Journal articles: 'ACO TECHNIQUE'

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Ratanavilisagul, Chiabwoot. "Modified Ant Colony Optimization with Route Elimination and Pheromone Reset for Multiple Pickup and Multiple Delivery Vehicle Routing Problem with Time Window." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 6 (November 20, 2022): 959–64. http://dx.doi.org/10.20965/jaciii.2022.p0959.

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The vehicle routing problem (VRP) has many applications in goods distribution and goods transportation. Today, many companies have requirements for VRP with multiple pickup and multiple delivery within due time. This problem is called multiple pickup and multiple delivery vehicle routing problem with time window (PDPTW). PDPTW has many constraints and ant colony optimization (ACO) has been used to solve it although ACO creates too many infeasible routes. Moreover, it often gets trapped in local optimum. To solve these problems, this paper proposed an improved ACO by using the route elimination technique and the pheromone reset technique. The ACO with route elimination technique, it has proven to solve the PDPTW problem with increased performance. The proposed technique was tested on datasets from the Li & Lim’s PDPTW benchmark problems and provided more satisfactory results compared to other ACO techniques.

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KATANGUR, AJAY K., SOMASHEKER AKKALADEVI, YI PAN, and MARTIN D. FRASER. "ROUTING IN OPTICAL MULTISTAGE NETWORKS WITH LIMITED CROSSTALK USING ANT COLONY OPTIMIZATION." International Journal of Foundations of Computer Science 16, no. 02 (April 2005): 301–20. http://dx.doi.org/10.1142/s0129054105003005.

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Ant Colony Optimization (ACO) techniques can be successfully implemented to solve many combinatorial optimization problems. After the traveling salesman problem was successfully solved using the ACO technique, other researchers have concentrated on solving other problems like the quadratic assignment and the job-shop scheduling problems using the same technique. In this paper we use the ACO technique to route messages through an N × N Optical Multistage Interconnection Network (OMIN) allowing upto ' C ' limited crosstalk's (conflicts between messages within a switch) where ' C ' is a technology driven parameter and is always less than log 2 N . Messages with switch conflicts satisfying the crosstalk constraint are allowed to pass in the same group, but if there is any link conflict, then messages have to be routed in a different group. The focus is to minimize the number of passes required for routing allowing upto ' C ' limited crosstalks in an N × N optical network. This routing problem is an NP-hard problem. In this paper we show how the ACO technique can be applied to the routing problem and compare the performance of the ACO technique to that of the degree-descending algorithm using simulation techniques. Finally the lower bound estimate on the minimum number of passes required is calculated and compared to the results obtained using the two algorithms discussed. The results obtained show that the ACO technique performs better than the degree-descending algorithm and is quite close to optimal algorithms to the problem.

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Hevin Rajesh, D. "Authentication Technique Using ACO in WSN." National Academy Science Letters 42, no. 1 (September 27, 2018): 19–23. http://dx.doi.org/10.1007/s40009-018-0667-5.

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Ahmed, Nisreen L. "ACO powered by Local Searches Algorithms for the Solution of TSP Problems." Qubahan Academic Journal 1, no. 1 (November 14, 2020): 1–10. http://dx.doi.org/10.48161/qaj.v1n1a5.

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Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

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Mhadhbi, Imene, Slim Ben Othman, and Slim Ben Saoud. "An Efficient Technique for Hardware/Software Partitioning Process in Codesign." Scientific Programming 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6382765.

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Codesign methodology deals with the problem of designing complex embedded systems, where automatic hardware/software partitioning is one key issue. The research efforts in this issue are focused on exploring new automatic partitioning methods which consider only binary or extended partitioning problems. The main contribution of this paper is to propose a hybrid FCMPSO partitioning technique, based on Fuzzy C-Means (FCM) and Particle Swarm Optimization (PSO) algorithms suitable for mapping embedded applications for both binary and multicores target architecture. Our FCMPSO optimization technique has been compared using different graphical models with a large number of instances. Performance analysis reveals that FCMPSO outperforms PSO algorithm as well as the Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO), and FCM standard metaheuristic based techniques and also hybrid solutions including PSO then GA, GA then SA, GA then ACO, ACO then SA, FCM then GA, FCM then SA, and finally ACO followed by FCM.

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Kumar, Sanjeev, and Preeti Singh. "Spectral Efficient Asymmetrically Clipped Hybrid FBMC for Visible Light Communication." International Journal of Optics 2021 (January 23, 2021): 1–8. http://dx.doi.org/10.1155/2021/8897928.

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Filter bank multicarrier (FBMC) modulation has shown sufficient potential for wireless communication. A hybrid optical FBMC technique is proposed to improve the spectral efficiency of a visible light communication (VLC) system. In this technique, a hybrid asymmetrically clipped optical offset quadrature amplitude modulation FBMC (HACO-OQAM-FBMC) modulation technique is used. Asymmetrically clipped optical FBMC (ACO-FBMC) is used for odd subcarriers, and pulse amplitude modulation-discrete multitone (PAM-DMT) is used for the even subcarriers. The proposed hybrid scheme uses an intensity modulation/direct detection (IM/DD) channel. It is shown that there is no interference on odd subcarriers using the proposed method and receiver demodulation is similar to that of ACO-FBMC receiver. However, clipping noise of ACO-FBMC falls on PAM-DMT subcarriers, which can be cancelled at receiver processing after estimation. The analytical performance of the proposed technique is compared using parameters, namely, bit error rate (BER), spectral efficiency, computational complexity, and peak to average power ratio (PAPR). It is found that HACO-OQAM-FBMC is more spectral efficient than ACO-FBMC and other OFDM-based techniques.

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Perumal, Boominathan, and Aramudhan M. "A Multi-Objective Fuzzy Ant Colony Optimization Algorithm for Virtual Machine Placement." International Journal of Fuzzy System Applications 5, no. 4 (October 2016): 165–91. http://dx.doi.org/10.4018/ijfsa.2016100108.

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In cloud computing, the most important challenge is to enforce proper utilization of physical resources. To accomplish the mentioned challenge, the cloud providers need to take care of optimal mapping of virtual machines to a set of physical machines. In this paper, the authors address the mapping problem as a multi-objective virtual machine placement problem (VMP) and propose to apply multi-objective fuzzy ant colony optimization (F-ACO) technique for optimal placing of virtual machines in the physical servers. VMP-F-ACO is a combination of fuzzy logic and ACO, where we use fuzzy transition probability rule to simulate the behaviour of the ants and the authors apply the same for virtual machine placement problem. The results of fuzzy ACO techniques are compared with five variants of classical ACO, three bin packing heuristics and two evolutionary algorithms. The results show that the fuzzy ACO techniques are better than the other optimization and heuristic techniques considered.

