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Dr. Lagouge Tartibu is an Associate Professor in the Department of Mechanical and Industrial Engineering Technology at the University of Johannesburg in South Africa. He holds a Doctorate in Mechanical Engineering from the Cape Peninsula University of Technology and a Bachelor's in Electromechanical Engineering from the University of Lubumbashi. His primary research areas are thermal science, electricity generation and refrigeration using thermo-acoustic technology, engineering optimization, and mechanical vibration.
Thermoacoustic refrigerators are emerging devices that make use of meaningful high-pressure sound waves to induce cooling. Despite the accelerated progress in the field of thermoacoustics, knowledge of the heat transfer process in the heat exchange of the devices is still developing. This work applies different soft computing techniques, namely, an artificial neural network trained by particle swarm optimisation (ANN-PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANNs) to predict the oscillatory heat transfer coefficient in the heat exchangers of a thermoacoustic device. This study provides the details of the parametric analysis of an artificial neural network model trained by particle swarm optimisation. The solution model considers the number of neurons, the swarm population, and the acceleration factors to develop and analyse the architecture of several models. The regression model (R2) and mean squared error (MSE) were used to evaluate the accuracy of the models. The result showed that the proposed soft computing techniques can potentially be used for the modelling and the analysis of the oscillatory heat transfer coefficient with a higher level of accuracy. The result reported in this study implies that the prediction of the OHTC can be considered for the enhancement of thermoacoustic refrigerators performances.
Mosa Machesa; Lagouge Tartibu; Modestus Okwu. Prediction of the Oscillatory Heat Transfer Coefficient in Thermoacoustic Refrigerators. Sustainability 2021, 13, 9509 .
AMA StyleMosa Machesa, Lagouge Tartibu, Modestus Okwu. Prediction of the Oscillatory Heat Transfer Coefficient in Thermoacoustic Refrigerators. Sustainability. 2021; 13 (17):9509.
Chicago/Turabian StyleMosa Machesa; Lagouge Tartibu; Modestus Okwu. 2021. "Prediction of the Oscillatory Heat Transfer Coefficient in Thermoacoustic Refrigerators." Sustainability 13, no. 17: 9509.
The implementation of nano-additives in machining fluid is significant for manufacturers to attain a sustainable manufacturing process. The material removal rate (MRR) is a significant process of transforming solid raw materials into specific shapes and sizes. This process has many challenges due to friction, vibration, chip discontinuity when machining aluminum alloy, which has led to poor accuracy and affected the fatigue life of the developed material. It is worth noting that aluminum 8112 alloy is currently being applied in most engineering applications due to its lightweight-to-strength ratio compared to some other metals. This research aims to compare the effects of copra oil-based-titanium dioxide (TiO2)- and Multi-walled Carbon Nanotubes (MWCNTs)-nano-lubricant with cutting parameter interactions by conducting a study on MRR for advanced machining of aluminum 8112 alloys. The biodegradable nano-additive-lubricants were developed using two-step preparation techniques. The study employed a quadratic rotatable central composite design (QRCCD) to carry out the interaction study of the five machining parameters in the three lubrication environments on MRR. The results show that the copra-based-TiO2 nano-lubricant increases the MRR by 7.5% and 16% than the MWCNTs and copra-oil-lubrication machining environments, respectively. In conclusion, the eco-friendly nano-additive-lubricant TiO2-Copra oil-based should be applied to manufacture machine parts for high entropy applications for sustainable production systems.
Imhade Okokpujie; Lagouge Tartibu. Performance Investigation of the Effects of Nano-Additive-Lubricants with Cutting Parameters on Material Removal Rate of AL8112 Alloy for Advanced Manufacturing Application. Sustainability 2021, 13, 8406 .
AMA StyleImhade Okokpujie, Lagouge Tartibu. Performance Investigation of the Effects of Nano-Additive-Lubricants with Cutting Parameters on Material Removal Rate of AL8112 Alloy for Advanced Manufacturing Application. Sustainability. 2021; 13 (15):8406.
