Ant Colony Optimization Genetic Algorithm Research Articles

Mission Planning of UAVs and UGV for Building Inspection in Rural Area

Unmanned aerial vehicles (UAVs) have become increasingly popular in the civil field, and building inspection is one of the most promising applications. In a rural area, the UAVs are assigned to inspect the surface of buildings, and an unmanned ground vehicle (UGV) is introduced to carry the UAVs to reach the rural area and also serve as a charging station. In this paper, the mission planning problem for UAVs and UGV systems is focused on, and the goal is to realize an efficient inspection of buildings in a specific rural area. Firstly, the mission planning problem (MPP) involving UGVs and UAVs is described, and an optimization model is established with the objective of minimizing the total UAV operation time, fully considering the impact of UAV operation time and its cruising capability. Subsequently, the locations of parking points are determined based on the information about task points. Finally, a hybrid ant colony optimization-genetic algorithm (ACO-GA) is designed to solve the problem. The update mechanism of ACO is incorporated into the selection operation of GA. At the same time, the GA is improved and the defects that make GA easy to fall into local optimal and ACO have insufficient searching ability are solved. Simulation results demonstrate that the ACO-GA algorithm can obtain reasonable solutions for MPP, and the search capability of the algorithm is enhanced, presenting significant advantages over the original GA and ACO.

Hybrid genetic ant colony optimization algorithm for full-coverage path planning of gardening pruning robots

Hybrid genetic ant colony optimization algorithm for full-coverage path planning of gardening pruning robots

Path Planning Method in Taigu County Based on the Hybrid Ant Colony Optimization-Genetic Algorithm in the Context of COVID-19

<p>During the period of COVID-19, there is a mixture of areas that are susceptible to COVID-19 infection and areas that are not susceptible to COVID-19 infection in cities. Blind wandering is often accompanied by the risk of infection. Hence, in order to improve the safety of people’s travel, this paper uses the hybrid Ant Colony Optimization-Genetic Algorithm (ACO-GA) to plan the central path of Taigu County. The volatilization coefficient of pheromone in Ant Colony Optimization (ACO) is changed dynamically. Pheromones in high-risk areas that are susceptible to epidemic infection are more volatile, while pheromones in low-risk areas that are less suscep-tible to epidemic infection are less volatile. Adjust the selection of “gene” mutation in Genetic Algorithm (GA). Vulner-able areas should be closed off to cut off the source of infection. As long as the shortest route which is of lower risk is formulated, people should stay away from high-risk areas that are susceptible to infection as much as possible to reduce the spread of COVID-19 and ensure the safety of people’s lives. The path under the influence of the COVID-19 is predicted and analyzed in the form of a simulation. The experimental results show that the algorithm can help to effectively avoid areas susceptible to the COVID-19 and reduce the risk of people getting sick.</p> <p> </p>

A hybrid of ant colony optimization, genetic algorithm and flux balance analysis for optimization of succinic acid production in Escherichia coli

Succinic acid, also known as dicarboxylic acid, is one of the biochemical products chemically produced from Escherichia coli (E. coli) metabolism. However, by using conventional methods succinic acid cannot be produced sufficiently and it is costly. Hence, there is a lot of ongoing research on E. coli by using in silico methods. Researchers build computational models of E. coli to analyze and modify their metabolic network. This paper proposes a hybrid of ant colony optimization–genetic algorithm–flux balance analysis (ACOGAFBA) in enhancing the succinic acid production of E. coli by identifying genes to be knocked out. Ant colony optimization (ACO) is a swarm intelligent optimization that is inspired based on the natural foraging behavior of ant colony. Local search technique like genetic algorithm (GA) is applied to solve optimization and search problem by approximation. Flux balance analysis (FBA) is used for fitness calculation after gene knockout. FBA identifies a point (fitness) in flux space by using quadratic programming, which is closest to the wild type point. ACOGAFBA produced three sets of gene knockout lists. The dataset [Formula: see text]JR904 is used in this paper. The results show that ACOGAFBA can identify the set of knockout genes to improve succinic acid production in E. coli.