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Karthikeyini, S., R. Sagayaraj, N. Rajkumar, and Punitha Kumaresa Pillai. "Security in Medical Image Management Using Ant Colony Optimization." Information Technology and Control 52, no. 2 (July 15, 2023): 276–87. http://dx.doi.org/10.5755/j01.itc.52.2.32532.

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Data encryption before transmission is still a crucial step in lowering security concerns in cloud-based environments. Steganography and image encryption methods validate the security of confidential data while it is being transmitted over the Internet. The paper presents the Ant Colony Optimization with Encryption Curve cryptography-based steganography technique to enhance the security of medical image management (ACO-ECC-SMIM). The initial stage is to create the stego images for the used cover image, the ACO algorithm-based image steganography technique is used. The creation of the encryption process is a key focus of the suggested ACO-ECC-SMIM strategy. The encryption process is initially carried out using an ECC technique, or elliptic curve cryptography. To maximize PSNR, the ACO technique is employed to optimize the crucial production process in the ECC model. The host image is subjected to an integer wavelet transform, and the coefficients have been altered. To determine the ideal coefficients where to conceal the data, the ACO optimization technique is utilized. The decryption and sharing reconstruction procedures are then carried out on the receiver side to create the original images. In image 1, the ACO-ECC-SMIM model showed an improved PSNR of 59.37dB. Image 5 has an improved PSNR of 59.53dB thanks to the ACO-ECC-SMIM model. A large-scale experimental investigation was conducted to show the improved performance of the proposed PIOE-SMIM method, and the findings demonstrated the superiority of the ACO-ECC-SMIM model over other approaches.

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V Raj, Shruthi, and Hemanth S R. "Simulation of ACO Technique Using NS2 Simulator." International Journal of Engineering Trends and Technology 23, no. 8 (May 25, 2015): 403–6. http://dx.doi.org/10.14445/22315381/ijett-v23p277.

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Sornchai, Piyanuch, and Sermsiri Chanprame. "Genetic Transformation of Dendrobium 'Sonia Earsakul' with Antisense Carica papaya ACO1 Gene." Modern Applied Science 9, no. 12 (November 1, 2015): 125. http://dx.doi.org/10.5539/mas.v9n12p125.

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Dendrobium orchid is one of the major export cut flowers not only in Thailand but also for several tropical countries. However, the production of ethylene by their flowers causes a shorter vase life. Flowers that contained lower levels of ethylene usually exhibited delayed senescence and consequently prolonged vase life. The transfer of antisense ACC oxidase (ACO) gene into orchid, in theory, may leads to decreased ethylene production because this gene can down regulates the ethylene biosynthesis pathway. This study focuses on the transformation and the existence and expression of the antisense ACO1 gene from papaya, namely (CP-ACO1), which was transferred in to Dendrobium 'Sonia Earsakul'. The successful stable transformation event obtained and the existence of the transferred gene was determined using PCR, dot blot hybridization and Southern blot hybridization techniques. The results revealed that antisense CP-ACO1 and hygromycin phosphotransferase (hpt) gene existed in all transgenic lines confirmed by PCR technique. The genomic dot blot confirmed the incorporation of the transgene in transgenic plant genome. Southern blot hybridization revealed the existed of one to four sets of the gene in transgenic lines. The expression of antisense CP-ACO1 gene was analyzed through the level of ACO enzyme activity and ethylene production in transgenic orchid. All of the transgenic lines had lower ACO enzyme activity and lower ethylene production than that of the non-transgenic orchid plants.

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Obeng, Asare Y., and Samuel K. Opoku. "Complementary Architecture of E-Learning Path Personalization and Optimization." European Journal of Electrical Engineering and Computer Science 7, no. 3 (June 13, 2023): 41–48. http://dx.doi.org/10.24018/ejece.2023.7.3.533.

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Using computing algorithms to generate personalized learning resources to provide the needs and improve the capabilities, preferences, and academic performance of diverse learners is creating preferred learning environments. As more learning resources, strategies and techniques are frequently added to these e-learning systems, input data to personalize the learning path has been growing exponentially making swift responses to learner’s requests difficult. This study proposed a complementary learning path personalization architecture using ant colony (ACO) with nearest neighbour technique and genetic algorithm (GA) to extend the functions of the Spark framework purposely to develop a robust evolutionary computing algorithm. Experimental results indicate that complementing ACO, GA and Spark frameworks improved the generation of personalized learning resources and best-fitted-optimized learning paths. Spark-ACO took less computational time than standalone ACO. Combining ACO and GA improved the likelihood of an ant colony being trapped at a local optimum, and Spark-ACO-GA significantly enhanced the accuracy of the solutions.

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Linda, M. Mary, and N. Kesavan Nair. "Optimal design of multi-machine power system stabilizer using robust ant colony optimization technique." Transactions of the Institute of Measurement and Control 34, no. 7 (September 30, 2011): 829–40. http://dx.doi.org/10.1177/0142331211421520.

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In this paper, a multi-objective design of the multi-machine Power System Stabilizer (PSS) using Ant Colony Optimization (ACO) is proposed. The fine tuning of PSS parameters problem is converted to an optimization problem that is resolved by an ACO-based dominant metaheuristic technique. The strength of the proposed ACO-based PSS is tested on two different multi-machine power systems under diverse operating conditions. The outcomes of the proposed ACOPSS are compared with the Conventional PSS, Genetic Local Search-based PSS, Chaotic Optimization-based PSS and Particle Swarm Optimization-based PSS (PSOPSS). From the simulation results it can be inferred that the ACOPSS reduces the settling time and maximum overshoot more than the other techniques.

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WANG, Chen, Hamzah ABDUL-RAHMAN, and Wei See CH’NG. "ANT COLONY OPTIMIZATION (ACO) IN SCHEDULING OVERLAPPING ARCHITECTURAL DESIGN ACTIVITIES." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 22, no. 6 (June 8, 2016): 780–91. http://dx.doi.org/10.3846/13923730.2014.914100.