Chicago/Turabian StyleImhade Okokpujie; Lagouge Tartibu. 2021. "Performance Investigation of the Effects of Nano-Additive-Lubricants with Cutting Parameters on Material Removal Rate of AL8112 Alloy for Advanced Manufacturing Application." Sustainability 13, no. 15: 8406.
Lagouge Tartibu; Miniyenkosi Ngcukayitobi; Samuel Gqibani. Design and Construction of a Four-Stage Travelling-Wave Thermo-Acoustic System for Power Generation. 2021, 1 .
AMA StyleLagouge Tartibu, Miniyenkosi Ngcukayitobi, Samuel Gqibani. Design and Construction of a Four-Stage Travelling-Wave Thermo-Acoustic System for Power Generation. . 2021; ():1.
Chicago/Turabian StyleLagouge Tartibu; Miniyenkosi Ngcukayitobi; Samuel Gqibani. 2021. "Design and Construction of a Four-Stage Travelling-Wave Thermo-Acoustic System for Power Generation." , no. : 1.
Lagouge Tartibu; Rolly Karodolan Ndeko Kabinga; Modestus Okechukwu Okwu. Development and Performance Evaluation of a Dynamic Compressed Air Energy Storage System. 2021, 1 .
AMA StyleLagouge Tartibu, Rolly Karodolan Ndeko Kabinga, Modestus Okechukwu Okwu. Development and Performance Evaluation of a Dynamic Compressed Air Energy Storage System. . 2021; ():1.
Chicago/Turabian StyleLagouge Tartibu; Rolly Karodolan Ndeko Kabinga; Modestus Okechukwu Okwu. 2021. "Development and Performance Evaluation of a Dynamic Compressed Air Energy Storage System." , no. : 1.
This paper describes the development of a modular unmanned aerial vehicle for the detection and eradication of weeds on farmland. Precision agriculture entails solving the problem of poor agricultural yield due to competition for nutrients by weeds and provides a faster approach to eliminating the problematic weeds using emerging technologies. This research has addressed the aforementioned problem. A quadcopter was built, and components were assembled with light-weight materials. The system consists of the electric motor, electronic speed controller, propellers, frame, lithium polymer (li-po) battery, flight controller, a global positioning system (GPS), and receiver. A sprayer module which consists of a relay, Raspberry Pi 3, spray pump, 12 V DC source, water hose, and the tank was built. It operated in such a way that when a weed is detected based on the deep learning algorithms deployed on the Raspberry Pi, general purpose input/output (GPIO) 17 or GPIO 18 (of the Raspberry Pi) were activated to supply 3.3 V, which turned on a DC relay to spray herbicides accordingly. The sprayer module was mounted on the quadcopter and from the test-running operation conducted, broadleaf and grass weeds were accurately detected and the spraying of herbicides according to the weed type occurred in less than a second.
Uchechi Ukaegbu; Lagouge Tartibu; Modestus Okwu; Isaac Olayode. Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture. Sensors 2021, 21, 4417 .
AMA StyleUchechi Ukaegbu, Lagouge Tartibu, Modestus Okwu, Isaac Olayode. Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture. Sensors. 2021; 21 (13):4417.
Chicago/Turabian StyleUchechi Ukaegbu; Lagouge Tartibu; Modestus Okwu; Isaac Olayode. 2021. "Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture." Sensors 21, no. 13: 4417.
This research output is focused on the modified design of a low-cost biodigester using a 295L of steel drum. The system has a 15kg bio-methane storage tank of 182L with a water displacement tank of 20L standard steel drum. Incorporated into the design is a cast iron with centrally positioned four impeller shaft to enhance mixing of substrate. A floating storage tank was designed, fabricated, and used for effective storage of bio-methane; biogas was produced from mixture of cowdungs, which was filtered to remove substituent of biogas. The retention time was for 4 weeks during which the pH valve and temperature variation were noted. Maximum biogas production was attained on the 8th, 9th, and 19th day, and the total volume of gas produced ranges between 92.6 and 102 mL/day. The test result showed that methane gas produced the highest percentage of biogas. Additionally, from the result, the displacement of ethane, ethylene, propane, propylene, I-butane, nitrogen, carbon monoxide, carbondioxide and other gases were minimal compared to methane gas.