Protection Strategy Selection Model Based on Genetic Ant Colony Optimization Algorithm

Industrial control systems (ICS) are facing an increasing number of sophisticated and damaging multi-step attacks. The complexity of multi-step attacks makes it difficult for security protection personnel to effectively determine the target attack path. In addition, most of the current protection models responding to multi-step attacks have not deeply studied the protection strategy selection method in the case of limited budget. Aiming at the above problems, we propose a protection strategy selection model based on the Genetic Ant Colony Optimization Algorithm. The model firstly evaluates the risk of ICS through the Bayesian attack graph; next, the target attack path is predicted from multiple angles through the maximum probability attack path and the maximum risk attack path; and finally, the Genetic Ant Colony Optimization Algorithm is used to select the most beneficial protection strategy set for the target attack path under limited budget. Compared with the Genetic Algorithm and Ant Colony Optimization Algorithm, the Genetic Ant Colony Optimization Algorithm proposed in this paper can handle the local optimal problem well. Simulation experiments verify the feasibility and effectiveness of our proposed model.

Optimized Hybridization of Ant Colony Optimization and Genetic Algorithm (HACOGA) Based Interpretable Intuitive and Correlated-Contours Fuzzy Neural Network Classifier for Abalone

Classification generally assigns objects to enormous predefined categories and it is pervasive crisis that covers various application. Preparing the data for Classification and Prediction is the major problem in classification. In order to rectify this issue, an approximate function is proposed using Interpretable intuitive and Correlated-contours Fuzzy Neural Network (IC-FNN). For acquiring cor- related fuzzy rules and non-separable rules that comes under proper optimization problem. The extracted fuzzy rule’s parameter was fine-tuned sourced on hierarchical Levenberg Marquardt (LM) learning method for enhancing performance. But here parameters of fuzzy rules aren’t tuned per- fectly. Hybridization of Ant Colony Optimization Genetic Algorithm (HACOGA) is proposed here to rectify these issues. It tunes the parameters of the extracted fuzzy rules. Hybridization is enforced to certain factors and ACO and GA variables that share same characteristics in the computation. Experimental results shows that proposed HACOGA assist in enhancing the performance of FNN with recall, precision, accuracy and F -measure for the Abalone age prediction dataset.

A Hybrid Method Based on Genetic Algorithm and Ant Colony System for Traffic Routing Optimization

TThis paper presents a hybrid method that combines the genetic algorithm (GA) and the ant colony system algorithm (ACS), namely GACS, to solve the traffic routing problem. In the proposed framework, we use the genetic algorithm to optimize the ACS parameters in order to attain the best trips and travelling time through several novel functions to help ants to update the global and local pheromones. The GACS framework is implemented using the VANETsim package and the real city maps from the open street map project. The experimental results show that our framework achieves a considerably higher performance than A-Star and the classical ACS algorithms in terms of the length of the global best path and the time for trips. Moreover, the GACS framework is also efficient in solving the congestion problem by online monitoring the conditions of traffic light systems.
 KeywordsTraffic routing; Ant colony system; Genetic algorithm; VANET simulator.
 References
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Data Aggregation Scheme Using Multiple Mobile Agents in Wireless Sensor Network

Wireless Sensor Nodes (WSN) has restricted sensing, communication and computational capabilities, in addition, are mainly operated by means of batteries in a bad atmosphere with the non-replenish-able power sources. As Data aggregation (DA) has more significance in solving the chief limitations of utilizing WSNs, say, the restricted battery life of the powered sensors in addition to short-communication gamut of sensors, it becomes an active research domain today. Effectively gathering data has constantly been the principal significance in WSNs. Regarding the static sink, nodes next to the sink would encompass more loads for routing data, and consequently Mobile Agent (MA) has been commenced. At the moment, the MA could move itself to the sensor nodes (SN) for amassing the data. This MA has made the gathering and aggregation of data possible in a means that is suitable for instantaneous applications. This work proposes an effective DA Scheme in WSN that employs manifold MAs for aggregating data in addition to transferring it to the sink centred on Itinerary planning. This could well be attained by grouping the nodes in clusters as well as planning itineraries effectually amongst cluster heads (CHs) alone. In the proposed DA scheme, itinerary planning is performed utilizing Hybrid Ant Colony Optimization-Genetic Algorithm (ACO-GA). Ultimately, the sink sends the MAs for amassing data as of the CH. Simulation outcome confirms clearly that the proposed work shows high-level performance than the other traditional techniques.