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The increasing complexity of architectural design works refers to the need for high quality design solutions for overlapping activities through a shorter time period. Conventional network analysis techniques such as CPM could only represent sequential processes yet it is unable to handle a process which contains iterations so that it leads to the occurrence of unwanted omission of logic or information links between design activities. Ant Colony Optimization emerged as an efficient metaheuristic technique for solving computational problems in finding good paths through graphs. This research aims to develop an ACO based Design Activity Scheduling model (ACO-DAS) for the scheduling of overlapping architectural design activities and to test the workability of ACO-DAS through a hypothetical run. From the computational results of both CPM and ACO methods, the determination of critical path using ACO-DAS model resulted in a design duration at 50 while that for CPM was as long as 78. The durations of architectural design activities have been significantly shortened by ACO-DAS. ACO-DAS results in shorter design completion time thus it deems more advanced than CPM.

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Liu, Yali, Fuling Hao, Rui Meng, and Weirong Xu. "Construction of Antisense ACC Oxidase Gene of Lilium and its Genetic Transformation." HortScience 41, no. 4 (July 2006): 1006D—1006. http://dx.doi.org/10.21273/hortsci.41.4.1006d.

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The enzyme ACC oxidase (ACO), encoded by a small multigene family in many plants, catalyzes the terminal step in the ethylene biosynthesis pathway. In this research, based on the total RNA isolated from the flowers of Asia hybrids `Pollyanna' and Oriental hybrids `Sorbonne', we obtained two cDNA fragments of ACO genes (Genbank accession DQ062133 and DQ062134) by RT-PCR technique. The two cDNA fragments were reversely inserted into plant expression vector pWR306 respectively, and constructed two antisense ACO gene expression binary vectors harboring hygromycin phosphotransferase (hptII), glucuronidase (uid A), and a green fluorescent protein (GFP) gene in the T-DNA region. We have developed a system to produce transgenic plants in LiLium via Agrobacterium tumefaciens-mediated transformation of calli. Transformants were subjected to GFP expression analysis, PCR assay, and Southern hybridization to confirm gene integration.

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Yaich, Mohamed, and Moez Ghariani. "A Novel Technique for Tuning PI -controller In Switched Reluctance Motor Drive for Transportation Systems." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4272. http://dx.doi.org/10.11591/ijece.v8i6.pp4272-4281.

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This paper presents, an optimal basic speed controller for switched reluctance motor (SRM) based on ant colony optimization (ACO) with the presence of good accuracies and performances. The control mechanism consists of proportional-integral (PI) speed controller in the outer loop and hysteresis current controller in the inner loop for the three phases, 6/4 switched reluctance motor. Because of nonlinear characteristics of a SRM, ACO algorithm is employed to tune coefficients of PI speed controller by minimizing the time domain objective function. Simulations of ACO based control of SRM are carried out using MATLAB /SIMULINK software. The behavior of the proposed ACO has been estimated with the classical Ziegler- Nichols (ZN) method in order to prove the proposed approach is able to improve the parameters of PI chosen by ZN method. Simulations results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on classical Ziegler-Nichols method.

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Luong, Van Nghia, Vijender Kumar Solanki, and Nguyen Ha Huy Cuong. "Fragmentation in Distributed Database Design Based on Ant Colony Optimization Technique." International Journal of Information Retrieval Research 9, no. 2 (April 2019): 28–37. http://dx.doi.org/10.4018/ijirr.2019040103.

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Distributed database design solutions depend heavily on the exploitation of input data sources by using clustering techniques in data mining. A new approach of biomimetic computation systems such as ant colony optimization (ACO) for this solution is of interest to informatics experts. Using ACO techniques for this solution has the advantages such as faster algorithms thanks to the randomness of ant colony behavior. The use of random numbers based on heuristic information to pickup (drop) points will facilitate the flexible search on a large data space, so that it provides us with a better answer. In this article, the authors present ACO algorithms application solutions to clustering techniques for the problem of vertical fragmentation of distributed data.

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Shankar, K., and E. Perumal. "ANT COLONY OPTIMIZATION BASED OPTIMAL STEGANOGRAPHY TECHNIQUE FOR SECRET IMAGE SHARING SCHEME." Advances in Mathematics: Scientific Journal 10, no. 1 (January 21, 2021): 453–61. http://dx.doi.org/10.37418/amsj.10.1.45.

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Data hiding earlier to transmission remains as an essential process for reducing the security issues in the cloud based environment. Image encryption and steganography techniques verify the safety of secret data during the transmission over the Internet. This paper presents a new multiple secret share creation (SSC) with ant colony optimization (ACO) based image steganography (SSC-ACO) technique to achieve security over image transmission. Initially, SSC algorithm is applied to generate a set of different shares for the applied image. Then, the ACO algorithm based image steganography technique is employed to generate the stego images for the applied cover and share images. The utilization of image steganography technique comprises a set of shares into the cover image to secure the details of the individual shares. The experimental validation of the projected model is tested using diabetic retinopathy (DR) images and the results are examined interms of peak signal to noise ratio (PSNR). The obtained PSNR values ensured the effective performance of the presented model on all the employed test images.

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Kiran, Aisha, Faiza Amin, Muneeb A. Lone, Imran Alam Moheet, Maham M. Lone, Syed Mahmood, and Muhammad Sohail Zafar. "Influence of Processing Techniques on Microhardness and Impact Strength of Conventional and Reinforced Heat Cured Acrylic Resin: A Comparative Study." Materiale Plastice 58, no. 3 (October 5, 2021): 239–46. http://dx.doi.org/10.37358/mp.21.3.5521.

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This study determined and compared the influences of various processing techniques including air circulating oven (ACO), dry heat oven (DHO) and water bath (WB) on the impact strength (IS) and microhardness (HV) of the conventional heat cure acrylic resin (CHCAR) and rubber reinforced heat cure acrylic resin (RRHCAR). Samples were fabricated using CHCAR (control Group A; n=114) and RRHCAR (experimental Group B; n=114). Group A and B were further divided into subgroups according to processing techniques: ACO, DHO and WB (n=38 each) for both testing variables microhardness and impact strength (n=19 each). Charpy testing machine and Vickers microhardness tester were utilized. Analysis of variance was applied to determine the presence of significant differences among processing techniques while P-value ≤ 0.05 was considered as significant. Water bath (P-value [0.001) and DHO technique (p-value [0.001) showed significant differences between both groups� impact strength and microhardness. Microhardness of group A and B showed a significant difference (p-value 0.002) when processed by ACO. Impact strength and micro hardness were improved in RRHCAR compared to CHCAR processed by ACO and DHO in comparison to WB technique. Rubber reinforced heat cure acrylic resin revealed improvement in the impact strength and microhardness. The air circulating oven exhibited highest microhardness in both testing materials. Dry heat oven showed improved values of impact strength in conventional heat cure acrylic resin.

