A study is conducted to confirm the practicality of the proposed method, using real data from a wind power plant to calculate the contrast between wind energy production and load consumption features, to influence the appropriate quantity for energy storage organization. The study demonstrates that the ACO approach provides precise and efficient solutions for determining the optimal energy distribution in wind power plants.

A Four-Grade Astrocytoma Classification System with Optimal Feature Selection Using Ant Colony Optimization Algorithm

An efficient Four-Grade Astrocytoma brain tumor classification is proposed in this research work. The work highlights the optimal feature selection using a hybrid combination of Ant Colony Optimization (ACO) algorithm and machine learning techniques, leading to better classification accuracy. A diverse set of features including spatial and frequency domain features will provide better discrimination of patterns. ACO provides a robust and efficient mechanism for exploring the vast space of possible feature subsets. A total of 135 features are extracted including Tamura features, Gabor Wavelet Features, Coiflet Wavelet Coefficients, Speeded Up Robust Features, and GLCM features. Ant Colony Optimization is used to find out the optimum set of features. The selected feature set is used for training a Decision Tree classifier, where the Astrocytoma MRI images are categorized into four grades of astrocyoma namely, pilocytic astrocytoma, diffuse astrocytoma, anaplastic astrocytoma and glioblastoma multiforne. Classification is also performed using KNN classifier, SVM classifier and Ensemble Bagged Tree Classifier. Performance metrics like accuracy, sensitivity, specificity, precision and FScore are utilized to evaluate the proposed system’s performance. It is found out that the decision tree classifier outperforms the other classifiers. Thus this integrated approach of ACO with machine learning classifiers results in improved Astrocytoma classification performance aiding clinicians in making accurate treatment decisions.

Enhancing efficiency in PCB assembly for the leading global electronics manufacturing services firm: a TRIZ and Ant Colony Optimization approach

Enhancing efficiency in PCB assembly for the leading global electronics manufacturing services firm: a TRIZ and Ant Colony Optimization approach

Ant Colony Optimization based Support Vector Machine for Improved Classification of Unbalanced Datasets

Classifying unbalanced datasets is a challenging task, and traditional Support Vector Machine (SVM) algorithms often struggle to achieve accurate results. In this paper, we propose an Ant Colony Optimization based Support Vector Machine (SVM-ACO) algorithm that outperforms SVM on unbalanced datasets. Traditional SVM algorithms tend to be biased towards the majority class, leading to suboptimal performance for the minority class. However, SVM-ACO addresses this issue by incorporating the ant colony optimization approach, which assigns different weights to samples based on their class membership, giving higher importance to the minority class. This enables SVM-ACO to achieve a more balanced classification performance and improved accuracy. We compare the accuracy achieved by SVM-ACO and SVM on various imbalanced datasets from the literature. The experimental results support the effectiveness of SVM-ACO in addressing the challenges of unbalanced datasets. By incorporating ant colony optimization, SVM-ACO achieves improved accuracy and provides a promising alternative for handling unbalanced classification problems.

Dynamic Decision Making for Connected Vehicles in IoT System

Intelligent Transportation System (ITS) which focuses on dynamic decision making for IoT data processes using distributed and decentralized systems has attracted much attention in recent years. In this paper, we propose a novel approach for enabling computational intelligence into dynamic decision-making for connected vehicles to provide various applications of smart cities. The proposed approach is based on the ant colony optimization (ACO) approach, which is able to improve the performance of intelligent transportation systems. In addition, the simulated results are compared with previous works in the literature.