Omonigho B. Otanocha; Raymond Oyovwikefe; Modestus O. Okwu; Lagouge K. Tartibu. Modified biogas digester tank for production of gas from decomposable organic wastes. Biomass Conversion and Biorefinery 2021, 1 -11.
AMA StyleOmonigho B. Otanocha, Raymond Oyovwikefe, Modestus O. Okwu, Lagouge K. Tartibu. Modified biogas digester tank for production of gas from decomposable organic wastes. Biomass Conversion and Biorefinery. 2021; ():1-11.
Chicago/Turabian StyleOmonigho B. Otanocha; Raymond Oyovwikefe; Modestus O. Okwu; Lagouge K. Tartibu. 2021. "Modified biogas digester tank for production of gas from decomposable organic wastes." Biomass Conversion and Biorefinery , no. : 1-11.
This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.
Modestus O. Okwu; Lagouge K. Tartibu. Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications. Econometrics for Financial Applications 2021, 1 .
AMA StyleModestus O. Okwu, Lagouge K. Tartibu. Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications. Econometrics for Financial Applications. 2021; ():1.
Chicago/Turabian StyleModestus O. Okwu; Lagouge K. Tartibu. 2021. "Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications." Econometrics for Financial Applications , no. : 1.
The International Engineering Alliance has developed graduate attributes to improve the employability of engineering graduates and to reduce the gap between academic work and practice. The three main accords that form part of this international alliance are the Washington Accord, the Sydney Accord, and the Dublin Accord. All countries that belong to these accords have developed graduate attributes for accreditation purposes. Embedding graduate attributes into the curriculum can be complex and would depend on academic staff involvement. Hence, the necessity to determine how best graduate attributes can be developed and give clarity about assessment strategies. Engineering faculties need to consider developing clear processes that clarify the programme outcomes assessment in the context of graduate attributes. This paper provides an overview of some practical approaches that could be used for the development of graduate attribute assessments.
Rita Steenkamp; Lagouge Tartibu. Practical approaches to implement graduate attributes in engineering faculties. 2020 IFEES World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC) 2020, 1 -5.
AMA StyleRita Steenkamp, Lagouge Tartibu. Practical approaches to implement graduate attributes in engineering faculties. 2020 IFEES World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC). 2020; ():1-5.
Chicago/Turabian StyleRita Steenkamp; Lagouge Tartibu. 2020. "Practical approaches to implement graduate attributes in engineering faculties." 2020 IFEES World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC) , no. : 1-5.
This work describes the design and construction of a four-stage traveling-wave thermo-acoustic system for electricity generation. The thermo-acoustic conversion consists of using a sound-wave for the transfer of heat from a low to high-temperature medium or the use of heat energy to generate a sound wave. Both the absence of moving parts and the simplicity of thermo-acoustic systems make the technology sustainable for converting low-grade waste heat into acoustic power. Many existing studies have pointed out the acoustic-to-electric potential of thermo-acoustic systems. Hence in this work, a thermo-acoustic system has been developed. The traveling-wave system has a total length of 3 560 mm. The distance between each thermo-acoustic engine is 640 mm. Each engine stage had four cartridge heaters used to generate the heat required. A commercial loudspeaker was used to convert sound into electricity. The minimum temperature difference necessary to induce a voltage at the terminals of the loudspeaker was approximately 200°C. The four-stage traveling-wave system generated the highest output voltage of 4.218 V.
Miniyenkosi Ngcukayitobi; Lagouge Tartibu; Samuel Gqibani. Design and Construction of a Four-Stage Travelling-Wave Thermo-Acoustic System for Power Generation. Volume 6: Design, Systems, and Complexity 2020, 1 .
AMA StyleMiniyenkosi Ngcukayitobi, Lagouge Tartibu, Samuel Gqibani. Design and Construction of a Four-Stage Travelling-Wave Thermo-Acoustic System for Power Generation. Volume 6: Design, Systems, and Complexity. 2020; ():1.
Chicago/Turabian StyleMiniyenkosi Ngcukayitobi; Lagouge Tartibu; Samuel Gqibani. 2020. "Design and Construction of a Four-Stage Travelling-Wave Thermo-Acoustic System for Power Generation." Volume 6: Design, Systems, and Complexity , no. : 1.