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Rajaković, Nikola. "Implementacija algoritma kolonije mrava za optimalnu rekonfiguraciju distributivne mreže." Energija, ekonomija, ekologija 22, no. 1-2 (2020): 50–57. http://dx.doi.org/10.46793/eee20-1-2.050m.

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The paper analyzes the possibility of application of Ant Colony Optimization (ACO) algorithm for reconfiguration of distribution network with the aim of active power minimiza- tion. ACO is a population-based meta-heuristic technique used to solve different combinatorial optimization problems. The search technique is inspired by the behaviour of ant colonies in nature. The efficiency of the proposed algorithm is demonstrated on IEEE 33-bus and IEEE 69-bus test distribution systems. Also, the results obtained by using ACO algorithm are compared to the results achievable by other heuristic and meta-heuristic algorithms.

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Manaskasemsak, Bundit, and Arnon Rungsawang. "Web spam detection using trust and distrust-based ant colony optimization learning." International Journal of Web Information Systems 11, no. 2 (June 15, 2015): 142–61. http://dx.doi.org/10.1108/ijwis-12-2014-0047.

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Purpose – This paper aims to present a machine learning approach for solving the problem of Web spam detection. Based on an adoption of the ant colony optimization (ACO), three algorithms are proposed to construct rule-based classifiers to distinguish between non-spam and spam hosts. Moreover, the paper also proposes an adaptive learning technique to enhance the spam detection performance. Design/methodology/approach – The Trust-ACO algorithm is designed to let an ant start from a non-spam seed, and afterwards, decide to walk through paths in the host graph. Trails (i.e. trust paths) discovered by ants are then interpreted and compiled to non-spam classification rules. Similarly, the Distrust-ACO algorithm is designed to generate spam classification ones. The last Combine-ACO algorithm aims to accumulate rules given from the former algorithms. Moreover, an adaptive learning technique is introduced to let ants walk with longer (or shorter) steps by rewarding them when they find desirable paths or penalizing them otherwise. Findings – Experiments are conducted on two publicly available WEBSPAM-UK2006 and WEBSPAM-UK2007 datasets. The results show that the proposed algorithms outperform well-known rule-based classification baselines. Especially, the proposed adaptive learning technique helps improving the AUC scores up to 0.899 and 0.784 on the former and the latter datasets, respectively. Originality/value – To the best of our knowledge, this is the first comprehensive study that adopts the ACO learning approach to solve the problem of Web spam detection. In addition, we have improved the traditional ACO by using the adaptive learning technique.

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R, Shanmugavalli, and Subashini P. "Investigation of Ant Colony Optimization Algorithm for Efficient Energy Utilization in Wireless Sensor Network." International journal of Computer Networks & Communications 15, no. 04 (July 27, 2023): 73–92. http://dx.doi.org/10.5121/ijcnc.2023.15405.

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Maintaining the energy conservation is considered as an important approach to increase the lifetime of WSN. In fact, an energy reduction mechanism is considered as the main concept to enhance the lifespan of the network. In this paper, the performance analysis/evaluation of optimization technique, specifically, Ant Colony Optimization (ACO) and modified ACO (m-ACO) in the routing method are investigated. This network analysis is done by 100 iterations and differentiated with 50, 75 and 100 numbers of nodes. Finally, experimental results illustrate that the performance of m-ACO algorithm obtained the obvious performance, which is comparatively better than ACO algorithm, because it improves the routing efficiency by pheromone evaporation control and energy threshold value. It demonstrates that m-ACO algorithm gives better results than ACO in terms of throughput (1.41%), transmission delay (1.43%), packet delivery ratio (1.41%), energy consumption (2.05%), and the packet loss (9.70%). The convergence rate is analysed for ACO and m-ACO algorithms with respect to 100 number of iterations for WSNs.

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Chandramohan, B. "Restructured Ant Colony Optimization Routing Protocol for Next Generation Network." International Journal of Computers Communications & Control 10, no. 4 (June 22, 2015): 492. http://dx.doi.org/10.15837/ijccc.2015.4.665.

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Wireless network is a major research domain in the past few decades. Wireless network evolves in many forms like cellular communication, ad hoc network, vehicular network, mesh network and sensor network. Next generation network is a recent cellular communication which provides heterogeneous connectivity on cellular communication. The routing in next generation wireless networks is an important research issue which requires many constraints than wired networks. Hence, Ant Colony Optimization (ACO) is applied in this paper for routing in heterogeneous next generation wireless network. The ACO is a swarm intelligence technique which applied for many engineering applications. ACO is an optimal technique for routing and travelling salesman problem. This paper proposed Restructured ACO which contains additional data structures for reducing packet loss and latency. Therefore, the proposed RACO provides higher throughput.

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Yaich, Mohamed, Youssef Dhieb, Mounir Bouzguenda, and Moez Ghariani. "Metaheuristic Optimization Algorithm of MPPT Controller for PV system application." E3S Web of Conferences 336 (2022): 00036. http://dx.doi.org/10.1051/e3sconf/202233600036.

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The Maximum Power Point Tracking controller (MPPT) is a key element in Photovoltaic systems (PV) since it allows maintaining the PV operating point at its maximum under different temperatures and sunlight irradiations. Metaheuristic algorithms such as the ant colony optimization (ACO) are adopted and have shown their superiority to many other techniques. The perturb and observe (P&O) algorithm is a simple and efﬁcient technique, and is one of the most commonly employed MPPT schemes for PV power generation systems. Which executed by manipulating direct duty ratio of the boost converter. P&O method miserably fails to recognize various MPPT controllers. This paper proposes ACO technique to solve real-life problems.

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Kulkarni, Jyoti S., and Rajankumar S. Bichkar. "A Novel Approach of Image Fusion Techniques using Ant Colony Optimization." Regular issue 10, no. 8 (June 30, 2021): 92–97. http://dx.doi.org/10.35940/ijitee.h9241.0610821.