A Hybrid Clustered Ant Colony Optimization Approach for the Hierarchical Multi-Switch Multi-Echelon Vehicle Routing Problem with Service Times

In this paper, the Hierarchical Multi Switch Multi Echelon Vehicle Routing Problem with Service Times (HMSME-VRP-ST) is presented. This novel problem considers distribution applications in which goods are delivered either directly from a central depot or through intermediate facilities called switch points. Commodities are loaded into interchangeable containers called swap bodies at the central depot, and can be transferred from one vehicle to another at the switch points. The goal is to minimize fixed and variable costs of the vehicle fleet and swap-bodies while serving a predetermined set of customers. The routes are constrained by time and swap bodies by loading capacity. A mathematical model of the HMSME-VRP-ST is presented, and a Hybrid Clustered Ant Colony Optimization (HCACO) algorithm is proposed to address the complexity of the problem. The approach utilizes the ant-based clustering algorithm, combining the density and connectivity properties of clustering for creating promising neighborhoods with the solution construction methodology and pheromone-based memory of the Ant Colony Optimization framework. Additionally, two local search schemes based on Variable Neighborhood Descent are incorporated to further improve the generated solutions. The behavior of each HCACO variant is analyzed, and their results are compared to a Greedy Randomized Adaptive Search Procedure metaheuristic in 36 newly generated benchmarks comprising of clustered, uniformly random, and mixed clustered-random instances.

Measuring Global Character Dimensions: An Ant Colony Optimization Approach toward Three Core Strength Scales

The Values in Action (VIA) framework encompasses 24 universally valued character strengths. Recent factor-analytic work has identified three global core strengths (metatraits) that proved to be well-interpretable and cross-culturally replicable: positivity, dependability, and mastery. However, there are no short scales to economically measure these core strengths that would encourage (large-scale) survey-based research on the global level of the VIA trait space. In the present study, we selected 18 items to measure the three metatraits from the 96-item IPIP-VIA-R inventory. To optimize the item selection while considering multiple psychometric criteria simultaneously, we made use of Ant Colony Optimization. Thereby, we obtained balanced-keyed scales that cover the heterogeneous constructs well, showed good model fit and reliability across six samples from Germany and the U.K. (total N = 2,754), and achieved scalar measurement invariance across countries. Furthermore, we demonstrated each scale’s validity by locating the three core strengths in a nomological net with personality and value metatraits, life satisfaction, and behavioral criteria. Available in the public domain in English and German, these both valid and economical core strength scales may further stimulate integrative research on personality and values.

A new path planning strategy integrating improved ACO and DWA algorithms for mobile robots in dynamic environments.

This article is concerned with the path planning of mobile robots in dynamic environments. A new path planning strategy is proposed by integrating the improved ant colony optimization (ACO) and dynamic window approach (DWA) algorithms. An improved ACO is developed to produce a globally optimal path for mobile robots in static environments. Through improvements in the initialization of pheromones, heuristic function, and updating of pheromones, the improved ACO can lead to a shorter path with fewer turning points in fewer iterations. Based on the globally optimal path, a modified DWA is presented for the path planning of mobile robots in dynamic environments. By deleting the redundant nodes, optimizing the initial orientation, and improving the evaluation function, the modified DWA can result in a more efficient path for mobile robots to avoid moving obstacles. Some simulations are conducted in different environments, which confirm the effectiveness and superiority of the proposed path planning algorithms.

Empirical Mode Decomposition with Imf Using Ant Colony Optimization

The serial remote sensing images allow us to view how a certain area has changed and grown across period. Even though these raw images have fewer opportunities to generate information and additional insight, serial remote sensing images (SRSI) offer a significant opportunity to generate clusters and patterns. The implementation of SRSI aggregation is supported by the developments of patterns in a variety of circumstances, which include urbanization, the spread of native vegetation, and farming. We propose a novel outlook to mining sequence patterns that makes use of Ant Colony Optimization and Empirical Mode Decomposition. The newly created Empirical Mode Decomposition (EMD) and Ant Colony Optimization (ACO) approach is integrated to extract the most important characteristics features of significantly accurate performance from serial remote sensing images based on distinct criteria. The outcomes demonstrate that the accuracy of projected sequence patterns can be greatly improved by combining a new hybrid method with EMD and ACO feature selection. Because of this, the method is appealing for employing a farmland dataset based on serial remote sensing images to create ground-based stream-flow for strategically extracting spatially sequential patterns despite requesting less computational mining time and cost.