Engineering design and build projects aims at improving student ability to think and solve problem while enhancing soft skills like communication skills and teamwork. Assessing this outcome could be relatively complicated because in some instances students are being exposed to advanced engineering courses that will allow them to perform complex calculations while completing their project. It is therefore necessary to instill into students the motivation to study independently and integrate engineering concepts into practice. Solving the design problem becomes the main motivation behind self-learning that frame the context for advanced engineering courses. This happens as a result of the Problem-Based Learning approach which pushes students to acquire engineering knowledge they don’t possess. This approach could raise several problem when assessing quantitatively student outcome achievement and the overall effectiveness of the course. This paper focuses on students’ attitudes rather than skills. Design thinking affinities such as team work, problem solving and communication are assessed as part on an ongoing improvement plan. A survey has been developed to measure the course effectiveness. The paper aims at measuring student attitude toward engineering design. Though such instrument has been used in existing engineering education literature, the diversity of students’ attitudes justify this study. The study reveals that problem-solving perception constitute the dimension that require interventions. External motivation are proposed in order to create good attitude.
Lagouge Tartibu; Rita Steenkamp. An assessment of the students’ attitudes toward engineering design and build projects. 2020 IFEES World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC) 2020, 1 -5.
AMA StyleLagouge Tartibu, Rita Steenkamp. An assessment of the students’ attitudes toward engineering design and build projects. 2020 IFEES World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC). 2020; ():1-5.
Chicago/Turabian StyleLagouge Tartibu; Rita Steenkamp. 2020. "An assessment of the students’ attitudes toward engineering design and build projects." 2020 IFEES World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC) , no. : 1-5.
This research focused on introduction to the Artificial Bee Colony (ABC) algorithm, the foraging behavior and waggle dance of honeybees while passing information about a given food source to the rest of the bee colony. The mathematical modelling and real-life application of the bee algorithm in fast moving grocery retail outlet was presented, the result obtained from the big box store or retail facility after analysis showed that the ABC algorithm is highly effective in system modeling and prediction.
Modestus O. Okwu; Lagouge K. Tartibu. Artificial Bee Colony Algorithm. Econometrics for Financial Applications 2020, 15 -31.
AMA StyleModestus O. Okwu, Lagouge K. Tartibu. Artificial Bee Colony Algorithm. Econometrics for Financial Applications. 2020; ():15-31.
Chicago/Turabian StyleModestus O. Okwu; Lagouge K. Tartibu. 2020. "Artificial Bee Colony Algorithm." Econometrics for Financial Applications , no. : 15-31.
Firefly Algorithm (FA) is a meta-heuristic algorithm which is categorized as one of the fast-growing swarm intelligence algorithms. Based on the flashing pattern of light and their intelligent behaviour, FA can solve problem in all fields of optimization and is considered very useful for solving complex mathematical and engineering problems. This chapter describes converging nature of the firefly, rules for developing the firefly algorithm and FA models. Illustration of the implementation of the FA for a typical optimization problem considering a numerical equation, with twenty (20) fireflies showed best results fbest = 8.9936e-13 corresponding to nbest = [1.0000 2.0000]. This algorithm is useful for solving routing and clustering problems and generic optimization of process variables.
Modestus O. Okwu; Lagouge K. Tartibu. Firefly Algorithm. Econometrics for Financial Applications 2020, 61 -69.
AMA StyleModestus O. Okwu, Lagouge K. Tartibu. Firefly Algorithm. Econometrics for Financial Applications. 2020; ():61-69.
Chicago/Turabian StyleModestus O. Okwu; Lagouge K. Tartibu. 2020. "Firefly Algorithm." Econometrics for Financial Applications , no. : 61-69.
GWO algorithm is a swarm or population-based meta-heuristic technique developed based on motivation from the hunting pattern of the Grey Wolves (GW). In this study, the model was implemented using MATLAB 2020. Thirty (30) search agents were considered and the maximum number of iterations was set to 1000. The best solution, best optimal value and objective function are presented in the study. GWO algorithm is considered useful for solving complex optimization problem.