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Ant Colony Optimization (ACO) is a relatively high approach for finding a relatively strong solution to the problem of optimization. The ACO based image fusion technique is proposed. The objective function and distance matrix is designed for image fusion. ACO is used to fuse input images at the feature-level by learning the fusion parameters. It is used to select the fusion parameters according to the user-defined cost functions. This algorithm transforms the results into the initial pheromone distribution and seeks the optimal solution by using the features. As to relevant parameters for the ACO, three parameters (α, β, ρ ) have the greatest impact on convergence. If the values of α, β are appropriately increased, convergence can speed up. But if the gap between these two is too large, the precision of convergence will be negatively affected. Since the ACO is a random search algorithm, its computation speed is relatively slow.

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Alameri, Ibrahim Ahmed, and Jitka Komarkova. "Performance and statistical analysis of ant colony route in mobile ad-hoc networks." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (June 1, 2022): 2818. http://dx.doi.org/10.11591/ijece.v12i3.pp2818-2828.

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<div class="WordSection1">Research on mobile ad-hoc networks (MANETs) is increasing in popularity due to its rapid, budget-friendly, and easily altered implementation, and relevance to emergencies such as forest firefighting and health care provisioning. The main concerns that ad-hoc networks face is dynamic topology, energy usage, packet drop rate, and throughput. Routing protocol selection is a critical point to surmount alterations in topology and maintain quality in MANET networks. The effectiveness of any network can be vastly enhanced with a well-designed routing protocol. In recent decades, standard MANET protocols have not been able to keep pace with growing demands for MANET applications. The current study investigates and contrasts ant colony optimization (ACO) with various routing protocols. This paper compares ad-hoc on-demand multi-path distance vector (AOMDV), dynamic source routing protocol (DSR), ad-hoc on-demand distance vector routing (AODV), and AntHocNet protocols regarding the quality of service (QoS) and statistical analysis. The current research aims to study the behavior of the state-of-the-art MANET protocols with the ACO technique. The ACO technique is a hybrid technique, integrating a reactive route maintaining technique with a proactive method. The reason and motivation for including the ACO algorithm in the current study is to improve by using optimization algorithms proved in other domains. The ACO algorithm appears to have substantial use in large-scale MANET simulation.</div>

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Saif Alghawli, Abed, and Ahmed I. Taloba. "An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders." Computational Intelligence and Neuroscience 2022 (June 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/1332664.

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Bipolar disorder is marked by mood swings that alternate between mania and depression. The stages of bipolar disorder (BD), as one of the most common mental conditions, are often misdiagnosed as major depressive disorder (MDD), resulting in ineffective treatment and a poor prognosis. As a result, distinguishing MDD from BD at an earlier phase of the disease may aid in more efficient and targeted treatments. In this research, an enhanced ACO (IACO) technique biologically inspired by and following the required ant colony optimization (ACO) was utilized to minimize the number of features by deleting unrelated or redundant feature data. To distinguish MDD and BD individuals, the selected features were loaded into a support vector machine (SVM), a sophisticated mathematical technique for classification process, regression, functional estimates, and modeling operations. In respect of classifications efficiency and frequency of features extracted, the performance of the IACO method was linked to that of regular ACO, particle swarm optimization (PSO), and genetic algorithm (GA) techniques. The validation was performed using a nested cross-validation (CV) approach to produce nearly reliable estimates of classification error.

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Iqbal, Md Shahid, Md Monirul Kabir, Amit Shaha Surja, and Abdur Rouf. "Solar Radiation Prediction using Ant Colony Optimization and Artificial Neural Network." European Journal of Engineering and Technology Research 7, no. 2 (April 8, 2022): 99–111. http://dx.doi.org/10.24018/ejeng.2022.7.2.2786.

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This paper proposes a Solar Radiation Prediction Model employing Ant Colony Optimization (ACO) and Artificial Neural Network (ANN), named as SRPM. SRPM aims to incorporate the Feature Selection (FS) technique in the ANN training with the guidance of hybridizing ACO. The main reason behind using FS technique is, it can provide an improved solution from a particular problem by identifying the most salient features from the available feature set. In SRPM, the hybridizing ACO search technique utilizes the information gathered from the correlation among the features and the result of ANN training. To assist the ACO search, pheromone updating technique and measurement of heuristic information have been performed by two particular sets of rules. Thus, SRPM utilizes the benefits of using the wrapper and filter approaches in selecting the salient features during SRP task. The combination eventually creates an equipoise between exploration and exploitation of ants in the way of searching as well as strengthening the capability of global search of ACO for obtaining well qualified solution in SRPM. To evaluate the executive efficiency of SRPM, data samples were collected from BMD and NASA-SSE department. Exploratory outcomes show that SRP can select six utmost salient features by providing 99.74% and 99.78% averaged testing accuracies for BMD and NASA-SSE data samples that are composed of 12 and 15 original features, respectively. Furthermore, the proposed model successfully obtained 0.26% and 0.22% MAPE for BMD and NASA-SSE data samples, respectively with a high correlation of about 99.97% within the actual and predicted data.

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Dubowitz, Julia, Jonathan Hiller, and Bernhard Riedel. "Anesthetic technique and cancer surgery outcomes." Current Opinion in Anaesthesiology 34, no. 3 (April 8, 2021): 317–25. http://dx.doi.org/10.1097/aco.0000000000001002.

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Kavanagh, Trevor, and Donal J. Buggy. "Can anaesthetic technique effect postoperative outcome?" Current Opinion in Anaesthesiology 25, no. 2 (April 2012): 185–98. http://dx.doi.org/10.1097/aco.0b013e32834f6c4c.

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Alaidi, A. H., C. Soong Der, and Y. Weng Leong. "Increased Efficiency of the Artificial Bee Colony Algorithm Using the Pheromone Technique." Engineering, Technology & Applied Science Research 12, no. 6 (December 15, 2022): 9732–36. http://dx.doi.org/10.48084/etasr.5305.

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Artificial Bee Colony (ABC) is a powerful metaheuristic algorithm inspired by the behavior of a honey bee swarm. ABC suffers from poor exploitation and, in some cases, poor exploration. Ant Colony Optimization (ACO) is another metaheuristic algorithm that uses pheromones as a guide for an ant to find its way. This study used a pheromone technique from ACO on ABC to enhance its exploration and exploitation. The performance of the proposed method was verified through twenty instances from TSPLIB. The results were compared with the original ABC method and showed that the proposed method leverages the performance of ABC.

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Zou, Da Yong, and Yun Shan Hou. "Fast Passive Synthetic Array Parameter Estimation by Ant Colony Optimization." Advanced Materials Research 433-440 (January 2012): 4506–11. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4506.