Multi-Objective Task Scheduling Using Hybrid Genetic-Ant Colony Optimization Algorithm in Cloud Environment

Task allocation within the cloud computing environment is a nondeterministic polynomial time class problem that is laborious to get the best solution. It is an important issue in the cloud computing setting. The usage of cloud based applications and cloud users are increasing tremendously. In order to handle the massive cloud user’s requests, effective multi-objective Hybrid Genetic Algorithm–Ant Colony Optimization (HGA–ACO) based task allocation technique is proposed in this paper. Utility based scheduler identifies the task order and suitable resources to be scheduled. The proposed HGA–ACO considers the utility based scheduler output and finds the best task allocation method based on response time, completion time and throughput. The HGA–ACO algorithm combines Genetic and Ant Colony Optimization algorithms together. Genetic algorithm (GA) initializes the effective pheromone for ant colony optimization (ACO). ACO is used to enhance the GA solutions for crossover operation of GA. The experimental results show that the proposed framework has better performance in task allocation and ensuring quality of service parameters.

Multi-objective four dimensional imprecise TSP solved with a hybrid multi-objective ant colony optimization-genetic algorithm with diversity

Multi-objective four dimensional imprecise TSP solved with a hybrid multi-objective ant colony optimization-genetic algorithm with diversity

SyntcRec: a Syntactic Recommender System Based on Improved Feature Selection Technique in Large Scholarly Data

In recent years, the amount of academic literature has been increasing, which results in information overloading. Another result of this fast data progress is that, one can feel lost in the variety of literature and realize the difficulties to yield decisions. To solve these issues, it is needed to filter, rank and competently deliver relevant material to several Internet operators. Recommendation schemes rectify this problem by providing users with personalized content and services. This paper presents a novel recommender system called syntactic Recommender, which employs improved feature selection techniques, a combination of ACO-GA (Ant Colony Optimization-Genetic Algorithm) algorithms aimed at feature selection in order to raise the accuracy of recommendations in recommender systems. The paper focuses on two key concepts, the first is performing and giving more exact object predictions to the operator and the second is handling a huge volume of data. The aim of this paper is to offer recommendation outcomes based on the users requests with accuracy and competency. The proposed ACO-GA feature selection algorithm and the Cosine Similarity technique increase accuracy of the Recommender system up to 92%.

A hybrid genetic-ant colony optimization algorithm for the word sense disambiguation problem

Word sense disambiguation (WSD) is a natural language processing problem that occurs at the semantic level. It consists of determining the sense of a polysemous word that is suitable in a particular context. WSD has been addressed using several approaches, including metaheuristic algorithms. We propose hybrid algorithms for WSD that consist of a self-adaptive genetic algorithm (SAGA) and variants of ant colony optimization (ACO) algorithms: max-min ant system (MMAS) and ant colony system (ACS). SAGA is used to automatically tune the parameters of MMAS and ACS. The ACO algorithms are adapted based on a combination of semantic relatedness between sequences of senses corresponding to the context words and semantic relatedness between the sense of a target word and the sense of a context word. We evaluated the performance of the two ACO algorithms (MMASWSD and ACSWSD) and their hybridization with SAGA (GMMASWSD and GACSWSD) on fine-grained and coarse-grained corpora, and compared them with the best-performing algorithms. The empirical results indicate that GMMASWSD outperformed the other variants and all of the rival algorithms on the fine-grained corpora. However, GMMASWSD did not achieve the best performance on the coarse-grained corpus, even though its performance was close to that of the best algorithm.

Automated screening tool for dry and wet age-related macular degeneration (ARMD) using pyramid of histogram of oriented gradients (PHOG) and nonlinear features

