scholarly journals Aplikasi Hybrid Firefly Algorithm untuk Pemecahan Masalah Traveling Salesman: Studi Kasus pada PT Anugerah Mandiri Success

2014 ◽  
Vol 5 (2) ◽  
pp. 828
Author(s):  
J. Sudirwan ◽  
Siti Nur Fadlilah ◽  
Teguh Teguh
Keyword(s):  
Fuel Consumption ◽  
Firefly Algorithm ◽  
Nearest Neighbor ◽  
Heuristic Method ◽  
Optimization Method ◽  
Traveling Salesman ◽  
Total Distance ◽  
Metaheuristic Methods ◽  
Hybrid Firefly Algorithm

Determining a good route is a major problem in a transport process in the company. This case study aims to overcome the problem of determining the route or often called the Traveling Salesman Problem (TSP) is optimal. This problem will be solved by the optimization method in determining the route, which is more systematic in PT Anugerah Mandiri Success. Metaheuristic methods, such as Hybrid Firefly Algorithm is used to assist the system in the process of determining the route. Hybrid Firefly Algorithm combines heuristic method of Nearest Neighbor Heuristic with metaheuristic method of Levy Flight Discrete Firefly Algorithm. The indicators used in this case study are the total distance traveled and total fuel used. Methods Object Oriented Analysis andDesign (OOAD) is used to develop an information system, which consists of determining system requirements, system architecture design, and design to the trials of the system were developed. The results of the constructedsystem provide a solution in the form of route determination with a total distance of 165.1 kilometers with a fuel consumption of 11,793 liters. These results are much better when compared with historical data that has a totaldistance of 260.8 kilometers with a fuel consumption of 18,628 liters.

scholarly journals Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Eduardo Batista de Moraes Barbosa ◽  
Edson Luiz França Senne
Keyword(s):  
Heuristic Method ◽  
Search Space ◽  
Computational Effort ◽  
Traveling Salesman ◽  
The Other ◽  
Fine Tuning ◽  
Large Set ◽  
Promising Alternative ◽  
Tuning Process

Usually, metaheuristic algorithms are adapted to a large set of problems by applying few modifications on parameters for each specific case. However, this flexibility demands a huge effort to correctly tune such parameters. Therefore, the tuning of metaheuristics arises as one of the most important challenges in the context of research of these algorithms. Thus, this paper aims to present a methodology combining Statistical and Artificial Intelligence methods in the fine-tuning of metaheuristics. The key idea is a heuristic method, called Heuristic Oriented Racing Algorithm (HORA), which explores a search space of parameters looking for candidate configurations close to a promising alternative. To confirm the validity of this approach, we present a case study for fine-tuning two distinct metaheuristics: Simulated Annealing (SA) and Genetic Algorithm (GA), in order to solve the classical traveling salesman problem. The results are compared considering the same metaheuristics tuned through a racing method. Broadly, the proposed approach proved to be effective in terms of the overall time of the tuning process. Our results reveal that metaheuristics tuned by means of HORA achieve, with much less computational effort, similar results compared to the case when they are tuned by the other fine-tuning approach.

A Hybrid Meta-Heuristic Method Based on Firefly Algorithm and Krill Herd

2016 ◽  
pp. 505-524 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir H. Gandomi ◽  
Amir H. Alavi ◽  
Yong-Quan Dong
Keyword(s):  
Light Intensity ◽  
Local Search ◽  
Firefly Algorithm ◽  
Optimization Algorithms ◽  
Heuristic Method ◽  
Optimization Method ◽  
Premature Convergence ◽  
Search Technique ◽  
Krill Herd

This study proposes a new firefly-inspired krill herd (FKH) optimization method based on integration of firefly and krill herd algorithms. FKH introduces an attractiveness and light intensity updating (ALIU) operator originally used in firefly algorithm into the krill herd method. This is basically done to improve local search technique and promote the diversity of the population to avoid a premature convergence. Moreover, an elitism strategy is adopted to maintain the optimal krill with the best fitness when updating the krill. The performance of the FKH method is verified using fifteen different benchmark functions. The results indicate that FKH performs more accurate and effective than the basic krill herd and other optimization algorithms.

scholarly journals A Hybrid Firefly Algorithm – Ant Colony Optimization for Traveling Salesman Problem

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Olief Ilmandira Ratu Farisi ◽  
Budi Setiyono ◽  
R. Imbang Danandjojo
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Hybrid Method ◽  
Firefly Algorithm ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Convergence Time ◽  
Local Optimum ◽  
Great Ability ◽  
Hybrid Firefly Algorithm