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The Maximum Likelihood(ML) estimator for passive synthetic arrays incurs heavy computation when search for signal azimuth and frequency at the same time. To reduce its computational complexity, we introduced Ant Colony Optimization(ACO) to work with it. A new kind of ACO technique for continuous domain featured by Gauss kernel function is used to sample the ML spectrum, which is regarded as the fitness function in the process. The resulted estimator is called Ant Colony Optimization based ML (ACO-ML). Simulations show that ACO-ML not only reduces the computational complexity greatly but also maintains the excellent performance of the original ML estimator.

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Palak, Palak, and Preeti Gulia. "Ant Colony Optimization Based Test Case Selection for Component Based Software." International Journal of Engineering & Technology 7, no. 4 (September 26, 2018): 2743. http://dx.doi.org/10.14419/ijet.v7i4.17565.

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Reusability is one of the prime aspects of high quality software. Based on the concept of reusing the previous effort, Component based software engineering is a widely evolving software development paradigm that sets new challenges for testing team. The third party components need to be selected and assembled in development framework. Components interact with each other for various services and the interface between them can prove as the point of failure. As exhaustive testing of all interaction sequences is not possible, there is need for automated test case reduction and prioritization techniques to increase the efficiency of testing process. Ant Colony Optimization (ACO), a nature inspired optimization technique has wide range of applications in the field of software engineering. This paper presents an ACO based technique for test case selection for interaction testing of reusable software components.

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Bala, Anju, and Priti. "Hybrid ACO-PSO-GA-DE Algorithm for Big Data Classification." International Journal of Recent Technology and Engineering (IJRTE) 8, no. 2 (July 30, 2019): 703–8. http://dx.doi.org/10.35940/ijitee.b1708.078219.

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This paper designs a technique to classify big data efficiently. This work considers the processing of big data as an optimization problem due to the trade-off between accuracy and time and solves this optimization problem by using a meta-heuristic approach. The HAPGD (Hybrid ACO (Ant Colony Optimization), PSO (Particle Swarm Optimization), GA (Genetic Algorithm), and DE (Differential Evolution)) classification algorithm is designed by using the support vector machine (SVM) along with hybrid ACO-PSO-GA-DE algorithm that hybrids exploration capability of ACO with exploitation capability of PSO whose balance is maintained using modified GA. The GA has been modified by using the DE algorithm. The presented technique performs classification efficiently as shown in results on seven datasets using different analysis parameters due to balanced exploration and exploitation search with fast convergence.

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HIJAZI, SAMER L., and BALASUBRAMANIAM NATARAJAN. "NOVEL LOW-COMPLEXITY ANT COLONY BASED MULTIUSER DETECTOR FOR DIRECT SEQUENCE CODE DIVISION MULTIPLE ACCESS." International Journal of Computational Intelligence and Applications 05, no. 02 (June 2005): 201–14. http://dx.doi.org/10.1142/s146902680500157x.

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In this paper, we present a novel multiuser detection (MUD) technique based on ant colony optimisation (ACO), for synchronous direct sequence code division multiple access systems. ACO algorithms are based on the cooperative foraging strategy of real ants. While an optimal MUD design using an exhaustive search method is prohibitively complex, we show that the ACO-based MUD converges to the optimal bit-error-rate performance in relatively few iterations providing 95% savings in computational complexity. This reduction in complexity is retained even when considering users with unequal received powers.

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Kumar, Rajeev, and Dilip Kumar. "Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks." Journal of Sensors 2016 (2016): 1–19. http://dx.doi.org/10.1155/2016/5836913.

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Currently, wireless sensor networks (WSNs) are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC) algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO) is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO) and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP) hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i) selection of optimal number of subregions and further subregion parts, (ii) cluster head selection using ABC algorithm, and (iii) efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS). The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively.

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Kosolsombat, Somkiat, and Chiabwoot Ratanavilisagul. "Modified ant colony optimization with selecting and elimination customer and re-initialization for VRPTW." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3471–82. http://dx.doi.org/10.11591/eei.v11i6.3943.

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Vehicle routing problem with time windows (VRPTW) is a special kind of vehicle routing with adding time windows constraints and has a variety of applications in logistics. Many researchers have attacked the VRPTW by approximate solutions. Ant colony optimization (ACO) is a classical method to solve the VRPTW problem but the constraints of VRPTW are not used to consider customer selection. Most ACO-based optimization algorithms can suffer from the complexity of the VRPTW such as trapping in local optimum. In this paper, we present a novel ACO-based optimization method for VRPTW by using customer selection in order to decrease or solve the inefficiency of the customer selection of the ACO process. Moreover, we enhance performance searching of ACO in order to eliminate these small routes from the ACO process. Finally, we proposed the re-initialization technique in order to decrease or solve trapping in local optimum. Experiments conducted on fifty-six maps dataset have shown that the proposed method achieves encouraging performance compared to other ACOs.

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Alani, Sameer, Atheer Baseel, Mustafa Maad Hamdi, and Sami Abduljabbar Rashid. "A hybrid technique for single-source shortest path-based on A* algorithm and ant colony optimization." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 2 (June 1, 2020): 356. http://dx.doi.org/10.11591/ijai.v9.i2.pp356-363.

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In the single-source shortest path (SSSP) problem, the shortest paths from a source vertex v to all other vertices in a graph should be executed in the best way. A common algorithm to solve the (SSSP) is the A* and Ant colony optimization (ACO). However, the traditional A* is fast but not accurate because it doesn’t calculate all node's distance of the graph. Moreover, it is slow in path computation. In this paper, we propose a new technique that consists of a hybridizing of A* algorithm and ant colony optimization (ACO). This solution depends on applying the optimization on the best path. For justification, the proposed algorithm has been applied to the parking system as a case study to validate the proposed algorithm performance. First, A*algorithm generates the shortest path in fast time processing. ACO will optimize this path and output the best path. The result showed that the proposed solution provides an average decreasing time performance is 13.5%.

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HARA, Motoshi, Daisuke KAJINO, and Tadashi HORIUCHI. "ACO Algorithm with Divide-and-Conquer Technique for The TSP." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 24, no. 6 (2012): 1101–5. http://dx.doi.org/10.3156/jsoft.24.1101.

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Kumar Jha, Awadhesh. "Advanced ACO Metaheuristic for Travelling Salesman Problem: A Proposed Technique." International Journal for Research in Applied Science and Engineering Technology V, no. III (March 25, 2017): 536–39. http://dx.doi.org/10.22214/ijraset.2017.3098.