Aging is the prime cause of age-related macular degeneration (ARMD). There are primarily two types of ARMD: (i) dry, and (ii) wet. The dry ARMD is the common form of ARMD. It typically starts with the formation of small pale yellowish deposits called drusen under the retina, causing atrophy at the macula, which is known as age-related macular degeneration. This, in turn, affects the central vision of a person. The wet ARMD is caused due to the abnormal growth of blood vessels under the retina. These vessels are known as the choroidal neovascular membrane break through the retina and bleed. They finally lead to either scarring at the macula or atrophic changes leading to severe visual impairment. Hence, the wet ARMD progresses faster and leads to an irreversible loss of sight. Therefore, it is advisable to go for routine eye screening, especially for elderly subjects. Although the ARMD cannot be fully cured, an accurate early detection can impede the progression of vision loss. However, manual diagnosis of ARMD is difficult and subjective. Thus, a computer-aided diagnosis (CAD) system can be utilized to automatically screen the eyes and give an accurate diagnosis of the type of ARMD. In this study, we propose a novel technique to identify normal, dry, and wet ARMD. A total of 945 fundus images (404 normal, 517 dry ARMD, and 24 wet ARMD) are used in this proposed framework. The Pyramid of Histograms of Orientation Gradients (PHOG) technique is implemented in this work to capture the subtle changes in the pixels of fundus images. Various nonlinear features are extracted from the PHOG descriptor. To balance the number of images in three classes, an adaptive synthetic sampling (ADASYN) approach is used. Two feature selection techniques namely ant colony optimization genetic algorithm (ACO-GA) and particle swarm optimization (PSO) are used to select the best performing method. The selected features are subjected to analysis of variance (ANOVA) to determine highly significant features for classification. The proposed system has achieved a maximum accuracy of 85.1%, sensitivity of 87.2%, and specificity of 80% using nine features selected by PSO feature selection method with support vector machine (SVM) classifier. It has obtained a maximum accuracy of 83.3%, sensitivity of 82.6%, and specificity of 84.8% using ten features selected by ACO-GA feature selection method with SVM classifier. The proposed CAD system yielded promising performance and thus, can be used as a practical adjunct ARMD screening tool in the clinical setting.

A Novel Interval Integer Genetic Algorithm Used for Simultaneously Selecting Wavelengths and Pre-processing Methods

Abstract Because the primary coding version for chromosome was adopted in previous genetic algorithm (GA), a large number of inexplicable individuals were created in this procedure and the priority of pre-processing methods could not be optimized when GA was used for simultaneously selecting wavelength and pre-processing treatments. To solve the problem, a novel interval integer genetic algorithm (NIGA) was presented in this work. After validating by a group of synthesizing data and two groups of real near spectra data, NIGA was better than anyone of the other usual methods such as partial least squares regression (PLSR), interval partial least squares regression (iPLS) and ant colony optimization genetic algorithm sample selection (ACOGASS). During its work, common pre-processing methods were classified into four categories, e.g. smoothing, derivative, correction and standardization. The parameters of these methods were directly encoded as NIGA chromosomes for auto-adjustment at maximum extent. On the other hand, partitioning full spectrum was used for reducing the random noise and computational complexity, extracting the available information to improve the ability of the model. Furthermore, this method can also work in different situations and demands with the other non-linear models.

A hybrid Genetic-Ant Colony Optimization Algorithm for the Optimal Path Selection

AbstractThe shortest path problem lies at the heart of network flows that seeks for the paths with minimum cost from source node to sink node in networks. This paper presents a hybrid genetic-ant colony optimization algorithmic approach to the optimal path selection problem. First, some existing solutions for the optimal path selection problem are analyzed, and some shortages and flaws are pointed out. Second, the data organization method for road network based on the graph theory is proposed. Furthermore, the optimal path selection algorithm integrated of sinusoidal probability transfer rules, pheromone update strategy and dual population is presented. Finally, the experimental results show that the proposed algorithm speeds up the convergence rate and improves the efficiency.

A genetic ant colony optimization based algorithm for solid multiple travelling salesmen problem in fuzzy rough environment

In this paper, a genetic-ant colony optimization algorithm has been presented to solve a solid multiple Travelling Salesmen Problem (mTSP) in fuzzy rough environment. In solid mTSP, a set of nodes (locations/cities) are given, and each of the cities must be visited exactly once by the salesmen such that all of them start and finish at a depot using different conveyance facility. A solid mTSP is an extension of mTSP where the travellers use different conveyance facilities for travelling from one city to another. To solve an mTSP, a hybrid algorithm has been developed based on the concept of two algorithms, namely genetic algorithm (GA) and ant colony optimization (ACO) based algorithm. Each salesman selects his/her route using ACO and the routes of different salesmen (to construct a complete solution) are controlled by the GA. Here, a set of simple ACO characteristics have further been modified by incorporating a special feature namely ‘refinement’. In this paper, we have utilized cyclic crossover and two-point’s mutation in the proposed algorithm to solve the problem. The travelling cost is considered as imprecise in nature (fuzzy-rough) and is reduced to its approximate crisp using fuzzy-rough expectation. Computational results with different data sets are presented and some sensitivity analysis has also been made.