Modestus O. Okwu; Lagouge K. Tartibu. Grey Wolf Optimizer. Econometrics for Financial Applications 2020, 43 -52.
AMA StyleModestus O. Okwu, Lagouge K. Tartibu. Grey Wolf Optimizer. Econometrics for Financial Applications. 2020; ():43-52.
Chicago/Turabian StyleModestus O. Okwu; Lagouge K. Tartibu. 2020. "Grey Wolf Optimizer." Econometrics for Financial Applications , no. : 43-52.
Butterfly Optimization Algorithm (BOA) is a unique algorithm inspired by nature. The algorithm replicates the behavior of the natural butterfly which can be described to perform a cooperative movement while navigating towards its food source and position. The butterflies are well known for having the power to analyze and receive smell in the air thereby tracing and discovering the direction of their partner and food source. BOA model was implemented in MATLAB to illustrate the approach, 30 search agents were considered (n = 30) and the maximum number of iterations was set to 1000. The best solution obtained by BOA is [3.0175 1.9708] and the best optimal value of the objective function found by BOA is 0.015431. The parameter space and the convergence test are presented. BOA is considered very useful for solving complex optimization problems.
Modestus O. Okwu; Lagouge K. Tartibu. Butterfly Optimization Algorithm. Econometrics for Financial Applications 2020, 105 -114.
AMA StyleModestus O. Okwu, Lagouge K. Tartibu. Butterfly Optimization Algorithm. Econometrics for Financial Applications. 2020; ():105-114.
Chicago/Turabian StyleModestus O. Okwu; Lagouge K. Tartibu. 2020. "Butterfly Optimization Algorithm." Econometrics for Financial Applications , no. : 105-114.
Grasshopper Optimization algorithm (GOA) is one of the newly introduced algorithms. The swarming ability of the grasshopper makes them unique herbivorous insects. A detailed description and step by step mathematical model of the GOA is presented in this study. GOA model for solving a real-life problem is revealed and numerical example are used to illustrate the approach. The model was implemented in MATLAB using 100 search agents (n = 100) and the maximum number of iterations was set to 1000. The best solution obtained by GOA is [101] and the best optimal value of the objective function found by GOA is 0.2361. The parameter space, the test history, the trajectory of the first grasshopper, and the convergence test are also presented in this study.
Modestus O. Okwu; Lagouge K. Tartibu. Grasshopper Optimisation Algorithm (GOA). Econometrics for Financial Applications 2020, 95 -104.
AMA StyleModestus O. Okwu, Lagouge K. Tartibu. Grasshopper Optimisation Algorithm (GOA). Econometrics for Financial Applications. 2020; ():95-104.
Chicago/Turabian StyleModestus O. Okwu; Lagouge K. Tartibu. 2020. "Grasshopper Optimisation Algorithm (GOA)." Econometrics for Financial Applications , no. : 95-104.
Bat algorithm (BA) is an innovative population-based technique which belongs to the swarm intelligence group. This meta-heuristic algorithm provides a suitable solution technique than numerous and prevalent classical and heuristic techniques. This chapter is an exposition of the communication and navigational pattern of bats and micro-bats echolocation (EL), algorithm development and solved numerical problem. The illustration and implementation of the BAT algorithm for a typical optimization problem considering a numerical equation, using MATLAB code has been demonstrated. The model considers 10 bats and the maximum number of iterations of 1000. The best solution obtained by BA is [1.3138 1.8528 0.261 0.83905 0.34859 1.1436 0.73859 1.7623 0.16537 0.28717]. The best optimal value of the objective function found by BAT is: 0.23604. BA is considered useful in engineering, business, transportation, and other fields of human endeavour.
Modestus O. Okwu; Lagouge K. Tartibu. Bat Algorithm. Econometrics for Financial Applications 2020, 71 -84.
AMA StyleModestus O. Okwu, Lagouge K. Tartibu. Bat Algorithm. Econometrics for Financial Applications. 2020; ():71-84.
Chicago/Turabian StyleModestus O. Okwu; Lagouge K. Tartibu. 2020. "Bat Algorithm." Econometrics for Financial Applications , no. : 71-84.