Abstrak. Pada penelitian ini dikembangkan suatu metode baru yaitu hybrid firefly algorithm-ant colony optimization (hybrid FA-ACO) untuk menyelesaikan masalah traveling salesman problem (TSP). ACO memiliki komputasi terdistribusi sehingga dapat mencegah konvergensi dini dan FA memiliki kemampuan konvergensi yang cepat dalam pencarian solusi. Untuk memperbaiki solusi dan mempercepat waktu konvergensi, digunakan metode kombinasi. Pendekatan kombinasi ini meliputi pencarian solusi dengan FA dan pencarian global dengan ACO. Solusi lokal dari FA dinormalisasi dan digunakan untuk menginisialisasi feromon untuk pencarian global ACO. Hasil dari hybrid FA-ACO dibandingkan dengan FA dan ACO. Hasil penelitian menunjukkan bahwa metode yang diusulkan dapat menemukan solusi yang lebih baik tanpa terjebak lokal optimum dengan waktu komputasi lebih pendek.Kata kunci: Traveling Salesman Problem, Firefly Algorithm, Ant Colony Optimization, metode hybrid. Abstract. In this paper, we develop a novel method hybrid firefly algorithm-ant colony optimization for solving traveling salesman problem. The ACO has distributed computation to avoid premature convergence and the FA has a very great ability to search solutions with a fast speed to converge. To improve the result and convergence time, we used hybrid method. The hybrid approach involves local search by the FA and global search by the ACO. Local solution of FA is normalized and is used to initialize the pheromone for the global solution search using the ACO. The outcome are compared with FA and ACO itself. The experiment showed that the proposed method can find the solution much better without trapped into local optimum with shorter computation time.Keywords: Traveling Salesman Problem, Firefly Algorithm, Ant Colony Optimization, hybrid method.

Traffic Flow Management Impact on Delay and Fuel Consumption: an Atlanta Case Study

2012 ◽  
Vol 20 (3) ◽  
pp. 203-224 ◽  
Author(s):  
Shon R. Grabbe ◽  
Banavar Sridhar ◽  
Avijit Mukherjee ◽  
Alexander Morando
Keyword(s):  
Fuel Consumption ◽  
Traffic Flow ◽  
Flow Management ◽  
Traffic Flow Management

scholarly journals Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1377
Author(s):  
Musaab I. Magzoub ◽  
Raj Kiran ◽  
Saeed Salehi ◽  
Ibnelwaleed A. Hussein ◽  
Mustafa S. Nasser
Keyword(s):  
Machine Learning ◽  
Rheological Properties ◽  
Nearest Neighbor ◽  
Drilling Fluid ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Machine Learning Approach ◽  
Drilling Operations

The traditional way to mitigate loss circulation in drilling operations is to use preventative and curative materials. However, it is difficult to quantify the amount of materials from every possible combination to produce customized rheological properties. In this study, machine learning (ML) is used to develop a framework to identify material composition for loss circulation applications based on the desired rheological characteristics. The relation between the rheological properties and the mud components for polyacrylamide/polyethyleneimine (PAM/PEI)-based mud is assessed experimentally. Four different ML algorithms were implemented to model the rheological data for various mud components at different concentrations and testing conditions. These four algorithms include (a) k-Nearest Neighbor, (b) Random Forest, (c) Gradient Boosting, and (d) AdaBoosting. The Gradient Boosting model showed the highest accuracy (91 and 74% for plastic and apparent viscosity, respectively), which can be further used for hydraulic calculations. Overall, the experimental study presented in this paper, together with the proposed ML-based framework, adds valuable information to the design of PAM/PEI-based mud. The ML models allowed a wide range of rheology assessments for various drilling fluid formulations with a mean accuracy of up to 91%. The case study has shown that with the appropriate combination of materials, reasonable rheological properties could be achieved to prevent loss circulation by managing the equivalent circulating density (ECD).

The WLTC vs NEDC: A Case Study on the Impacts of Driving Cycle on Engine Performance and Fuel Consumption

2021 ◽  
Vol 18 (3) ◽  
pp. 9071-9081
Author(s):  
Jakub Lasocki
Keyword(s):  
Fuel Consumption ◽  
Combustion Engine ◽  
Engine Performance ◽  
Driving Cycle ◽  
Performance Characteristics ◽  
Pollutant Emission ◽  
Strong Impact ◽  
Light Duty ◽  
Light Duty Vehicles

The World-wide harmonised Light-duty Test Cycle (WLTC) was developed internationally for the determination of pollutant emission and fuel consumption from combustion engines of light-duty vehicles. It replaced the New European Driving Cycle (NEDC) used in the European Union (EU) for type-approval testing purposes. This paper presents an extensive comparison of the WLTC and NEDC. The main specifications of both driving cycles are provided, and their advantages and limitations are analysed. The WLTC, compared to the NEDC, is more dynamic, covers a broader spectrum of engine working states and is more realistic in simulating typical real-world driving conditions. The expected impact of the WLTC on vehicle engine performance characteristics is discussed. It is further illustrated by a case study on two light-duty vehicles tested in the WLTC and NEDC. Findings from the investigation demonstrated that the driving cycle has a strong impact on the performance characteristics of the vehicle combustion engine. For the vehicles tested, the average engine speed, engine torque and fuel flow rate measured over the WLTC are higher than those measured over the NEDC. The opposite trend is observed in terms of fuel economy (expressed in l/100 km); the first vehicle achieved a 9% reduction, while the second – a 3% increase when switching from NEDC to WLTC. Several factors potentially contributing to this discrepancy have been pointed out. The implementation of the WLTC in the EU will force vehicle manufacturers to optimise engine control strategy according to the operating range of the new driving cycle.

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