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Guha Neogi, Avirup, Singamreddy Mounika, Salagrama Kalyani, and S. A. Yogananda Sai. "A Comprehensive Study of Vehicle Routing Problem With Time Windows Using Ant Colony Optimization Techniques." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 80. http://dx.doi.org/10.14419/ijet.v7i2.32.13532.

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Ant Colony Optimization (ACO) is a nature-inspired swarm intelligence technique and a metaheuristic approach which is inspired by the foraging behavior of the real ants, where ants release pheromones to find the best and shortest route from their nest to the food source. ACO is being applied to various optimization problems till date and has been giving good quality results in the field. One such popular problem is known as Vehicle Routing Problem(VRP). Among many variants of VRP, this paper presents a comprehensive survey on VRP with Time Window constraints(VRPTW). The survey is presented in a chronological order discussing which of the variants of ACO is used in each paper followed by the advantages and limitations of the same.

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Hossain, Sayed Kaes Maruf, Sajia Afrin Ema, and Hansuk Sohn. "Rule-Based Classification Based on Ant Colony Optimization: A Comprehensive Review." Applied Computational Intelligence and Soft Computing 2022 (April 8, 2022): 1–17. http://dx.doi.org/10.1155/2022/2232000.

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The Ant Colony Optimization (ACO) algorithms have been well-studied by the Operations Research community for solving combinatorial optimization problems. A handful of researchers in the Data Science community have successfully implemented various ACO methodologies for rule-based classification. This family of ACO algorithms is referred to as AntMiner algorithms. Due to the flexibility of the framework, and the availability of alternative strategies at the modular level, a systematic review on the AntMiner algorithms can benefit the broader community of researchers and practitioners interested in highly interpretable classification techniques. In this paper, we provided a comprehensive review of each module of the AntMiner algorithms. Our motivation is to provide insight into the current practices and future research scope in the context of the rule-based classification. Our discussions address ACO methodologies, rule construction strategies, candidate selection metrics, rule quality evaluation functions, rule pruning strategies, methods to address continuous attributes, parameter selection, and experimental settings. This review also reports a summary of real-life implementations of the rule-based classifiers in diverse domains including medical, genetics, portfolio analysis, geographic information system (GIS), human-machine interaction (HMI), autonomous driving, ICT, quality, and reliability engineering. These implementations demonstrate the potential application domains that can be benefitted from the methodological contributions to the rule-based classification technique.

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Adeyemo, Ismail Adeyemi, Olabode Moses Ola, and Demilade Olujide Babajide. "Real-Time Harmonic Optimization in Multilevel Inverter Using Artificial Neural Network (ANN)." European Journal of Engineering and Technology Research 7, no. 5 (September 20, 2022): 12–17. http://dx.doi.org/10.24018/ejeng.2022.7.5.2720.

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Multilevel inverters (MLIs) are increasingly used in real time applications. Among several pulse width modulation (PMW) techniques currently deployed for the control of MLIs, selective harmonic elimination PWM (SHEPWM) technique arguably gives the best performance due to its direct harmonic mitigation capability. However, real time application of SHEPWM technique is presently infeasible due to the heavy computational cost involved in solving the transcendental nonlinear equations known as selective harmonic elimination (SHE) equations, which characterize the harmonics that are selected for elimination or mitigation. This paper presents a twostage approach to the online generation of switching angles that mitigate selected lower-order harmonics in multilevel inverters. The first stage involves an offline solution of SHE equations using ant colony optimisation (ACO). In the second stage, ACO computed results are used to train an artificial neural network (ANN) predictive model. The results obtained from the simulation of the proposed method in MATLAB/SIMULINK environment show that the method is highly efficient and accurate.

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Ibraheem, Ibraheem Kasim, and Fatin Hassan Ajeil. "Path Planning of an autonomous Mobile Robot using Swarm Based Optimization Techniques." Al-Khwarizmi Engineering Journal 12, no. 4 (December 18, 2017): 12–25. http://dx.doi.org/10.22153/kej.2016.08.002.

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This paper presents a meta-heuristic swarm based optimization technique for solving robot path planning. The natural activities of actual ants inspire which named Ant Colony Optimization. (ACO) has been proposed in this work to find the shortest and safest path for a mobile robot in different static environments with different complexities. A nonzero size for the mobile robot has been considered in the project by taking a tolerance around the obstacle to account for the actual size of the mobile robot. A new concept was added to standard Ant Colony Optimization (ACO) for further modifications. Simulations results, which carried out using MATLAB 2015(a) environment, prove that the suggested algorithm outperforms the standard version of ACO algorithm for the same problem with the same environmental conditions by providing the shortest path for multiple testing environments.

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Singh Kushwah, Virendra, Sandip K. Goyal, and Avinash Sharma. "Measuring Throughput for Fault Tolerant Based ACO Algorithm under Cloud Computing: A Comparison Study." International Journal of Engineering & Technology 7, no. 4.12 (October 4, 2018): 39. http://dx.doi.org/10.14419/ijet.v7i4.12.20989.

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Any technical problem can be main cause for any fault. Due to any fault, system would be suffered the work and enhance the system cost in term of money and others. There are many algorithms for fault tolerant in cloud computing and make comparison with fault tolerant based ant colony optimization and which is used to minimize fault during load balancing. In this paper, throughput is measured by such kind of fault tolerant based algorithms and determines that which algorithm is better. It has been compared with ACO. After such comparison, it is clearly determined that ACO has good functionalities to have better throughput. Comparative study is shown by the graphically and finally described that ACO is better than others in context of throughput calculation are. ACO is itself a meta-heuristic algorithm and better optimization technique.

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Katuri, Rayudu, Guduri Yesuratnam, and Askani Jayalaxmi. "BAT algorithm and Ant Colony Optimization based Optimal Reactive Power Dispatch to Improve Voltage Stability." European Journal of Engineering Research and Science 2, no. 6 (June 18, 2017): 27. http://dx.doi.org/10.24018/ejers.2017.2.6.378.

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One of the important tasks of a power system engineer is to run the system in safe and reliable mode for secure operation with increase in loading. So, it is significant to perform voltage stability analysis by optimal reactive power dispatch with Artificial Intelligence (AI) techniques. This paper presents the application of Ant Colony Optimization (ACO) and BAT algorithms for Optimal Reactive Power Dispatch (ORPD) to enhance voltage stability. The proposed ACO and BAT algorithms are used to find the optimal settings of On-load Tap changing Transformers (OLTC), Generator excitation and Static Var Compensators (SVC) to minimize the sum of the squares of the voltage stability L– indices of all the load buses. By calculating system parameters like L-Index, voltage error/deviation and real power loss for the practical Equivalent of Extra High Voltage (EHV) Southern Region Indian 24 bus system, voltage profile is improved and voltage stability is enhanced. A comparative analysis is done with the conventional optimization technique like Linear Programming (LP) for the given objective function to demonstrate the effectiveness of proposed ACO and BAT algorithms.