Research on meta-heuristics, other high level computational intelligence techniques and evolutionary computing, are the focus of researchers in the current industrial revolution. Nature-inspired algorithms have been discussed intensively in the previous chapters. They are very useful algorithms for solving complex problems. Traditional algorithms may not offer good solutions to such complex problems. Non-linear and stochastic problems require sophisticated algorithms especially to support the technologies of industry 4.0. This present industrial revolution which represent the successive trend of digital technologies can be described as a blurred streak between idea and reality, biological and digital connections where meta-heuristic techniques will be very useful in real-life problem solving. Other interesting meta-heuristic and hybrid algorithms for the future are detailed in this chapter.
Modestus O. Okwu; Lagouge K. Tartibu. Future of Nature Inspired Algorithm, Swarm and Computational Intelligence. Econometrics for Financial Applications 2020, 147 -151.
AMA StyleModestus O. Okwu, Lagouge K. Tartibu. Future of Nature Inspired Algorithm, Swarm and Computational Intelligence. Econometrics for Financial Applications. 2020; ():147-151.
Chicago/Turabian StyleModestus O. Okwu; Lagouge K. Tartibu. 2020. "Future of Nature Inspired Algorithm, Swarm and Computational Intelligence." Econometrics for Financial Applications , no. : 147-151.
Metaheuristics are global optimization techniques for solving real life problems. The classical techniques for solving such realistic day to day problems is time consuming and will not proffer an exact solution. Metaheuristic techniques are capable of using search experience intelligently to explore and exploit the search space in a randomized manner providing robust and good solutions. This book provide guide on how to solve real life complex stochastic problems in our everyday life using global optimization techniques.
Modestus O. Okwu; Lagouge K. Tartibu. Introduction to Optimization. Econometrics for Financial Applications 2020, 1 -4.
AMA StyleModestus O. Okwu, Lagouge K. Tartibu. Introduction to Optimization. Econometrics for Financial Applications. 2020; ():1-4.
Chicago/Turabian StyleModestus O. Okwu; Lagouge K. Tartibu. 2020. "Introduction to Optimization." Econometrics for Financial Applications , no. : 1-4.
Genetic algorithm (GA) is an optimization algorithm that is often categorized as a global search heuristic technique. Being a branch of evolutionary computation, it is known to mimic the natural selection of biological processes of reproduction and to solve the ‘fittest’ solutions. In this chapter, the knapsack problem was solved using GA technique to determine the strength or capacity of bag used in convey items. The solution of the model shows that no combination of any form would give an exact weight or capacity the bag can carry except set spaces 15 and 29, where the weight of items are 34 kg and 36 kg respectively. Hence the feasible weight of item to be stored in the bag is 34 kg at a value of 16. Any weight of material above 36 kg will lead to the ripping of the bag.
Modestus O. Okwu; Lagouge K. Tartibu. Genetic Algorithm. Econometrics for Financial Applications 2020, 125 -132.
AMA StyleModestus O. Okwu, Lagouge K. Tartibu. Genetic Algorithm. Econometrics for Financial Applications. 2020; ():125-132.
Chicago/Turabian StyleModestus O. Okwu; Lagouge K. Tartibu. 2020. "Genetic Algorithm." Econometrics for Financial Applications , no. : 125-132.
Ant colony optimization (ACO) algorithm, models the real-life behavior of ants to solve optimization problems. Information provided in this chapter include: path navigation of ants, foraging pattern of ants by depositing pheromones during exploration and ACO model development. ACO algorithm has been applied to routing in telecommunication networks using TSP. The best route was [1 2 5 3 4 1], corresponding to a minimum cost of 52.
Modestus O. Okwu; Lagouge K. Tartibu. Ant Colony Algorithm. Econometrics for Financial Applications 2020, 33 -41.
AMA StyleModestus O. Okwu, Lagouge K. Tartibu. Ant Colony Algorithm. Econometrics for Financial Applications. 2020; ():33-41.
Chicago/Turabian StyleModestus O. Okwu; Lagouge K. Tartibu. 2020. "Ant Colony Algorithm." Econometrics for Financial Applications , no. : 33-41.