An Integrated Approach of Ant Colony Optimization (ACO), Machine Learning (ML), and Fuzzy Logic for Revolutionizing Inventory Management in Modern Supply Chains

An Integrated Approach of Ant Colony Optimization (ACO), Machine Learning (ML), and Fuzzy Logic for Revolutionizing Inventory Management in Modern Supply Chains

Effective fault module localization in substation critical equipment: an improved ant colony optimization and back propagation neural network approach

AbstractThe rapid development of substations has increased the demand for accurate and fast fault prediction systems. In order to achieve rapid localization and autonomous decision‐making of fault modules and types in substations, the article proposes a fault autonomous localization algorithm based on improved ant colony optimization (IACO) and back propagation neural network (BPNN). The fault data of the substation secondary equipment for training and testing the BPNN model is based on the actual operating equipment of the substation, which can significantly improve the reliability of the model results. In addition, the IACO is used to globally optimize the weights and thresholds of BPNN, and the number of hidden layer nodes in BPNN was analyzed to further improve the accuracy of the established fault prediction algorithm. The test results show that the fault prediction accuracy of the BPNN model optimized by IACO is 93.67%, which is significantly improved compared to the traditional BPNN and BPNN with ant colony optimization method (with an accuracy of 82.98% and 91.04%). The above results effectively demonstrate the high accuracy and effectiveness of the established prediction algorithm in processing data and locating faults, which can improve the maintenance and operational efficiency of substations.

Optimization of Association Rule Using Ant Colony Optimization (ACO) Approach

The Apriori algorithm creates all possible association rules between items in the database using the Association Rule Mining and Apriori Algorithm. Using Ant Colony Optimization, a new algorithm is proposed for improving association rule mining results. Using ant colony behaviour as a starting point, an optimization of ant colonies (ACO) is developed. The Apriori algorithm creates association rules. Determine the weakest rule set and reduce the association rules to find rules of higher quality than apriori based on the Ant Colony algorithm's threshold value. Through optimization and improvement of rules generated for ACO, the proposed research work aims to reduce the scanning of datasets.

Adaptation of the Ant Colony Algorithm to Avoid Congestion in Wireless Mesh Networks

Wireless mesh networks have recently presented a promising environment for many researchers to develop large-scale wireless communication. Traffic in WMNs often suffers from congestion due to heavy traffic load’s saturation of certain routes. Therefore, this article proposes an efficient approach for congestion awareness and load balancing in WMNs, based on the Ant Colony Optimization (ACO) approach. The proposed approach aims to raise the performance of the WMN by distributing the traffic load between optimal routes and avoiding severe traffic congestion. The proposed approach relies on three basic mechanisms: detection of severe congestion within the ideal paths used for data transmission, creation of ideal secondary paths with updated pheromone values, and distribution of the traffic load (data packet flow) between the primary and secondary ideal paths. According to the results of the NS2 simulator, the suggested approach increased the WMN throughput by 14.8% when compared to the CACO approach and by 37% when employing the WCETT approach. The results also showed that the proposed approach achieved an average end-to-end delay closing of 0.0562, while WCETT and CACO approaches achieved an average end-to-end delay close to 0.1021 and 0.0976, respectively. The results indicated that the proposed approach achieved a lower percentage of dropped packets by 6.97% and 0.99% compared to the WCETT and CACO approaches. Thus, the proposed approach is effective in improving the performance of WMNs.