Energy efficient multiconstrained optimization using hybrid ACO and GA in MANET routing

Nodes in mobile ad hoc networks (MANET) suffer from limited battery power and bandwidth. Particularly for real time multimedia communications through MANET, metrics like residual node energy, bandwidth, and end-to-end delay have major impacts. In MANET, designing a dynamic routing algorithm to satisfy quality of service (QoS) requirements is a challenging task. Additionally, multiconstrained QoS routing aims to optimize multiple QoS metrics while providing required network resources and is an admittedly complex problem. It has been proved to be NP-complete when a combination of additive, concave, and multiplicative metrics are considered. Hence, this problem can be solved using metaheuristic methods like ant colony optimization (ACO) and the genetic algorithm (GA). The proposed energy-efficient ACO GA hybrid metaheuristic approach aims to utilize the benefits of both as a combined approach in order to reduce the routing complexities in the dynamic environment. After due investigation, it has been shown that the proposed hybrid approach improves the performance of MANET routing with satisfied QoS requirements.

Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines

Different from a large number of existing studies in the literature, this paper addresses two important issues in managing production lines, the problems of line balancing and model sequencing, concurrently. A novel hybrid agent-based ant colony optimization–genetic algorithm approach is developed for the solution of mixed model parallel two-sided assembly line balancing and sequencing problem. The existing agent-based ant colony optimization algorithm is enhanced with the integration of a new genetic algorithm-based model sequencing mechanism. The algorithm provides ants the opportunity of selecting a random behavior among ten heuristics commonly used in the line balancing domain. A numerical example is given to illustrate the solution building procedure of the algorithm and the evolution of the chromosomes. The performance of the developed algorithm is also assessed through test problems and analysis of their solutions through a statistical test, namely paired sample t test. In accordance with the test results, it is statistically proven that the integrated genetic algorithm-based model sequencing engine helps agent-based ant colony optimization algorithm robustly find significantly better quality solutions.

Gas ultrasonic flow rate measurement through genetic-ant colony optimization based on the ultrasonic pulse received signal model

For gas ultrasonic flowmeters, the signals received by ultrasonic sensors are susceptible to noise interference. If signals are mingled with noise, a large error in flow measurement can be caused by triggering mistakenly using the traditional double-threshold method. To solve this problem, genetic-ant colony optimization (GACO) based on the ultrasonic pulse received signal model is proposed. Furthermore, in consideration of the real-time performance of the flow measurement system, the improvement of processing only the first three cycles of the received signals rather than the whole signal is proposed. Simulation results show that the GACO algorithm has the best estimation accuracy and ant-noise ability compared with the genetic algorithm, ant colony optimization, double-threshold and enveloped zero-crossing. Local convergence doesn’t appear with the GACO algorithm until –10 dB. For the GACO algorithm, the converging accuracy and converging speed and the amount of computation are further improved when using the first three cycles (called GACO-3cycles). Experimental results involving actual received signals show that the accuracy of single-gas ultrasonic flow rate measurement can reach 0.5% with GACO-3 cycles, which is better than with the double-threshold method.

A Hybrid Approach Based on ACO and Ga for Multi Objective Mobile Robot Path Planning

In this study, we developed an Ant Colony Optimization (ACO) - Genetic Algorithm (GA) hybrid approach for solving the Multi objectives Optimization global path planning (MOPP) problem of mobile robot. The ACO optimization algorithm is used to find the sub-optimal collision free path which then used as initial population for GA. In the proposed modified genetic algorithms, specific genetic operator such as deletion operator is proposed, which is based on domain heuristic knowledge, to fit the optimum path planning for mobile robots. The objective of this study is improving GA performance for efficient and fast selection in generating the Multi objective optimal path for mobile robot navigation in static environment. First we used the proposed approach to evaluate its ability to solve single objective problem in length term as well as we compared it with traditional ACO and simple GA then we extended to solve Pareto optimality ideas based on three criteria: length, smoothness and security, and making it Multi objective Hybrid approach. The proposed approach is tested to generate the single and multi objective optimal collision free path. The simulation results show that the mobile robot travels successfully from one location to another and reaches its goal after avoiding all obstacles that are located in its way in all tested environment and indicate that the proposed approach is accurate and can find a set Pareto optimal solution efficiently in a single run.