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Ticala, Cristina, Camelia-M. Pintea, and Oliviu Matei. "Sensitive Ant Algorithm for Edge Detection in Medical Images." Applied Sciences 11, no. 23 (November 29, 2021): 11303. http://dx.doi.org/10.3390/app112311303.

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Nowadays, reliable medical diagnostics from computed tomography (CT) and X-rays can be obtained by using a large number of image edge detection methods. One technique with a high potential to improve the edge detection of images is ant colony optimization (ACO). In order to increase both the quality and the stability of image edge detection, a vector called pheromone sensitivity level, PSL, was used within ACO. Each ant in the algorithm has one assigned element from PSL, representing the ant’s sensibility to the artificial pheromone. A matrix of artificial pheromone with the edge information of the image is built during the process. Demi-contractions in terms of the mathematical admissible perturbation are also used in order to obtain feasible results. In order to enhance the edge results, post-processing with the DeNoise convolutional neural network (DnCNN) was performed. When compared with Canny edge detection and similar techniques, the sensitive ACO model was found to obtain overall better results for the tested medical images; it outperformed the Canny edge detector by 37.76%.

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Katuri, Rayudu, Guduri Yesuratnam, and Askani Jayalaxmi. "BAT algorithm and Ant Colony Optimization based Optimal Reactive Power Dispatch to Improve Voltage Stability." European Journal of Engineering and Technology Research 2, no. 6 (June 18, 2017): 27–35. http://dx.doi.org/10.24018/ejeng.2017.2.6.378.

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One of the important tasks of a power system engineer is to run the system in safe and reliable mode for secure operation with increase in loading. So, it is significant to perform voltage stability analysis by optimal reactive power dispatch with Artificial Intelligence (AI) techniques. This paper presents the application of Ant Colony Optimization (ACO) and BAT algorithms for Optimal Reactive Power Dispatch (ORPD) to enhance voltage stability. The proposed ACO and BAT algorithms are used to find the optimal settings of On-load Tap changing Transformers (OLTC), Generator excitation and Static Var Compensators (SVC) to minimize the sum of the squares of the voltage stability L– indices of all the load buses. By calculating system parameters like L-Index, voltage error/deviation and real power loss for the practical Equivalent of Extra High Voltage (EHV) Southern Region Indian 24 bus system, voltage profile is improved and voltage stability is enhanced. A comparative analysis is done with the conventional optimization technique like Linear Programming (LP) for the given objective function to demonstrate the effectiveness of proposed ACO and BAT algorithms.

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Qamar, Muhammad Salman, Shanshan Tu, Farman Ali, Ammar Armghan, Muhammad Fahad Munir, Fayadh Alenezi, Fazal Muhammad, Asar Ali, and Norah Alnaim. "Improvement of Traveling Salesman Problem Solution Using Hybrid Algorithm Based on Best-Worst Ant System and Particle Swarm Optimization." Applied Sciences 11, no. 11 (May 23, 2021): 4780. http://dx.doi.org/10.3390/app11114780.

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This work presents a novel Best-Worst Ant System (BWAS) based algorithm to settle the Traveling Salesman Problem (TSP). The researchers has been involved in ordinary Ant Colony Optimization (ACO) technique for TSP due to its versatile and easily adaptable nature. However, additional potential improvement in the arrangement way decrease is yet possible in this approach. In this paper BWAS based incorporated arrangement as a high level type of ACO to upgrade the exhibition of the TSP arrangement is proposed. In addition, a novel approach, based on hybrid Particle Swarm Optimization (PSO) and ACO (BWAS) has also been introduced in this work. The presentation measurements of arrangement quality and assembly time have been utilized in this work and proposed algorithm is tried against various standard test sets to examine the upgrade in search capacity. The outcomes for TSP arrangement show that initial trail setup for the best particle can result in shortening the accumulated process of the optimization by a considerable amount. The exhibition of the mathematical test shows the viability of the proposed calculation over regular ACO and PSO-ACO based strategies.

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Inamdar, Sadanand, B. Sathish Babu, and Ravi Yadahalli. "Ant Based Adaptive Directional Monitoring MAC Protocol Using Smart Antennas in MANET." International Journal of Business Data Communications and Networking 14, no. 1 (January 2018): 46–66. http://dx.doi.org/10.4018/ijbdcn.2018010103.

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In Mobile Ad Hoc Network (MAC), the existing MAC protocol based on the short busy advertisement is not an efficient method for data fragmentation. In addition, the data-fragment length adjustment according to the network environments is still an issue. In order to overcome this issue, in this article, the authors propose to design an Adaptive Directional Monitoring MAC (ADM-MAC) protocol for smart Antennas using Ant colony optimization (ACO) technique. In this technique, the network density and traffic intensity information is estimated using Ant Colony Optimization (ACO) technique and then passed on to the MAC protocol. Then data fragment transmission is performed by adaptively adjusting the directional monitoring period based on the packet size. By simulation results, the authors show that the proposed technique reduces the delay, packet drop due to collision and increases the throughput and packet delivery ratio.

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Khan, Sahib. "Ant Colony Optimization (ACO) based Data Hiding in Image Complex Region." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 1 (February 1, 2018): 379. http://dx.doi.org/10.11591/ijece.v8i1.pp379-389.

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This paper presents data an Ant colony optimization (ACO) based data hiding technique. ACO is used to detect complex region of cover image and afterward, least significant bits (LSB) substitution is used to hide secret information in the detected complex regions’ pixels. ACO is an algorithm developed inspired by the inborn manners of ant species. The ant leaves pheromone on the ground for searching food and provisions. The proposed ACO-based data hiding in complex region establishes an array of pheromone, also called pheromone matrix, which represents the complex region in sequence at each pixel position of the cover image. The pheromone matrix is developed according to the movements of ants, determined by local differences of the image element’s intensity. The least significant bits of complex region pixels are substituted with message bits, to hide secret information. The experimental results, provided, show the significance of the performance of the proposed method.

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Journal articles on the topic 'ACO TECHNIQUE'

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