Multi-Objective Fault-Coverage Based Regression Test Selection and Prioritization Using Enhanced ACO_TCSP

Regression testing of the software during its maintenance phase, requires test case prioritization and selection due to the dearth of the allotted time. The resources and the time in this phase are very limited, thus testers tend to use regression testing methods such as test case prioritization and selection. The current study evaluates the effectiveness of testing with two major goals: (1) Least running time and (2) Maximum fault coverage possible. Ant Colony Optimization (ACO) is a well-known soft computing technique that draws its inspiration from nature and has been widely researched, implemented, analyzed, and validated for regression test prioritization and selection. Many versions of ACO approaches have been prolifically applied to find solutions to many non-polynomial time-solvable problems. Hence, an attempt has been made to enhance the performance of the existing ACO_TCSP algorithm without affecting its time complexity. There have been efforts to enhance the exploration space of various paths in each iteration and with elite exploitation, reducing the total number of iterations required to converge to an optimal path. Counterbalancing enhanced exploration with intelligent exploitation implies that the run time is not adversely affected, the same has also been empirically validated. The enhanced algorithm has been compared with the existing ACO algorithm and with the traditional approaches. The approach has also been validated on four benchmark programs to empirically evaluate the proposed Enhanced ACO_TCSP algorithm. The experiment revealed the increased cost-effectiveness and correctness of the algorithm. The same has also been validated using the statistical test (independent t-test). The results obtained by evaluating the proposed approach against other reference techniques using Average Percentage of Faults Detected (APFD) metrics indicate a near-optimal solution. The multiple objectives of the highest fault coverage and least running time were fruitfully attained using the Enhanced ACO_TCSP approach without compromising the complexity of the algorithm.

GDASC: Optimizing Urban Green Space for Public Health: An Ant Colony Optimization Approach

GDASC: Optimizing Urban Green Space for Public Health: An Ant Colony Optimization Approach

An efficient multi-objective scheduling algorithm based on spider monkey and ant colony optimization in cloud computing

Due to easier access, improved performance, and lower costs, the use of cloud services has increased dramatically. However, cloud service providers are still looking for ways to complete users’ jobs at a high speed to increase profits and reduce energy consumption costs. To achieve such a goal, many algorithms for scheduling problem have been introduced. However, most techniques consider an objective in the scheduling process. This paper presents a new hybrid multi-objective algorithm, called SMO_ACO, for addressing the scheduling problem. The proposed SMO_ACO algorithm combines Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) algorithm. Additionally, a fitness function is formulated to tackle 4 objectives of the scheduling problem. The proposed fitness function considers parameters like schedule length, execution cost, consumed energy, and resource utilization. The proposed algorithm is implemented using the Cloud Sim toolkit and evaluated for different workloads. The performance of the proposed technique is verified using several performance metrics and the results are compared with the most recent existing algorithms. The results prove that the proposed SMO_ACO approach allocates resources efficiently while maintaining cloud performance that increases profits.

A Hybrid Metaheuristic Solution Method to Traveling Salesman Problem with Drone

The challenging idea of using drones in last-mile delivery systems of logistics addresses a new routing problem referred to as the traveling salesman problem with drone (TSP-D). TSP-D aims to construct a route to deliver parcels to a set of customers by either a truck or a drone, thereby minimizing operational costs. Since TSP-D is considered NP-hard, using metaheuristics is one of the most promising solutions. This paper presents a hybrid metaheuristic solution method of TSP-D based on two state-of-the-art algorithms: the genetic algorithm and ant colony optimization algorithm. Heuristics in TSP-D literature are based on two consequent decisions: truck routing and drone assignment. Unlike those in the existing literature, the proposed metaheuristic constructs both truck and drone routes simultaneously. Additionally, to the best of our knowledge, we introduce for the first time a solution method on the basis of an ant colony optimization approach to TSP-D. Additionally, we propose a binary pheromone framework for both drone and truck, diverging from the traditional pheromone structure. Computational experiments indicate that the proposed hybrid metaheuristic algorithm is able to generate optimal routes for provided instances of TSP-D benchmarking. In addition, the algorithm improves the best-known solutions of some instances found by rival heuristics.

A Quantum-Inspired Ant Colony Optimization Approach for Exploring Routing Gateways in Mobile Ad Hoc Networks

Establishing internet access for mobile ad hoc networks (MANET) is a job that is both vital and complex. MANET is used to build a broad range of applications, both commercial and non-commercial, with the majority of these apps obtaining access to internet resources. Since the gateways (GWs) are the central nodes in a MANET’s ability to connect to the internet, it is common practice to deploy numerous GWs to increase the capabilities of a MANET. Current routing methods have been adapted and optimized for use with MANET through the use of both conventional routing techniques and tree-based network architectures. Exploring new or tacking-failure GWs also increases network overhead but is essential given that MANET is a dynamic and complicated network. To handle these issues, the work presented in this paper presents a modified gateway discovery approach inspired by the quantum swarm intelligence technique. The suggested approach follows the non-root tree-based GW discovery category to reduce broadcasting in the process of exploring GWs and uses quantum-inspired ant colony optimization (QACO) for constructing new paths. Due to the sequential method of execution of the algorithms, the complexity of ACO grows dramatically with the rise in the number of paths explored and the number of iterations required to obtain better performance. The exploration of a huge optimization problem’s solution space may be made much more efficient with the help of quantum parallelization and entanglement of quantum states. Compared to other broad evolutionary algorithms, QACO s have more promise for tackling large-scale issues due to their ability to prevent premature convergence with a simple implementation. The experimental results using benchmarked datasets reveal the feasibility of the suggested approach of improving the processes of exploring new GWs, testing and maintaining existing paths to GWs, exploring different paths to existing GWs, detecting any connection failure in any route, and attempting to fix that failure by discovering an alternative optimal path. Furthermore, the comparative study demonstrates that the utilized QACO is valid and outperforms the discrete binary ACO algorithm (AntHocNet Protocol) in terms of time to discover new GWs (27% improvement on average), time that the recently inserted node takes to discover all GWs (on average, 70% improvement), routing overhead (53% improvement on average), and gateway’s overhead (on average, 60% improvement).

Design, analysis and optimization of a composite leaf spring for light vehicles

Leaf spring is the most widely used suspension system in automobiles which is subjected to various types of stresses and deflection. Generally, steel is used as a material for the leaf spring but steel is having high density due to which its weight is also more. There is a lot of scopes to replace the steel with other material to reduce the total weight of the vehicle. The advancement in plastic-reinforced fibre is useful for decreasing the weight of the component the composite materials are having high strength and low weight as compared to steel. In this proposed work composite material is used in place of conventional steel. The composite material is made up of glass and fibre called Epoxy resin. Various test is performed to carry out the properties of the composite material. Ant colony optimization approach is used to design and optimization of composite leaf springs. A geometric model of the leaf spring is prepared in the CATIA V5 software and analysis is done. It is found that composite springs can sustain the load the same as that of the steel leaf spring, and less deflection is observed in composite springs than in steel leaf springs. The density of the composite material is low so eventually, weight is also reduced by 70 % to 80%.

Yin-yang framework of decision-making: Development and assessment of Yin-yang decision-making styles questionnaire

Decision-making styles represent the typical manner individuals use to make decisions. This study aimed to develop and test the structure of a decision-making style questionnaire based on the Yin-yang framework of decision making. Two studies were carried out. In Study 1, we generated an initial item pool of 122 items using a theory-driven approach. Then three focus groups were used to finalize 54 items for the field testing. Third, using a hybrid of iterative exploratory factor analysis and ant colony optimization approach (n = 895), we identified 23 items that were deemed best to represent five constructs of the yin-yang framework of decision making: adaptability, relative positioning, self-awareness, proactivity, and prudence. In Study 2, we performed the validation study (n = 569) by testing three models of the yin-yang framework of decision making styles. We also explored age and sex differences. The results from two studies provide initial support for the factor structure and reliability of the Yin-yang decision-making questionnaires. The results were discussed in the context of the yin-yang framework and age and sex differences regarding these constructs.